This was the big idea behind a book that was widely called as ‘The Bible of Investing’.

Written by two professors at Columbia University, Benjamin Graham and David Dodd, in 1934, called Security Analysis that shaped how generations of investors think about the stock market.

Graham believed that in the short run, the stock market acts like a voting machine, and in the long run, like a weighing machine.

Sounds familiar?

You might have heard it from one of Graham’s students, Warren Buffett, who studied under him at Columbia in 1950.

One of the world’s most successful investors, Buffett built his fortune by patiently buying undervalued businesses.

He popularised Graham’s idea by quoting it in a letter to Berkshire Hathaway shareholders.

But what did Graham mean when he compared markets to a voting machine and a weighing machine?

Simply put, a voting machine measures opinions.

In the stock market, investors react to headlines, rumours, fear, excitement and what everyone else seems to be doing. A stock can rise or fall simply because the mood changed.

In the long run, however, Graham believed markets measure what something is actually worth, like a weighing machine.

A weighing machine doesn’t care about opinions. For a company, the weight is what it earns, year after year.

The voting can go on for a long time. But Graham believed the weighing machine eventually has the final say.

Quarterly results are one of the most closely watched events in the market. Most investors assume that better earnings will push a stock higher.

But is that really true? Do strong quarterly results really move a stock? And if they don’t in the short run, does the weighing machine catch up later?

We decided to test this.

The Study

We analysed the quarterly results for the current constituents of the Nifty 50 from the June quarter in 2016 to March 2026 quarter.

This gave us the results of 50 companies across 40 quarters, which is 2,000 quarterly results overall.

In each quarter, we measured the year-on-year profit growth. Now, a 20% profit jump during a time when the economy is booming is normal. A 20% jump during a crisis like Covid is exceptional.

So we ranked the companies against each other in each quarter and put them into three baskets.

Strong: the top 1/3rd by profit growth that quarter or the highest profit growth

Middle: the middle third

Weak: the bottom third or the weakest profit growth

We compared each stock’s return with the Nifty 50’s return during the results season, to see whether the market rewarded stronger results.

The season starts on the last trading day of the quarter and ends on the first trading day after SEBI’s deadline for declaring results: 45 days after the quarter ends, or 60 days for the March quarter.

By then, every company’s numbers are public. We used the longer deadlines SEBI allowed during COVID in 2020 and 2021.

Some quarterly results had to be left out: where we did not have data for that quarter, where the company was not yet listed, or where a merger or demerger made the year-on-year comparison meaningless.

We also left out quarters where the company had zero or negative profit a year earlier, since percentage growth cannot be calculated from that base.

We were then left with 1,525 quarterly results to study if strong profits actually lead to strong stock returns.

Study 1

The companies in the strong basket beat Nifty 50, 54% of the time. The median profit growth of this group was 62%. And the weak basket also managed to beat Nifty 50 returns 43% of the time.

So stronger results did lead to better returns.

But the advantage is much smaller than most investors expect. Buying a stock simply because it announced strong earnings does not guarantee it will beat the market.

Despite the 11 percentage point gap, even the best companies lost to the index almost half the time.

On average, a Strong stock beat the Nifty by 3.0% over the season, while a Weak stock trailed it by 0.2%.

The typical (median) Strong stock beat the Nifty by only about 0.9%, while the typical Weak stock trailed it by 1.4%.

Stocks in the Strong basket still trailed the Nifty 50 in about 46% of the 494 strong quarters.

This holds true for weak quarters as well.

In 43% of the weak quarters, the stock beat the market anyway. In 75 of those cases, it beat the market by more than 10 points.

Hindalco was one of the extreme cases where profits grew massively.

In the March 2022 quarter (Q4 FY22), Hindalco’s profit grew 98.4% from a year earlier. A massive profit like that should be celebrated by investors right? But that is not what happened.

Yet over that results season, from 31 March to 31 May 2022, the stock fell almost 26%. Nifty 50 also fell 5% during this period.

But why?

Two things weighed on the stock. Its US subsidiary Novelis reported a 15% year-on-year fall in adjusted EBITDA for the quarter, citing higher operating costs.

And global aluminium prices were falling: Hindalco cut its domestic prices several times from late April 2022 as prices on the London Metal Exchange slid.

Any, or all of these could have led to the fall in the stock despite a spectacular result on paper.

Tata Steel represents the other end of the extreme.

The company’s profits fell sharply by 60% in Q2FY21 compared to the same period a year ago. With profits down by more than half, one would expect the stock to fall sharply as well.

In reality, the shares jumped 45% over the results season and beat the Nifty 50 by almost 31 percentage points.

This was because the damage was already done. Most investors expected bad earnings due to the impact of Covid. But they were seeing signs of recovery.

The table above shows a few more cases like these, from Trent to SBI.

The market was not ignoring the results in these exceptional cases. But typically, stock prices react to multiple factors at the same time.

Markets are forward looking. This means that by the time a company declares its earnings, analyst estimates, peer results etc have already given investors an indication of the company’s expected result. This gets factored into the stock price.

The valuation of the stock, market conditions and where its sector is in the cycle also matter.

The pattern of stronger earnings slightly outperforming weaker earnings held true in 9 out of the 10 financial years in our study. There was one exception, the Covid year.

In FY21, the pattern reversed: stocks in the Weak basket beat the Nifty slightly more often than the Strong ones (58% vs 55% of the time).

That is because the market had fallen significantly during Covid. So when conditions improved even a little, hopes of a recovery led to big gains in the stocks that had been hit hardest.

But if many factors play a role, shouldn’t earnings play a bigger role? How much of a stock price movement do earnings explain?

How much do the results explain?

To quantify this, we looked at every single quarter to check if the companies with the fastest profit growth were actually the same ones whose stocks outperformed, and then used rank correlation instead of averages.

A correlation of 1 means the company with the highest profit growth had the best-performing stock, the second-highest had the second-best, and so on.

If the correlation is 0 that means there is no link between the profits and the stock performance. A correlation of −1 means they move in opposite directions.

In 29 out of 40 quarters, faster profit growth went hand-in-hand with better stock performance. But the average correlation across all 40 quarters was just 0.14, only slightly above zero. The link is weak.

Put simply, in a typical results season, profit growth explained only about 7% of why some stocks did better than others.

The remaining 93% came from other factors, and from things our study cannot see, like what investors were expecting.

This can be from management’s guidance for the next year, changes in debt levels, valuations, market expectations, etc. On an economy level, RBI interest rate decisions, GDP among other factors can also play a role.

Interestingly, in some other quarters, the correlation between profits and stock performance also flipped completely.

In the September 2016 quarter, the correlation between profit growth and share-price performance was −0.37.

Only 24 companies could be ranked that quarter, so a single reading like this can swing a lot.

That seems odd right? Why would the market reward the worst performers in September 2016?

That results season ran from 30 September to 15 November 2016. On 8 November, the government announced demonetisation. A shock of that size moved stocks for reasons that had little to do with the September-quarter numbers.

But this is the story of a single quarter, which is a shorter period of time. Over a span of just three months, a stock is highly vulnerable to daily noise, rumors, and fleeting sentiment.

How do stocks react to earnings over a longer period of time?

The Long Game

To assess the long term relationship between profits and stock performance, we looked at how the stock performed over a period of one, three, five and seven year windows. Then, we compared this to the change in Earnings Per Share (EPS).

We added the EPS for the last four consecutive quarters to calculate the earnings on Trailing Twelve Months (TTM) basis. This weeds out any extremes due to seasonality. For example, a company that sells air conditioners will do well in the June quarter as the sales go up during summer months.

Now, we compared this long term EPS growth against the change in the company’s share price over those exact same multi-year windows.

The results were starkly clear. The longer the timeframe, the stronger the relation between profitability and its stock price.

In one year, the correlation was 0.29. For three and five years, the correlation was stronger. Over three years it grew to 0.40 and by five years it rose to 0.46.

For context, over a single results season the correlation between profit growth and stock performance was just 0.14.

In a short period of time, such as a quarter, stock prices react a lot to news. News can be geopolitical or about the industry that the company operates in.

A company can report phenomenal earnings but if the market sentiment is reacting to a geopolitical conflict, the chances of the stocks falling are higher.

Over a slightly longer period of time, this hype or panic tends to fade away. Suppose the stock of a company has moved higher in anticipation of good results.

But the company fails to deliver profits. Then over the next couple of quarters, investors may lose patience and end up selling the stock. The reverse can also happen.

Over an extremely long period of 7 years, the correlation stood at 0.61 for our study. This is based on a single seven-year window, March 2019 to March 2026, covering 39 companies.

If a company has consistently delivered a good set of profits, it is more likely to be rewarded by the market as the fundamentals of the company take over.

Over the long run, the Nifty 50 companies whose earnings grew the most were largely the ones whose stocks rose the most.

A stock can rise because the company posted strong results. But it can also move up, if investors are more optimistic on the stock and its expected earnings. This is why when we talk about earnings, it is almost always with valuations.

EPS measures earnings, and the price-to-earnings (P/E) ratio measures valuation. The P/E ratio shows what investors are willing to pay for each rupee of those earnings.

We wanted to test if the stock gained because of real earnings or by a change in the mood (signaled by the P/E ratio).

A shift in the valuation had a bigger impact on stock prices in the short term, compared to earnings. Over one year, the change in P/E moved the share price more than the change in EPS in 59% of cases. Earnings were the bigger driver in only 41%.

But this turns around over longer time horizons.

Over three years, EPS was the bigger driver in 65% of cases. Over seven years, it was the bigger driver in 90%.

So if earnings are the drivers in the long term, then buying only stocks with strong quarterly results should be the best investment strategy right?

Only the best

We assumed you only bought the 10 Nifty 50 companies with the fastest year-on-year profit growth, and held them until the next results season. Then repeat the process every quarter.

To check whether it was actually a good strategy, we compared it with three alternatives:

The Bottom 10 growers: Buying the companies with the worst profit growth.

All eligible companies: Buying a plain, equal-weight basket of all the stocks available in the study.

The Nifty 50: Buying the standard market index.

We ran a simulation of this exact strategy 40 times, spanning a ten-year period from August 2016 to August 2026 with an initial investment of Rs 10,000.

On the surface this looks like an attractive strategy.

If you had put Rs 10,000 in the Top 10 strategy, it would have fetched returns worth almost Rs 80,000 (Rs 79,340) at 23.0% CAGR. The Nifty 50 grew at 10.9% a year over the same period, turning Rs 10,000 into Rs 28,102.

However, it is important to note that the study tests the current composition of the Nifty 50. This means it leaves out companies that were in the Nifty 50 at some point since 2016 but later fell out of the index.

So the companies in our study were the ones that survived and grew enough to be in the top tier of the Indian stock market in 2026.

To make a fair comparison, we looked at the Top 10 strategy against an equal-weight basket of the exact same companies.

Since both carry the same bias, the gap between them is what matters. All eligible companies at equal weight returned 18.1%

The Top 10 strategy still had an edge of 4.9 percentage points.

When we dug deeper, the edge seemed to be on shaky ground.

The Top 10 strategy beat the equal-weight basket in only 23 of the 40 periods.

Most of the outperformance came between mid-2023 and mid-2024, so it was not consistent. It also did better in rising markets: when the Nifty rose, the Top 10 beat the basket in 18 of 29 periods, but when the Nifty fell, it did so in only 5 of 11.

That was not all.

The Top 10 strategy also turned out to be a riskier and more volatile bet.

Its returns swung more from quarter to quarter: a typical swing of 10.7%, against 7.6% for the basket and 6.3% for the Nifty 50.

This strategy delivered the worst fall in the study at 21.7% (between 17 February and 3 August 2020, when Covid hit markets).

Another point to add here is that the study does not include the real world costs of running this strategy.

Rebalancing a portfolio of 10 stocks four times a year means paying brokerage, Securities Transaction Tax (STT) and slippage (the gap between bid and ask prices) on every trade, plus short-term capital gains tax on profits (15% until July 2024, 20% since).

This can easily eat into the 4.9 percentage point edge.

In other words, that 4.9 percentage point edge exists before costs and taxes.

But how did the companies with the slowest profit growth fare?

This group still earned 17.9% a year, close to the 18.1% earned by the equal-weight basket. A weak quarterly result did not automatically condemn a stock over the following three months.

Conclusion

So, do quarterly results move share prices? Yes, but less than most investors think, and more the longer you wait.

Companies with the strongest profit growth beat the Nifty 50 in 54% of results seasons, against 43% for the weakest.

That is an edge, but a small one. In a typical season, profit growth explained only about 7% of how stocks ranked against each other. Expectations, guidance, valuations and the mood of the market did most of the work.

Stretch the horizon, and the picture changes. The link between earnings and share prices rose from 0.14 over a quarter to 0.61 over seven years.

Over a year, a change in valuation moved the price more than a change in earnings in 59% of cases. Over seven years, earnings were the bigger driver 90% of the time.

Chasing the best quarterly results did beat a basket of the same companies, by about 4.9 percentage points a year.

But it won in only 23 of 40 quarters, most of the gain came between mid-2023 and mid-2024, it suffered the deepest fall, and it ignores trading costs and taxes.

Earnings are not the only thing that moves a stock. In the short run, the market reacts to much more. In the long run, earnings are what it eventually follows.

So, Graham was right. In the short run, the market votes. In the long run, it weighs the earnings.

Limitations

Results season, not result day: we did not have each company’s announcement date, so each window also captures price moves before the results and other news during the season.

No analyst expectations: we compared each company’s profit growth with other Nifty 50 companies, not with what analysts expected. A “Strong” quarter could still have been a disappointment.

Gaps in history: ACE Equity data for 14 companies starts only in June 2018, Nestle India from September 2023, and SBI Life has no data. Quarters with missing data were excluded, not estimated. As a result, early quarters (FY17–FY19) compare 23–28 companies, against 32–49 later.

One-off items: the data does not separate exceptional items, so some profit jumps may include one-time gains or losses.

Survivorship bias: the study uses today’s Nifty 50 companies, which have all done well enough to be in the index now. This flatters absolute returns, especially in the backtest.

Corporate actions: quarters affected by the HDFC–HDFC Bank merger, the Shriram merger, and demergers at Reliance, ITC, Tata Motors, Grasim and Adani Enterprises were excluded where they broke the comparison.

Price returns only: stock returns exclude dividends, so all comparisons use the Nifty 50 price index. The backtest also ignores brokerage, taxes and slippage.

Long-horizon windows: the seven-year result comes from a single window (March 2019 to March 2026, 39 companies), and the five-year result from three windows.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

Netflix was going through a difficult period.

Just 2 years before, during the pandemic in 2020, when millions of people were forced to stay home, Netflix had added around 37 million subscribers.

The company had seen tremendous growth, and investors were confident about its future.

But things were beginning to change.

In 2021, Netflix added just 18 million subscribers, almost half the number it had added the previous year.

And the outlook for 2022 wasn’t encouraging either.

On 20 January 2022, Netflix announced that it expected to add only 2.5 million subscribers in the first three months of 2022.

Investors were beginning to worry that Netflix’s best years of growth might be behind it. Competition from streaming platforms such as Disney+ was increasing, and Netflix was finding it harder to attract new customers.

These concerns were reflected in its share price.

After reaching its highest closing price of $691.69 on 17 November 2021, Netflix shares fell 26.5% to $508.25 by 20 January 2022.

The next day, Netflix shares crashed another 21.8% to $397.50.

That was a total fall of 42.5% in just over 2 months.

But while investors were selling, one billionaire saw an opportunity.

Bill Ackman.

Ackman runs a major investment firm, Pershing Square. He believed investors had overreacted to Netflix’s slowing growth.

And now, the shares were available at a much lower price than just a few months earlier.

So, beginning on 21 January, his investment firm started buying Netflix shares.

By 26 January, Pershing Square had bought shares worth around $1.1 billion.

It was now one of Netflix’s 20 largest shareholders. Ackman believed he had found a good business at an attractive price.

But just three months later, things got worse.

In April 2022, Netflix announced that instead of adding the expected 2.5 million subscribers, it had actually lost 200,000. It also expected to lose another 2 million in the following quarter.

The next day, its shares crashed another 35%.

Ackman decided to sell his entire investment, taking a loss of more than $400 million.

But here’s the interesting part.

Netflix eventually recovered. The company introduced ad-supported plans and restricted password sharing, helping its subscriber growth improve.

By August 2024, its shares had surpassed their previous highest point.

But Ackman had already sold.

He had bought after a fall of more than 40%, believing it was a good opportunity. Yet the stock fell much further before eventually recovering.

Now, investors often have a similar belief about entire sectors.

If a sector has fallen 30% or 40% from its highest point, it might seem like a bargain. After all, it has already fallen so much. Surely, a recovery must be coming.

The same thinking applies when a sector has underperformed the broader market for several years. Investors might believe it has struggled for long enough and that its turn to outperform is next.

And if they invest before that comeback, they could make substantial returns.

But does a sector really become a better investment simply because it has performed badly? Or could it continue struggling for years?

That’s what we decided to test.

The experiment

We studied nine Indian sector indices: Auto, Bank, FMCG, IT, Media, Metal, Pharma, PSU Bank and Realty.

We used data from 2005 to 23 October 2026 and compared their performance with the Nifty 50.

For this, we used Total Returns Indices, or TRI. These include dividends along with changes in share prices, allowing us to compare sectors and the Nifty on the same basis.

We tested two different situations.

First: What if an investor bought a sector after it had performed worse than the Nifty 50 for three consecutive calendar years?

Second: What if an investor bought a sector after it had fallen at least 30% from its previous highest point?

We looked at what happened over the following one, three and five years, with three years as our main comparison period.

But we also wanted to answer another important question.

Even if these investments made money, was buying after a poor period any better than buying at an ordinary time?

Because sectors can deliver positive returns even without first going through a bad period.

So, wherever possible, we compared the results with what investors would normally have experienced by investing in a sector without waiting for these signals.

Let’s start with the first situation.

Part 1

Three years of losing to the Nifty 50

First, we calculated each sector’s return for every calendar year and compared it with the Nifty 50 TRI.

“Losing” here does not mean the sector fell. It means it did worse than the Nifty 50. A sector can rise 10% and still lose if the Nifty 50 rose 15%.

Whenever a sector lost to the Nifty 50 three years in a row, we marked the end of that third year as a signal. If the streak continued into a fourth or fifth year, we did not count it again. Each streak counts once.

This happened 12 times between 2007 and 2024.

Every losing-streak signal, with what the sector and the Nifty 50 TRI did over the next 1 and 3 years.

In the next year, the loser usually kept losing

After three losing years, the sector beat the Nifty 50 in the following year only 4 of 12 times. The other 8 times, it lost again.

Now compare that with the base rate. Across all 183 sector-years in our data, a sector beat the Nifty 50 in 91 of 183 years. That is about one in two.

So a three-year losing streak did not signal a comeback the next year. If anything, the weak sector stayed weak more often than usual.

The returns tell the same story. The sector rose in the next year only 4 of 12 times. In an ordinary sector-year, sectors rose 126 of 183 times.

Over three years, it was a coin toss

Over the next three years, the losing sector beat the Nifty 50 6 out of 11 times. (One signal, Media at the end of 2024, does not have three full years yet.)

The base rate for any three-year window was 84 of 165. That is also roughly one in two.

So the losing streak gave no real edge over three years either. Sometimes the comeback came. Auto after 2018–2020 more than doubled in the next three years. Sometimes it did not. Metal after 2010–2012 fell 33% over the next three years while the Nifty 50 rose 39%.

The winners kept winning

We also tested the mirror image. What happens after a sector beats the Nifty 50 three years in a row?

This happened 10 times. In the following year, the winning sector beat the Nifty 50 again 8 of 10 times.

Over three years, it beat the Nifty 50 5 of 8 times.

Losing streaks were followed by more losing in the next year. Winning streaks were followed by more winning. Over three years, both were close to the base rate.

So in the short run, sectors that were struggling usually kept struggling for another year. Sectors that were doing well usually kept doing well.

The samples here are small. 12 and 10 events are not a law of nature. But the direction is clear. A long losing streak was not, by itself, a sign that the turn was near.

Part 2:

A 30% fall from the highest point

The second strategy was to buy a sector after it had fallen at least 30% from its previous highest point.

For this, we tracked each sector’s closing level every trading day and recorded the highest level it had reached until then. We then checked how far it had fallen from that level.

The first time a sector closed 30% or more below its highest point, we counted it as a buying opportunity.

We counted each fall only once. So, if a sector fell 30%, then 31% or 32%, it was still considered one fall. A new opportunity was counted only after the sector recovered to its previous highest point and then fell again.

We also repeated the same test for falls of 20% and 40%.

Across the nine sectors, we found 31 instances of a 30% fall between 2006 and 2026.

How far each sector TRI was below its peak on every trading day. Red dots are the 30% signals.

Buying after a 30% fall was close to buying on any day

Three years after buying a sector following a 30% fall, it beat the Nifty 50 in 16 out of 29 cases, or around 55%.

The other two instances, IT in February 2026 and FMCG in September 2026, were too recent to have completed three years.

Now, how does this compare with buying on an ordinary day?

To find out, we looked at every trading day across all nine sectors. We found 40,740 possible investments with a complete three-year holding period.

Of these, 21,047 beat the Nifty, or around 52%.

So, buying after a 30% fall had a success rate of 55%, compared with 52% on an ordinary day. The difference was quite small.

What if we looked at different holding periods?

After one year, the sector beat the Nifty in 14 out of 29 cases, compared with an ordinary-day success rate of 51%.

Over five years, the results were better. The sector beat the Nifty in 18 out of 27 cases, compared with 50% on an ordinary day.

However, this five-year result was influenced by a few unusual market periods, which we’ll explain in the Things to Note section.

But what if we ignored the Nifty and simply checked whether investors earned a positive return on their investment?

After three years, 25 out of 29 investments had made money, compared with 78% of ordinary-day investments.

Over five years, 21 out of 27 had made money, compared with 84% on an ordinary day.

So, while buying after a 30% fall often resulted in positive returns, it didn’t consistently improve an investor’s chances of making money or beating the Nifty compared with investing on an ordinary day.

A deeper fall did not mean a better buy

If a 30% fall is a good entry, surely 40% is even better?

Not clearly.

Over three years, the 20% signals beat the Nifty 50 24 of 43 times, the 30% signals 16 of 29 times, and the 40% signals 9 of 14 times. Over five years, the order flipped. The 40% signals beat the Nifty 50 only 7 of 14 times.

There is no steady pattern where a bigger fall leads to a better result.

The share of 20%, 30% and 40% signals that beat the Nifty 50 TRI, compared with the base rate.

30% was often not the bottom

This is the part that hurts in real life.

After the 30% signal, 16 of 31 sectors fell at least another 10% before recovering. 12 of 31 fell at least another 20%.

Imagine buying Realty in January 2008 because it had fallen 30%. It then fell another 90% from that point.

Media in February 2008 fell another 70%. Metal in February 2008 fell another 68%. PSU Bank in May 2011 fell another 68%.

How much lower each sector went after the 30% signal.

Most falls recovered, but some took more than a decade

Out of 31 signals, 27 sectors got back to their old peak. 12 of them did it within a year.

But 4 took more than three years. Media took 7.9 years after its 2008 fall. PSU Bank took 12.3 years after 2011. Metal took 13.1 years after 2008.

And 5 were still below the old peak five years after the signal.

Time from the 30% signal until the sector closed back at its old peak.

The most extreme case is Realty.

If you had put Rs 100 into the Nifty Realty TRI at its peak on 14 January 2008, it would have fallen to about Rs 7 at its lowest point in February 2016. On 23 October 2026, more than 18 years later, it was worth about Rs 46. The same Rs 100 in the Nifty 50 TRI was worth about Rs 455.

Realty has not returned to its January 2008 peak in more than 18 years.

Takeaway

So, does a sector become a better investment simply because it has performed badly?

Our study suggests that it’s not that simple.

After three years of underperformance, sectors continued to lag the Nifty in the following year in 8 out of 12 cases. Even over three years, the chances of beating the Nifty were roughly the same as usual.

Buying after a 30% fall didn’t offer a clear advantage either. The chances of beating the Nifty over the next three years were almost the same as buying on an ordinary day.

And in 12 out of 31 cases, the sector fell at least another 20% after the initial decline. Some sectors even took more than a decade to recover.

But this doesn’t mean buying a struggling sector is always a bad idea.

Sometimes, investors overreact to bad news, and the sector eventually recovers. Other times, the sector is struggling because its businesses are facing serious problems that could take years to resolve.

The important thing is that a sector isn’t automatically cheap just because it has fallen a lot.

Instead of looking only at how far a sector has fallen or how long it has underperformed, investors also need to understand why it has struggled and what could change in the future.

Things to Note

• Small sample: We found only 12 cases of three-year underperformance and 31 cases of a 30% fall. A few different outcomes could significantly change the results.

• Falls happened together: 15 of the 31 major falls occurred in 2006 and 2008. Since multiple sectors fell during the same market crashes, these aren’t independent events.

• 2008 influenced five-year returns: Many investments started in 2006–2008 and benefited from the 2009–2010 recovery.

• Some data was calculated later: Auto, Media and Metal indices launched in July 2011, while PSU Bank and Realty launched in August 2007. NSE calculated earlier values retrospectively. Using only post-launch data, the 30%-fall strategy beat the Nifty in 13 of 21 cases over three years and 14 of 19 over five years.

• Long falls counted once: A new fall was counted only after the sector regained its previous highest point. Since Realty never recovered to its 2008 peak, later falls weren’t counted.

• No costs or taxes: We used index returns, excluding fund expenses and taxes.

• Not investment advice: Past results don’t predict future sector performance.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

That was the name of an octopus living in an aquarium in Germany.

And he wasn’t just any ordinary octopus.

He became a global celebrity during the 2010 FIFA World Cup.

But why?

Well, Paul was known for predicting the winners of football matches.

Before each match, his keepers would place two transparent boxes inside his tank. Both boxes contained food, but each had the flag of one of the countries playing the match.

Whichever box Paul opened first was considered his prediction for the winner.

Now, an octopus predicting football matches sounds ridiculous.

But something interesting happened.

Paul correctly predicted the winners of all eight matches he was asked to predict during the 2010 World Cup.

It was statistically astonishing because the probability of that happening is just 1 in 256 times.

This led to people believing that Paul had predictive powers.

His predictions became so popular that when he picked Spain to beat Germany in the semi-final, some angry German fans called for him to be eaten.

Spain’s Prime Minister even joked about sending protection for him.

But there was something people paid less attention to.

Paul had also predicted the outcomes of Germany’s matches during the 2008 European Championship.

Back then, he got only four out of six predictions right.

He incorrectly predicted that Germany would beat Croatia and later that Germany would defeat Spain in the final.

Yet, his eight correct predictions in 2010 were enough to make him a global celebrity.

This tells us something interesting about how we think.

When we see a pattern working repeatedly, we tend to believe there’s a reason behind it, even when it could simply be luck.

And we often remember the times a prediction worked while paying less attention to the times it failed.

Something similar happens in the stock market.

Over the years, investors have developed several beliefs about when it is a good time to buy or sell shares.

Some believe that buying shares during Muhurat Trading on Diwali brings good fortune.

Others follow sayings such as Sell in May and Go Away, As January Goes, So Goes the Year, or the Santa Claus Rally.

These beliefs have survived for years, partly because there have been times when they worked.

But how often have these beliefs failed? And more importantly, would investors have earned better returns by following them?

With Diwali just around the corner and the special Muhurat Trading session coming up, we thought this would be a good time to find out.

So we picked four popular stock market beliefs and tested how well they worked in the Indian market between 2015 and 2025.

We looked at how often each belief worked and compared the returns with what investors would have earned

The Study

We picked four popular proverbs in the stock market and analysed if they held true across 2015 to 2025.

For every proverb, we asked three questions:

• What would an investor following it have done?

• How often did it work?

• How did it compare with a normal period of the same length, chosen at random?

The third question matters most. A rule that “works” 8 times out of 10 means little if the market goes up 8 times out of 10 anyway.

For this, we used daily closing data from NSE Indices for the Nifty 50, the Nifty 50 Total Return Index (which includes dividends), the Nifty 500, the Nifty Midcap 150, the Nifty Smallcap 250 and the Nifty Auto.

Gold prices are the BSE spot price in Mumbai, in rupees per 10 grams. We also used monthly FPI equity flows from NSDL.

Study 1: Muhurat Trading

Investors believe that purchasing stocks and gold during the Diwali is auspicious and guarantees good luck and strong returns.

The stock market has a special one-hour session every year on Diwali called ‘Muhurat Trading’ that many investors look forward to.

So we decided to track the investments made in stocks and in gold during this festive window, from Diwali 2015 to Diwali 2024 to see if this belief actually holds water. We used Diwali 2015 to 2024 so that every Diwali in the study has a full year of returns after it.

The Muhurat Hour is actually positive.

Nifty 50 has ended in green for 8 out of 10 years, a normal day is only 54% positive. The average move in these 10 years was 0.36%.

The broader market showed a lot more investor optimism. The Nifty Midcap 150 rose on 9 out of 10 occasions, and the Nifty Smallcap 250 was up on all 10 instances.

For gold, measured from the last spot price before Diwali to the first one after it (spot gold isn’t quoted on Diwali), prices rose in only 4 of 10 years, with an average move of just 0.04%.

The gap between those two prices ranged from 1 to 10 days, so this is a rough check rather than a like-for-like comparison with the one-hour session.

This shows us why this belief is popular. But the gains tend to fade over time.

When we compared someone who buys exactly on Muhurat trading day (Ritualist) to another investor who bought on a random day, the results were different.

The random buyer made slightly more money over six months to a year.

6 months in, Muhurat buyers earned 1.94% on average from Nifty 50, and 3.92% and 3.09% from midcap and smallcap indices. However, a random buyer made 6.12% from Nifty 50 itself.

This means the Muhurat buyer made one-third to half of what an average day buyer earned, six months after Diwali.

A year later, the gap between their returns reduced but the random buyer still had an edge.

Over a year, Gold beat Nifty 50 returns in 8 out of 10 years while the Nifty 50 outperformed the bullion only twice, in 2016 and 2020.

There are also other factors to consider like the market mood. An investor who bought smallcaps during Diwali 2017 saw their portfolio fall 22.5% a year later.

This could be due to multiple factors like the stretched valuations after that rally, a few regulatory changes like SEBI’s mutual fund recategorisation, return of long term capital gains tax in 2018, a weakening rupee and rising crude oil and the IL&FS default in September 2018, which squeezed NBFC funding.

Someone who bought Nifty 50 in Diwali 2019 witnessed a sharp fall of 21% in the investment in just six months.

Valuations, earnings growth trajectory, the debt taken by companies and many other reasons also factor into stock prices. So while the belief holds for D-Day, it did not hold for the longer term.

Gold has no Muhurat-session price, so we track it from the last price before Diwali.

For gold, we looked at the returns a month after Diwali trading for the short term. The results were not as shiny as the yellow metal.

Over six months to a year, gold bought around Diwali actually did slightly better than gold bought on a random day: 8.19% against 6.18% after six months, and 17.95% against 15.45% after a year. The first month was the weak spot.

8 out of 10 times, gold purchased around Diwali fell a month later with an average return of -1.25%. For context, during a normal month, gold rose about 56% of the time.

Auto stocks vs Festive Boost

Diwali is not just considered auspicious to invest in stocks and gold, but also to buy vehicles. Many people believe that the demand for cars and bikes will rise in this period, which will give a ‘festive boost’ to auto stocks.

According to the Federation of Automobile Dealers Associations’ (FADA) 42-day festive retail data, covering Navratri to 15 days after Dhanteras, festive vehicle sales have been rising:

In 2023: sales rose 19%, to 37.93 lakh vehicles from 31.95 lakh the year before

In 2024: registrations rose 11.76%, to 42,88,248 from 38,37,040

In 2025: sales rose 21%, to 52,38,401 from 43,25,632, with GST-led price cuts helping

(Note: The base figures get revised between releases,so year-on-year totals won’t chain exactly.)

This means auto stocks will outperform, right?

We studied the performance of the Nifty Auto index in the last decade from Diwali 2015 to Diwali 2024.

A month prior to Diwali, the Nifty Auto index beat Nifty 50, 6 out of 10 times. In a normal month, the index beat Nifty 50 52% of the time. So the edge was worth a coin toss.

Post Diwali, the auto index lagged the Nifty 50 in 8 out of 10 years, and fell outright in 7 of them.

Why did this happen?

While automotive sales can tick up during the festive season (as the FADA numbers above show), these may or may not show in the stock price immediately.

In some instances, the investor anticipation of auto sales inching higher is priced into the stock prices and it may move higher on expectation.

Take 2025. From 14 August, just before the GST overhaul was first announced (15 August), to 22 September, when it took effect, the Nifty Auto rose 12.6% while the Nifty 50 rose 2.3%.

From 22 September to Diwali, during a record festive season, it rose just 0.3%, against 2.6% for the Nifty 50.

On other occasions, the sales may take time to show up as earnings in the next quarter which is when the stocks may react more. Valuation of the stocks, interest rates, the investor sentiment in the market at the time will of course also matter.

Another thing to note is that the Nifty Auto index is composed of not just car and bike manufacturers, but also includes component and tyre makers whose sales aren’t tied to festive buying.

This could have also led to the auto index’s performance being weak in the period of our study.

To recap, the ‘Muhurat Blessing’ belief sticks because muhurat trading has yielded gains for investors most of the time. However, the session is short and the returns do not guarantee long term advantage.

What happens to your portfolio over the next year depends largely on global events, interest rates, and company earnings, not the festive day you clicked ‘buy.’

While the muhurat trading belief is popular in the Indian investing community, the concept of popular investing beliefs is quite global.

There are a few investing proverbs that are famously repeated and believed by investors in the US and other countries as well.

How does investing based on these globally popular beliefs fare? Do they work for Indian markets?

Study 2: The January Barometer

There is a famous proverb that claims “As January goes, so goes the year.” This means that the yearly returns tend to be good when January gives good returns and vice versa.

Investors who believe in this, wait to see if the markets start the year strong before deploying funds for the year.

We tested two versions of the proverb. First was looking at the first 5 trading days in January and comparing it with the returns for the rest of the year.

The other was comparing the returns of the whole month of January with the returns of the rest of the year (February through December).

The 5-Day Rule is a Coin Toss

In the first version of the proverb that looked at the returns for the first 5 days of January vs the rest of the year, the signal matched the year’s direction half the time.

Nifty 50 fell in the first five trading days of January in 5 out of 10 years in our study, and interestingly, in all 5 of those years, the Nifty 50 still rose over the rest of the year.

In the other 5 years, the Nifty 50 rose in the first five days and kept rising for the rest of the year.

There was no year in which a weak start was followed by a weak year.

The Nifty 500 index did fall in 2018 by 3.38%, but the first 5 days of January returns of this year were positive which meant it failed to warn investors.

Between 2017 and 2025, the 5 day rule did match the rest of the year’s direction 4 out of 9 times for Nifty 500. (9 years: measuring 2016 needs the Nifty 500’s closing value on 31 December 2015, which is not in our dataset.)

The Full-Month Rule

While the first 5 day signal matched the market’s direction half the time (only because the market rose every year), the full month was almost always wrong.

January returns were negative 80% of the time in the last ten years. On all of those 8 occasions, Nifty 50 rose over the remaining 11 months.

The two years when January returns were positive the results were split. In 2017, a positive January was followed by the markets rising for the rest of the year.

But in 2018, a strong January return of 4.72% was followed by the market falling by 1.5% for the rest of the year. So the full-January proverb pointed in the correct direction only once in the last decade.

Waiting for the entire month of January to finish before investing yielded terrible predictions.

The biggest limitation of testing the January Barometer in this decade was that the Nifty 50 rose every year from 2016 to 2025. Because there were no down years overall, every negative January warning was a false alarm.

Falling for those false alarms cost investors heavily.

If you invested Rs 1 lakh and simply stayed in the market, it would have grown to about Rs 3.29 lakh.

If you sat out every year that had a bad first five days, your Rs1 lakh would have only grown to Rs 1.81 lakh. If you sat out every year with a negative January, it would have grown to only about Rs 1.21 lakh.

So the January Barometer sticks because a strong start of the year gives people a psychological sense of confidence. However, a few days, or even a month is just a tiny slice of time.

The market trajectory over the long term is driven by many factors, as we have discussed, trying to time your entry based on a single month usually just results in missing out on standard market gains and the power of compounding

Study 3: Sell in May and Go Away

This stock market proverb assumes that stock markets are typically weak during the summer months. As per this rule, investors can sell their stocks at the end of April and hold their cash safely, only to buy back into the market in November.

In India, May to October is actually the stronger half.

The proverb did not hold water in the Indian market. The May to October period that investors are supposed to sit out were much more profitable than the rest of the year.

The average returns in the May–October half was 9.40% while that of the November–April half was 5.85%.

In terms of consistency, the summer half beat the winter half in 6 out of 9 cycles. It lagged in the other 3, and was negative only once, falling 2.35% in 2018. (9 cycles: the 10th, May 2025 to April 2026, needs index data up to April 2026, which is beyond our dataset.)

The worst crash of the study came in the winter months between November 2019 and April 2020 when Nifty 50 fell almost 17%. A Seasonal Exiter, would have been completely invested in this period having bought back in November.

The cost of stepping away from the market in the summer months was also huge. If an investor put Rs 1 lakh into the Nifty 50 in May 2016, and stayed invested till April 2025, the corpus would have grown to Rs 3.46 lakh which is about 14.8% per annum.

If the investor sold in May and held cash earning nothing, the corpus would have grown only about 5.2% a year, to Rs 1.57 lakh.

For the Seasonal Exiter to just break even with the buy-and-hold investor, their cash sitting in the bank would have needed to earn a massive 18% a year every summer.

Some also believe that the proverb works for smaller stocks rather than largecaps, because trading is thinner in the summer months. Not in our data: smallcaps did better from May to October in 5 of 9 cycles, averaging 11.73% against 6.89% for November to April.

Smallcaps crashed 24.58% in the summer of 2018, the sharpest summer drop in our study, but they also jumped the most, 37.64% in summer 2020. Rs 1 lakh left in smallcaps grew to Rs 3.64 lakh; selling every May left Rs 1.55 lakh.

So while the proverb comes from Western markets, Indian markets defied this rule in the last decade, as stepping away for the summer was one of the most expensive mistakes an investor could make.

Study 4: The Santa Rally

The end of the year brings Christmas cheer to the stock market as well. At least that is what is believed to be true by investors in the case of a Santa Rally.

It is assumed that the last 5 trading days of December and first 2 trading days of January will see the markets inch higher.

We decided to test this by assuming that an investor buys just before this window and sells it at the end of the window.

This is the one proverb that held up.

Nifty 50 rose in 10 out of 10 santa windows (December 2016 to January 2026) with an average return of 1.85%. Investing in a random 7 day stretch over the same period rose only 60% of the time with an average return of 0.38%.

Smaller stocks did even better. The Nifty Midcap 150 index rose on all 9 instances with a higher average returns of 3.02%. Smallcap index also rose on all occasions and gave an even higher average return of 3.98%. (9 windows: our Midcap and Smallcap data ends on 31 December 2025, so the December 2025 to January 2026 window can’t be measured.)

The catch however, is that it is a small prize. An investor who only invested in the market during the Santa window turned Rs 1 lakh into Rs 1.20 lakh over the decade. But an investor who stayed invested in the Nifty 50 over the same period made Rs 3.30 lakh.

A common explanation is that foreign investors go on holiday, leaving thin markets that drift up.

The FPI data however, does not support it.

The net FPI flow in December was smaller than the median month in 6 out of 10 years, so yes it is a relatively quieter month. But in 2020 and 2023, December was the single biggest month of foreign buying of the year.

And the rally did not depend on foreign buying.

FPIs were net sellers in December of 2016, 2021and 2025. The Santa window was still positive each time, and 2021’s was the best of the decade at 4.71%.

So the Santa Rally has worked enough times to be remembered but the mistake is treating a one week pattern as a strategy.

And a pattern everyone knows can be traded away.

If enough people buy earlier, the gain moves earlier or disappears, and one shock in a thin holiday week could undo several years of small wins.

The money also has to sit somewhere for the other 51 weeks; if it is in the market anyway, the Santa trade adds nothing.

In rupee terms, the Santa trade earned about Rs 20,000 over the decade, against about Rs 2.3 lakh for simply staying invested.

We have not considered taxes in the study but short term capital gains, brokerage and STT will apply and eat into some of the gains.

Conclusion

Investing in the markets based on certain proverbs does not always pan out. The Muhurat trading window did little harm and little good for the buyers in the period.

Muhurat buyers of stocks earned slightly less than investors who bought on any other day.

Diwali gold buyers lost ground in the first month but did slightly better over six months to a year.

An auspicious day did not always translate into an auspicious year.

Globally well known proverbs that tell you to step out of the market often cost you the most.

Sell in May and Go Away and predicting markets based on the January returns did far less for investors compared to those who did nothing.

They did not avoid bad periods, but they did miss the good ones.

The Santa Rally pointed to a pattern that did hold up but it is important to remember that this is a pattern not a strategy for wealth creation. A week of gains is too small against a decade of compounding.

Each proverb offered investors a reason to act on the calendar but ultimately, the market

rewarded staying invested more than any of the proverbial investing.

Limitations

Index data: NSE Indices daily closing values for the Nifty 50, Nifty 50 Total Return Index, Nifty 500, Nifty Midcap 150, Nifty Smallcap 250 and Nifty Auto.

Gold: BSE spot price, Mumbai, Rs per 10 grams (CMIE). Spot gold is not quoted on most Diwali days, so the entry is the last available price before the Muhurat session. Gold’s Diwali move compares the last price before Diwali with the first price after it; the gap between the two was 1 to 10 days.

FPI flows: NSDL, monthly net investment, equity.

Sell in May assumes the investor holds cash earning nothing between May and October.

Muhurat dates and session returns: NSE index data.

Returns are price returns unless stated and exclude dividends. The main Sell in May comparison uses the total return index.

Muhurat: Diwali 2015-2024. One month = 30 days, six months = 182 days, one year = 365 days, using the last available price on or before each date. 12-month returns cover every Diwali in the study.

January Barometer: 2016-2025 (Nifty 500: 2017-2025). Sell in May: May 2016-April 2025, with re-entry at the last October close. Santa Rally: Nifty 50, December 2016-January 2026; other indices, December 2016-January 2025.

Baselines use every trading day in the same period with a full forward window available.

Taxes and transaction costs are excluded. They would hurt the frequent-trading strategies further.

Sample sizes are small (9-10 observations per study). These are historical observations, not predictions.

Past performance does not guarantee future results.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

In 2001, Naspers, a South African media company, bet $32 million on a Chinese startup.

This startup later built WeChat, which is often called China’s WhatsApp.

It is used by 1.4 billion people, and not just to chat but also to pay bills, shop and send or receive money.

The same company also became the world’s largest game publisher. Among its many hits is PUBG Mobile.

Tencent.

Valued at nearly $500 billion as of early October 2026. Naspers got a big slice of this pie since they had invested early on.

By 2018, that $32 million investment was valued at more than $175 billion (that is more than 5,000x)

It came to be one of the most successful venture capital deals ever. Even today, Naspers remains the largest shareholder in Tencent.

Naspers now holds its Tencent stake through its investment arm, Prosus. And one of the other companies they invested in was Indian.

Byju’s.

In 2018, it started investing in Byju’s and over the years, Naspers and Prosus put a total of $536 million into the company.

By October 2022, Byju’s was valued at more than $22 billion.

Other well-known global investors, including BlackRock, the world’s largest asset manager, also backed it.

But things quickly turned sour.

By the end of 2023, about a year after Byju’s reached its peak valuation, Prosus valued the company at less than $3 billion. BlackRock valued it even lower, at just $1 billion.

Things got worse. In 2024, Byju’s was struggling to repay its debts and was caught up in a legal process over the money it owed. Prosus and BlackRock both considered their investments in Byju’s to be worth zero.

So, the interesting thing here is that the same investor had invested in two companies.

One turned out to be one of the most successful startup investments ever. The other was considered worthless.

When a big investor puts money into a company, other people notice.

Some might even invest because they think that if such a big investor is putting money into it, the company must be good.

But does that really mean the company will do well?

We see something similar in the stock market too.

QIP, or Qualified Institutional Placement.

It is one of the ways in which listed companies can raise money.

The company issues new shares, but instead of offering them to everyone, it sells them to a selected group of big investors. These include mutual funds, foreign investors and insurance companies. Regular investors like you and me cannot participate.

Unlike IPOs and FPOs, which require lengthy, heavily regulated processes such as filing detailed offer documents with SEBI, QIPs let companies raise money from institutions much faster.

QIPs are priced using a SEBI formula based on the stock’s recent prices, which sets a floor price. The floor price is the benchmark.

Companies can then offer institutions a discount of up to 5% on that floor price. This discount is optional, not mandatory.

This raises a question. If the most sophisticated investors are writing big cheques, shouldn’t the stock do well?

After all, if the “smart money” is pouring thousands of crores into a stock, shouldn’t you be buying it too?

We decided to test this.

Can institutional buying be considered the stamp of approval, and could that lead the stock to outperformance when a company raises money through a QIP?

Do these stocks create wealth in the long term?

The Study

We picked the five biggest QIPs by issue size in each year from 2016 to 2025. That gave us 50 QIPs.

We removed 3 because those companies later merged with other companies and their shares were no longer trading.

So, we were left with 47 QIPs from 40 companies. Together, they raised about Rs 2.5 lakh crore.

Some companies raised money through QIPs more than once. For example, Bajaj Finance and Godrej Properties appeared 3 times each. Bank of India, Kotak Mahindra Bank and State Bank of India appeared twice. We looked at each QIP separately.

Then, we checked how the stocks performed at different points:

• One month before the QIP was announced.

• One day after the announcement.

• Between the announcement and the day the new shares were given to investors.

• One month and one year after the shares were given.

• From the day the shares were given until 5 October 2026.

We also compared each stock’s performance with the Nifty 50 over the same periods to see whether it did better or worse than the broader market.

Stocks ran up before the QIP, not after it

In the month before the QIP started, 29 of the 47 stocks went up, and 23 performed better than the Nifty 50.

The median stock gained 2.7%, compared to 1.1% for the Nifty.

So, does that mean you should buy a stock when you hear a QIP is coming?

Not really. In many cases, the stock had already risen before the QIP started.

But why does this happen?

One possible reason is that companies prefer to raise money when their share prices are high.

Think about it. When a company raises thousands of crores through a QIP, it issues new shares. This means existing shareholders now own a smaller percentage of the company.

But if the share price is high, the company can raise the same amount of money by issuing fewer new shares. So, existing shareholders give up a smaller share of ownership.

There could be other reasons too.

Before launching a QIP, investment bankers may privately meet mutual funds and foreign investors through something called a roadshow. This helps them understand whether big investors are interested in buying the shares and how much demand there might be.

Some companies also choose to launch QIPs after reporting good quarterly results, when more investors might be interested in buying their shares.

Or the stock could already be rising because the company’s profits are growing or because the entire sector is doing well.

So, a company may choose to launch a QIP because its stock has been performing well, rather than the stock rising because of the QIP.

And there’s another problem. Regular investors usually cannot know for sure when a QIP is coming. Board or shareholder approvals to raise money might give an early hint, but they don’t tell you exactly when it will happen.

Now, what happened after the QIP started?

The price changes were much smaller.

The day after launch, the median stock rose just 0.4%, compared to 0.1% for the Nifty. Of the 47 stocks, 28 rose and 28 performed better than the Nifty. These were not necessarily the same 28 stocks.

Between the launch and the day the new shares were given to big investors, the median stock gained 1%, while the Nifty fell slightly by 0.1%.

During this period, investment bankers were collecting orders from big investors and deciding how many shares each would get. But the stock prices didn’t move much. Only 25 of the 47 stocks rose.

And one month after the shares were given, things looked worse.

The median stock had fallen 1.1%, while the Nifty had risen 0.7%.

Of the 47 stocks, 21 rose (45%) and 26 fell (55%). And 29 stocks (62%) performed worse than the Nifty.

So, while many stocks had risen before the QIP, that strong performance didn’t necessarily continue afterwards.

Now, remember that regular investors like you and me cannot buy shares directly through a QIP. We can only buy them from the stock market at the market price.

So, if you bought the stock around the time big investors received their shares, would you have paid more than them?

Who got the better price?

Now, big investors can sometimes buy shares through a QIP at a discount. But regular investors like you and me can only buy those shares from the stock market.

So, who ends up paying less?

First, let’s understand how the price is decided.

SEBI has a formula that looks at the highest and lowest closing share prices in each of the two weeks before the company decides to open the QIP. The average of these prices helps decide the minimum price at which the company can issue shares.

With shareholder approval, the company can then offer a discount of up to 5% on this floor price, and the final price institutions pay is called the issue price.

For example, IDFC First Bank’s 2021 QIP had a floor price of Rs 60.34. The bank offered a discount of Rs 2.99, so institutions paid an issue price of Rs 57.35.

Since retail investors cannot participate in a QIP, they can only aim to get a piece of the pie on allotment day from the open market.

So we compared the price that institutional investors paid for the QIP, called the issue price, with the price retail investors could have paid on allotment day (closing price). (Allotments on non-trading days use the previous trading day’s close.)

In 38 of 47 cases (81%), institutional investors had bought shares at a price which was lower than the price on allotment day.

The median gap between issue price and allotment day closing price was 5.5% for our study. This means institutional buyers entered with a price cushion of 5.5% over retail buyers who bought on allotment day.

Part of this is the discount. The gap was also much wider where the stock rose while the QIP was open. Where the stock rose between launch and allotment, the median gap was 10.4%; where it didn’t, it was 0.5%.

But while the direction was obvious, the size and exceptions were not.

For Adani Energy Solutions, this gap in prices reached 29.3%. Institutional investors had almost a 30% safety cushion in this case which is huge.

However, this is not guaranteed for the stocks in our data set.

9 out of 47 stocks closed below issue price on allotment day. All 9 fell between launch and allotment, by 0.8% to 5.1%. IDFC First was the extreme case where it closed 3.6% below its issue price on allotment day.

The Nifty also fell in 7 of the 9 windows. So market conditions could have played a role.

In all 9 cases, the issue price was close to the stock’s market price when the QIP was launched, at most 4.2% below it. That left institutions with very little cushion.

And some were already falling, 6 of the 9 had fallen in the month before launch.

For these 9, the allotment-day close was a median 0.8% below the issue price. On paper, institutions were briefly behind.

Retail investors could buy these 9 slightly below what institutions paid.

‘Follow the big funds’ can still work, since a stock can rise after you buy above the issue price. But retail investors usually start from a higher price than the institutions did.

That is the case at least for the short term. But did the results change over a longer period of time?

What happened a year later?

We looked at the performance of 46 QIPs (excluding Swiggy because it hasn’t completed a year since its QIP) over 1 year after allotment.

25 stocks were trading above their allotment-day closing price, while 21 had fallen below it.

The picture had changed quite a bit when it came to returns.

Only 19 out of the 46 QIPs managed to beat the Nifty 50 and the median QIP stock returned 5.6% in this period while Nifty gained 8.9%.

What had changed in a year?

The market cycle could have turned negative, or the company could have faced some crisis.

For example, Idea Cellular (now Vodafone Idea), which raised money through a QIP in 2018, was facing issues by early 2019. After Idea’s merger with Vodafone India, the combined company recorded losses of about Rs 5,000 crore each in its first two quarters, driven by a pricing war in the telecom sector.

When the stock closed more than 5.5% above the issue price, only 4 of 22 beat the Nifty over the next year.

Stocks that rose more than 5% in the month before launch beat the Nifty in 6 of 16 cases a year later, against 13 of 30 for the rest.

This means pre-launch gains were a weak indicator of 1-year returns post QIP.

Stocks that had risen the most did worse a year later

Now, we wanted to check something else.

Did stocks that were already trading much higher than the price big investors paid through the QIP perform better over the next year?

To find out, we divided 46 QIPs into three groups based on the difference between their market price on allotment day and the issue price.

Remember, the median price difference in our study was 5.5%. So, we used that to divide the stocks.

Here’s what we found.

15 stocks were trading up to 5.5% above their issue price. Of these, 13 beat the Nifty over the next year. Their median one-year return was 31.9%.

That’s quite good, right? But here’s where things get interesting.

22 stocks were trading more than 5.5% above their issue price. Only 4 of them beat the Nifty over the next year. Their median return was actually a loss of 14.4%.

And 9 stocks were trading below their issue price. Only 2 beat the Nifty, and their median return was a loss of 0.7%.

So, in our study, stocks that were already trading much higher than the price institutions paid performed the worst over the next year.

Interestingly, the stocks that were trading only slightly above the issue price did the best.

We also checked whether changing our 5.5% cut-off would change the result. Even when we used cut-offs anywhere between 3% and 10%, the overall pattern remained the same, although the exact numbers changed.

But this was only for the first year.

When we looked at returns from allotment until 5 October 2026, the difference between these groups became much smaller.

Of the stocks trading more than 5.5% above the issue price, 12 out of 22 beat the Nifty. Among the remaining stocks, 13 out of 22 beat it.

So, the pattern we saw over one year didn’t really continue over longer periods.

What if you held on for longer?

So far, we’ve looked at what happened around the QIP and one year after it.

But what if you had bought these stocks on allotment day and held them until 5 October 2026?

For this, we had to remove two companies, Samvardhana Motherson and Vedanta, because they later split parts of their businesses into separate companies, and our share price data didn’t fully account for those changes.

We also excluded Swiggy from the comparison with the Nifty because a year hadn’t passed since its QIP.

First, let’s look at whether the stocks went up at all.

Of the 45 QIPs we could track, 36 were trading above their allotment-day prices.

That’s a pretty good number. But just because a stock went up doesn’t mean it was a better investment than the Nifty.

So, we compared their returns with the Nifty over the same periods.

Since these QIPs happened in different years, we calculated their annualised returns to make the comparison easier.

Of the 44 QIPs, 25 performed better than the Nifty 50.

The median QIP stock gave an annual return of 8.8%, compared to 7.9% for the Nifty.

That’s better than what we saw after one year, but it’s still not a very strong result.

For example, when we looked only at QIPs that had completed at least three years, just 16 of the 33 beat the Nifty.

And the results also changed depending on when we measured them.

If we had ended our study on 30 March 2026 instead, only 18 of the 42 QIPs would have beaten the Nifty. We used 42 here because three QIPs had not yet completed a year at that point, while Vedanta was still included because its demerger came later.

So, even over longer periods, there wasn’t a clear pattern showing that stocks with big QIPs would consistently perform better than the market.

Takeaway

When a company raises thousands of crores from big investors, it’s easy to think those investors must know something that regular investors don’t.

But we saw from the study that it didn’t really matter.

And over longer periods, the results were close to 50-50.

But why isn’t there a clear pattern?

Think about it. A QIP only tells us that big investors were willing to put money into a company. It doesn’t tell us what will happen to that company next.

A company might raise money to expand its business, repay debt or fund new projects. But raising money is one thing. Using it well is another.

Some companies might use the money to grow their profits. Others might struggle because of competition, falling demand or problems within the business.

And even if the company does well, its stock might not. If investors have already pushed the share price too high, good business performance might not be enough to deliver good returns.

The overall stock market also matters. A company might perform well but still struggle to beat the Nifty if the broader market rises faster.

So, a big QIP tells you how much money a company managed to raise. It doesn’t tell you how much money you will make by investing in it.

Just because big investors are putting thousands of crores into a company doesn’t mean you should follow them.

Limitations

• We used ACE Equity data, which may not include every QIP. For example, Axis Bank’s Rs 10,000 crore QIP in August 2020 was missing. Including it wouldn’t change our conclusions.

• Our study covered only 47 QIPs. Also, some of the biggest QIPs were relatively small. In 2016, the fifth-largest raised just Rs 150 crore.

• We considered share price changes, adjusted for stock splits, bonuses and rights issues. Dividends, taxes and transaction costs were excluded. We also used the Nifty 50 price index, which excludes dividends.

• QIPs from 2024 and 2025 have less than two and a half years of performance data.

• We excluded 3 QIPs because the companies merged (Bharat Financial Inclusion and HDFC Ltd twice). Swiggy was excluded from one-year and annualised comparisons, while Samvardhana Motherson and Vedanta were excluded from long-term calculations because our data didn’t fully account for their business splits.

• Some companies raised money multiple times, so they had a greater influence on the results.

• We didn’t separate other fundraising activities happening around the same time. For example, HDFC Bank’s 2018 QIP happened alongside a US share offering, while Indian Overseas Bank received government funding in the same financial year.

• The study shows patterns, not exact reasons. We didn’t examine company valuations, business performance or how the money raised was used.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

India announced a nationwide lockdown because Covid-19 was spreading.

For the next 21 days, people were told to stay at home and almost everything except essential services was shut. The lockdown was later extended as well.

But almost immediately after it was announced, there was another problem to deal with.

Money.

People’s cash flows were getting disrupted. Businesses were shut, people couldn’t go to work, and nobody really knew how long all this would continue.

Some estimates say around 12.2 crore jobs were lost in April 2020 alone.

When work stops, income can stop too.

But the money coming into a household is usually already allocated every month, be it groceries, medical bills, education, EMIs and savings.

Everything has a certain amount of money assigned to it.

And for many investors, one part of those savings could be an SIP. We will come to that in a bit.

Since it was the government that imposed the lockdown, the government also had to think about what would happen to people who suddenly couldn’t make their regular loan payments.

Just three days after the lockdown announcement, the RBI allowed banks and other lenders to give borrowers a three-month moratorium on loan installments due.

Basically, you could delay your EMIs.

It was not waived off. You still had to pay it later, and interest continued to build.

But the important part was that delaying those payments would not be treated like a normal loan default or hurt your credit score.

And this wasn’t only a problem for daily-wage workers or small-time traders. Around 1.78 crore salaried workers were also estimated to have lost their jobs in April 2020.

Now think about salaried workers who had been investing part of their salary every month through an SIP.

SIP (Systematic Investment Plan) is simply a way of investing a fixed amount regularly into a mutual fund. Maybe it is Rs 5,000 a month. Maybe Rs 10,000.

If you were investing Rs 10,000 every month through an SIP in a situation like 2020, it would have been quite natural to think to just stop the SIP for a moment.

That Rs 10,000 could stay as liquid cash in the bank for a medical emergency, a problem with your salary or some other unexpected expense; you had a little more cash available.

You could always restart the SIP once things became normal again, right?

This pattern could be noticed in the SIP data around the lockdown period.

According to AMFI data, monthly SIP contributions fell from Rs 8,641 crore in March 2020 to Rs 8,123 crore in May.

Of course, we cannot say that this entire fall happened because people were struggling financially. SIP contributions can change for many reasons.

But the bigger point is that 2020 gave people a very real reason to think about stopping.

And Covid was obviously an extreme example. Life can give you much smaller reasons to miss an SIP too.

Maybe your salary comes late. There is a wedding in the family. You change jobs. The car needs repairs. Or maybe you invest manually every month and simply forget once in a while.

Sometimes there isn’t even a financial emergency. The market falls, your portfolio is in red, and you think, ‘Maybe I’ll just wait for a couple of months before putting more money in.’

And that got us wondering about something. What actually happens if you miss your SIP?

This question probably matters to more people today than it did in 2020.

Why?

Remember that Rs 8,123 crore monthly SIP number from May 2020?

By August 2026, monthly SIP contributions had increased to Rs 32,297 crore (almost 4X)

India now has around 10.75 crore SIP accounts, and more than 10 crore of them invested in August 2026 itself.

That is a lot of people investing every month. And obviously, not everyone will manage to do it perfectly every single time.

So we wanted to know what happens if you miss some of your SIPs?

That is what we decided to test.

The Study

We wanted the result to depend on the investor, not on whether a fund manager happened to make good or bad calls.

So we used the UTI Nifty 50 Index Fund (Direct Plan, Growth). It is the oldest Nifty 50 index fund in India and simply tracks the Nifty 50. Since direct plans started in January 2013, our main study runs from January 2013 to September 2026.

Then we created five types of investors.

All of them invest Rs 10,000 on the 1st of every month in the same fund. If the 1st is a holiday, the SIP goes in on the next working day.

What changes is how regularly they invest. One investor never misses an SIP. The next three miss 1, 2 or 3 SIPs every year. The last investor takes one continuous break from investing.

Each small box represents one monthly SIP. A coloured box means the Rs 10,000 was invested, while a red box means that SIP was missed.

In the first four rows, the SIPs are either never missed or missed 1, 2 or 3 times a year. The orange row is different: here, the investor stops investing completely for a while.

We tested pauses of 3, 6, and 12 months and tested what happened when that pause came at different points in the investment journey.

For investors missing 1, 2 and 3 SIPs a year, we did not choose the months ourselves. That could bias the result depending on whether those months were good or bad for investing.

So we let a computer pick the missed months randomly and repeated this 2,000 times for every starting month.

We also did not want the result to depend on someone who happened to start investing in one particular month.

So we tested every possible starting month. That gave us 153 one-year SIPs, 129 three-year SIPs, 105 five-year SIPs, 81 seven-year SIPs and 45 ten-year SIPs.

Finally, we tested what happens to the missed money in two different situations.

In one, the money is never invested at all.

In the other, the investor catches up later. If one monthly SIP is missed, that Rs 10,000 goes in on the next SIP date. If the investor takes a longer pause, all the missed installments are invested together when the SIP restarts.

We also ran one separate test later in the study.

What if the SIP is not missed randomly, but because the market has fallen?

For that, we created an investor who skips the SIP whenever the fund is 10% or more below the highest NAV it had reached till then, and starts again only after it recovers to within 10% of that level.

We wanted this test to include crashes such as 2008, so we used the Regular Growth plan of the same UTI Nifty 50 Index Fund from 2002 onwards, since Direct plans did not exist that far back.

The Results

A missed SIP costs more the longer you stay invested

If you skip one Rs 10,000 SIP, how much less do you have at the end?

To understand this, we looked at one missed instalment and asked: if that Rs 10,000 had been invested, what would it have been worth by the end of the SIP?

The dark part of each bar is the original Rs 10,000. The lighter part is the growth that money could have earned.

Over 1 year, a missed Rs 10,000 was worth about Rs 10,741 by the end. Over 5 years, it was worth Rs 14,054. And over 10 years, Rs 20,474.

So over longer periods, you do not just miss the Rs 10,000 itself. You also miss the growth that money could have earned over the years. In a 10-year SIP, that lost growth was actually slightly more than the original Rs 10,000.

But shorter periods were more unpredictable.

We tested 153 different one-year SIP periods. For example, one starting in January 2013, another in February 2013, another in March 2013, and so on.

In 28 of those 153 periods, the Rs 10,000 you skipped would actually have fallen in value if you had invested it. In other words, that Rs 10,000 investment would have ended the year worth less than Rs 10,000.

This happened in periods like 2015-16, 2019-20 and 2024-25, when the market ended lower.

Over 3 and 5 years, this happened only a few times.

And over 7 and 10 years, it never happened. Not once.

Now take a 10-year SIP from September 2016 to September 2026.

Someone who never missed an SIP invested Rs 12 lakh and ended with about Rs 21.55 lakh.

Someone who missed 2 random SIPs every year invested Rs 2 lakh less but ended up about Rs 3.59 lakh lower on average.

So Rs 2 lakh of that gap was simply the money that never got invested. The remaining Rs 1.59 lakh was the growth those missed investments could have earned.

So there is no separate penalty for missing an SIP. The gap is simply the money you did not invest, plus whatever that money could have grown into over time.

Your corpus gets smaller. Your return can still look almost the same

Next, we looked at what happens when you keep missing SIPs.

First, a quick distinction.

Your corpus is simply the total value of your investment at the end — the money you invested plus whatever returns it earned.

So if you normally invest Rs 10,000 a month, making all 12 SIPs means putting in Rs 1.2 lakh a year.

Miss 1 SIP, and you invest about 8.3% less money. Miss 2, and you invest about 16.7% less. Miss 3, and you invest 25% less.

Interestingly, the final corpus fell by almost the same percentages.

If you missed 1 out of every 12 SIPs, your final corpus was about 8.3% smaller. Miss 2 out of 12, and it was about 16.7% smaller. Miss 3, and it was about 25% smaller. This stayed roughly the same across 1-, 3-, 5-, 7- and 10-year SIPs.

Why?

Because every SIP is a separate investment. If one Rs 10,000 SIP never happens, it does not affect the money you already invested in the other months.

And since we picked the missed months randomly, the months you skipped were, on average, not very different from the months you invested in.

So if roughly one-sixth of your money never goes in, you also end up with roughly one-sixth less wealth.

But here is the surprising part.

Your return can still look almost the same.

That is where XIRR becomes important.

XIRR tells you the annualised return earned by the money that actually got invested. So even if you skipped a few SIPs, the money you did invest was still in the same fund and earning its returns.

Over 10 years, someone who missed 2 SIPs every year saw their XIRR change by only around -0.07 to +0.07 percentage points in the middle 80% of cases.

So you could open your app and see a perfectly healthy XIRR, while still having a much smaller corpus.

XIRR tells you how well the money you invested performed. Your corpus tells you how much money you actually ended up with.

Missing an SIP is different from investing it late

So far, when we said an SIP was missed, we assumed that money was never invested.

But that is not always what happens.

Say you were only late on your SIP, and you kept the Rs 10,000 aside and invested Rs 20,000 in April instead.

Both people missed their March SIP. But in one case, the money never entered the investment. In the other, it entered just one month late.

So we tested both.

As you can see, the difference between the red and green bars is huge.

The red bars show what happens when the missed money is never invested. If you miss 1 SIP every year, the 10-year corpus is about 8.3% smaller. Miss 2 every year and it is about 16.7% smaller. Miss 3 and the gap reaches 25%.

The green bars show the same missed SIPs, but this time the money is invested on the next SIP date.

Now the gaps become tiny.

Missing 1 SIP every year and catching up the next month left the corpus only about 0.08% smaller. For 2 missed SIPs, it was 0.18%, and for 3, about 0.30%.

We saw the same thing with longer pauses.

Say you stop a Rs 10,000 SIP for six months. That means Rs 60,000 does not go in during those months.

If that Rs 60,000 is never invested, the 10-year corpus ends up about 5% smaller.

But if you keep the Rs 60,000 aside and invest it in one go when the SIP restarts, the gap is only about 0.17% on average.

Even after a full 12-month pause, never investing the missed money left the corpus about 9.9% smaller. Catching up once the SIP restarted reduced that gap to about 0.63%.

The full January 2013 to September 2026 journey makes this even easier to see.

Someone who invested Rs 10,000 every month put in Rs 16.5 lakh and ended with Rs 35.97 lakh.

Someone who missed 2 random SIPs every year (in a typical random draw) and never invested that money ended with Rs 29.93 lakh.

But someone who missed the exact same SIPs and invested the money the following month ended with Rs 35.82 lakh — only around Rs 14,194 behind the person who never missed.

So when someone says, “I missed my SIP,” there is an important follow-up question:

Did the money never get invested, or did it just get invested late?

Because as the chart shows, those two situations can end very differently.

Not every missed SIP costs the same

There is one more thing that matters: when you miss it.

A Rs 10,000 SIP made early in a long investing journey gets many more years to grow. The same Rs 10,000 invested near the end gets much less time.

To see this clearly, we looked at every Rs 10,000 monthly SIP from January 2013 to September 2026 and checked what each one was worth on 30 September 2026.

In the chart, every bar is one Rs 10,000 SIP. The older SIPs are on the left and the newer ones are on the right. The taller bars on the left are mostly the earlier investments, simply because they had more years to grow.

For example, the Rs 10,000 invested in September 2013 was worth about Rs 45,903 by September 2026.

Now compare that with the Rs 10,000 invested in April 2020, just after the Covid crash. That was worth about Rs 29,153 by September 2026.

The April 2020 SIP was bought after a big market fall, so it got a good entry point. But the September 2013 SIP still ended up worth more because it had almost seven extra years to grow.

There is one more thing to notice in the chart.

The red bars near the right are the most recent SIPs. On 30 September 2026, the fund was about 13% below its January 2026 highest point. Because of that, 26 of the last 28 SIPs were worth less than the original Rs 10,000 at that point.

That does not mean those were bad SIPs. They were simply recent investments being measured at a time when the market was lower.

So the cost of missing an SIP depends on two things: the price at which that money would have gone in, and how much time it would have had to grow.

Over longer periods, that second part (time) starts to matter a lot more.

We saw this even more clearly when we looked at investors who stopped their SIP completely for a while.

Suppose you invest Rs 10,000 every month but have to take a six-month break during a 10-year SIP. That means six SIPs, or Rs 60,000 in total, are missed.

Now imagine two people take exactly the same six-month break. The only difference is that one pauses in the first year and the other pauses in the last year.

As you can see in the chart, the later the pause happens, the lower the cost tends to be.

For a pause that began in year 1, every Rs 10,000 skipped would have been worth about Rs 31,558 by the end of the 10-year SIP. If the same pause began in year 5, that fell to about Rs 21,140. And if it happened in year 10, each missed Rs 10,000 would have been worth only about Rs 10,577.

So both investors missed the same six SIPs and the same Rs 60,000. What changed was how much time that money would have had to grow.

A SIP missed in year 1 loses almost the entire 10-year journey. One missed in year 10 has hardly any time left.

That is why a six-month break near the beginning ended up costing roughly three times as much as the same break near the end.

This also explains why a few SIPs missed here and there can behave differently from one continuous break.

With random misses spread across 10 years, some happen early and some late, so they partly balance each other out. With a six-month pause, all six misses happen together, so where that block falls in the journey matters much more.

What if you stopped your SIP because the market had fallen?

So far, all the missed SIPs in our study were random.

But sometimes investors stop for a very specific reason.

The market falls.

The portfolio turns red, and putting another Rs 10,000 into it can feel uncomfortable. You might think it is better to wait until things look a little safer and then start again.

So we created an investor who did exactly that.

On every SIP date, if the fund was 10% or more below the highest point it had reached till then, the investor skipped the SIP. They started investing again only once the fund recovered to within 10% of that highest point.

For this part, we also wanted to include the 2008 crash. Direct plans did not exist that far back, so we used the Regular Growth plan of the same UTI Nifty 50 Index Fund from 2002 onwards. The Regular plan charges a little more, but everyone in this comparison uses the same plan, so we are still comparing the behaviour in the same fund.

In the chart, the green dots are months when the SIP went in and the red dots are months when the investor stayed out.

And the first thing you notice is that this approach does not make you miss only a few bad months.

Out of 297 SIP dates between January 2002 and September 2026, the investor skipped 111.

After the 2008 crash alone, the investor stayed out for 57 of the next 77 months. They also skipped much of 2002 and 2003 because the fund was still more than 10% below the highest point it had reached in 2000.

That is the problem with waiting for the market to look safe again. You do not know when the bottom has arrived. By the time the recovery becomes obvious enough to start investing again, part of that recovery may already have happened.

So we compared these market-fall misses with the random misses from earlier.

The blue bars show a random missed SIP. The red bars show an SIP deliberately skipped because the fund was at least 10% below its previous highest point.

Over a 10-year SIP, every Rs 10,000 skipped because of a market fall would have been worth about Rs 21,714 by the end. For a random missed SIP, it was about Rs 18,814.

Over 20 years, the difference became even larger: about Rs 54,664 for a market-fall skip versus Rs 41,492 for a random one.

We saw the same pattern in the main Direct-plan study too. In every 7-year and 10-year SIP we tested, skipping because the market had fallen cost more than skipping at random.

There was another important difference.

Earlier, we saw that if a random SIP was missed but the money was invested the following month, most of the gap disappeared. Over 10 years, the corpus was only about 0.18% smaller.

But when the investor waited for the market to recover, they could stay out for several months. Even if all the skipped money was kept aside and invested as soon as the market came back within 10% of its previous high, the final 10-year corpus was still about 1.5% lower on average.

The full 2002–2026 journey makes this easier to see.

Someone who simply kept investing put in Rs 29.7 lakh and ended with about Rs 1.69 crore.

The investor who stopped during market falls skipped 111 SIPs and spent that money instead. So they invested only Rs 18.6 lakh and ended with about Rs 69.37 lakh.

But the more useful comparison is the investor who did not spend the skipped money. They kept every rupee aside and invested it once the market had recovered enough for them to restart.

That investor also put in the full Rs 29.7 lakh, just like the person who never stopped. But they ended with about Rs 1.47 crore (roughly Rs 22 lakh less).

So here, both investors eventually invested the same amount.

The difference was when the money entered the market.

So, what does this tell us?

Missing an SIP is not automatically a big problem.

Sometimes life gets in the way and the money genuinely needs to be used somewhere else. What matters more is what happens after that.

If the money eventually gets invested, even a little late, that can be very different from never investing it at all.

When you miss also matters. An SIP missed early has much more time that it could have spent growing, while one missed near the end has far less.

And there is an important difference between having to miss an SIP and choosing to stop because the market has fallen. Waiting for things to look safer can keep you out for much longer than expected, including during periods when prices are lower.

So there is no single answer to “How bad is missing an SIP?”

It depends on whether the money eventually gets invested, when in the journey the miss happens, and why you stopped in the first place.

A few missed months because life got in the way are very different from money that never gets invested at all. And they are also very different from deliberately waiting for the market to look safe again.

So maybe the useful way to think about a missed SIP is not whether you broke your perfect SIP streak.

The better question is what happened to the money you were supposed to invest.

Did it get invested later? Did it never get invested at all? Or did you keep it out because you were waiting for a better time?

Those choices can matter much more than simply missing the SIP date itself.

Note:

• The study uses UTI Nifty 50 Index Fund. The main study uses the Direct Plan (Growth) from 2013–2026; the market-fall test uses the Regular Plan (Growth) from 2002–2026.

• Each SIP is Rs 10,000 monthly on the 1st, or the next working day.

• Missed months were chosen randomly and tested 2,000 times for every starting month. Results shown are averages.

• Many investment periods overlap, so they are not completely independent experiences.

• The market-fall rule skips SIPs when the fund is 10% or more below its previous highest NAV.

• Results depend on the period chosen. The fund was about 13% below its January 2026 high on 30 September 2026.

• Stamp duty, exit loads and taxes are not included.

• A few errors in early Regular-plan NAV data were corrected using the fund’s IDCW-option data.
  
  

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

That was the name of America’s first modern open-ended mutual fund.

Investors could put money into the fund and, whenever they wanted to leave, redeem their units directly with the fund.

That basic structure eventually became the model for modern mutual funds.

But there is an interesting story behind the word “Trust” in its name.

It actually has its roots in real estate.

Back then, some investors in Massachusetts wanted to pool their money and buy properties together.

But there was a problem.

Companies could not simply buy buildings just to rent them out. They were generally allowed to own property only if they needed it for their own business, like an office or factory.

So some Boston lawyers and investors found a loophole.

Instead of forming a company, they used an older legal structure called a trust.

A few people, called trustees, would legally hold and manage the properties. The people who put in money would get certificates giving them a share of the income and value of those properties.

Because a trust was not technically a company, the restriction on companies did not apply.

So even if one person could not afford a large property, many people could pool their money and invest together.

By the late 1800s, these real estate trusts had become fairly common.

They also had a tax advantage. From 1913 to 1935, they were generally not taxed like normal companies.

But in 1935, the US Supreme Court changed how these trusts were taxed.

The trust could now be taxed on the income it earned. And when that money was passed on to investors, they could be taxed again.

So the same income could effectively be taxed twice.

A year later, the rules changed for funds that invested in stocks, so they no longer had to deal with this extra layer of tax.

Real estate trusts were left out.

That changed in 1960.

At the time, the US Congress, basically the American version of Parliament, was passing a law called the Cigar Excise Tax Extension Act.

It was mainly about taxes on cigars, but lawmakers can also add other provisions to a bill that is already being passed.

One of those provisions was about real estate trusts.

Until then, these structures went by names such as Massachusetts trusts, business trusts and common-law trusts.

The 1960 law gave qualifying trusts a common legal identity: Real Estate Investment Trusts, or REITs.

So what exactly is a REIT?

Understanding mutual funds will help you understand REITs.

A mutual fund pools money from thousands of investors and uses it to buy stocks.

A REIT does something similar, except it uses that money to own real estate, such as office buildings, malls and other large properties.

But there are two important differences.

First, a REIT does more than just hold properties. It also runs them and signs leases, collects rent and maintains the buildings.

So in that sense, it is closer to a landlord business than just a portfolio of investments.

The second difference is how the money comes back to investors.

In a mutual fund, dividends and gains usually stay inside the fund and show up in the NAV. You generally get the money when you sell your units, unless you have chosen a payout option.

A REIT is different.

In India, REITs are required to distribute at least 90% of their net distributable cash flow to investors.

Most listed REITs make these payouts every quarter. India introduced REIT regulations in 2014.

Now, if you want to invest in real estate through the stock market, you now have two options.

A developer company that mainly makes money by building and selling properties or a REIT that mainly owns completed properties, rents them out, and earns regular income from them.

Both give you exposure to real estate, but they work very differently.

Do REITs and real estate developer stocks, also called realty stocks, actually behave the same way? And which factors matter more for their returns?

That is what we decided to test.

Results

Did real estate stocks follow house prices?

We started with the most obvious question: if house prices rise or fall, do REITs and real estate developer stocks move in the same direction?

We compared them from March 2021 to March 2025, starting after Brookfield was listed, which gave us 16 quarters.

For all six investments, along with the Nifty 50 and Nifty Realty, we used quarter-end closing prices and compared them with the RBI All-India House Price Index, or HPI.

For every quarter, we checked whether house prices went up or down and whether each REIT or developer/realty stock moved in the same direction.

If these investments closely followed house prices, they should have matched them most of the time.

They did not.

As the chart shows, none of the six REITs or developer/realty stocks matched house prices more than 50% of the time.

The relationship looked a little better when house prices were rising, with match rates of around 42% to 58%.

But in the four quarters when house prices fell, never by more than 1.1%, the relationship became much weaker.

Brookfield and Nifty Realty matched in 2 of the 4 quarters, Embassy, DLF and Prestige matched in just 1, while Mindspace, Oberoi Realty and the Nifty 50 did not match in any of them.

The size of the moves told a similar story.

Over the four years, house prices rose around 14% and REIT prices rose around 12% to 30%, but developer/realty stocks rose far more and even outpaced the Nifty 50’s roughly 60% gain.

So why weren’t these investments moving with house prices?

One reason could simply be timing.

The RBI’s house price index is based on property registrations. A property deal may have been agreed earlier, but it can show up in the data only when the registration happens.

Stock prices do not have that delay.

They can react immediately to earnings, new projects, policy changes or even a change in overall market sentiment.

That is why we also compared the total move over the full four-year period, where this timing gap matters less.

But timing is only part of the story.

A listed real estate stock can move for many reasons that have little to do with house prices.

A developer can launch a new project, report better profits, reduce debt or simply rise because the broader stock market is doing well.

Property prices also usually move much more slowly. If a homeowner does not like the price being offered, they can simply wait. A listed stock cannot do that. Its price changes every day as buyers and sellers react to new information.

And there is another important difference when we look at REITs.

The RBI HPI tracks residential house prices. The REITs in our study mainly own office properties. So there is no reason to expect both to move in exactly the same way.

Put all of this together, and the result makes more sense.

Rising house prices alone were not enough to explain how these investments moved. Other factors were clearly playing a role.

That brings us to REITs. REITs earn rental income from office properties and regularly distribute a large part of that cash to investors.

So investors can compare the income from a REIT with what they can earn from safer investments like government bonds.

If interest rates rise, those safer investments can become more attractive. If rates fall, REIT income can start looking more attractive again.

So if house prices were not explaining REIT prices very well, the next question was quite natural:

Did interest rates have a bigger role in moving REIT prices?

What did they respond to instead?

If house prices were not explaining REIT prices well, the next thing we tested was interest rates. We compared the three REITs and three developer/realty stocks with India’s 10-year government bond yield, or G-sec yield.

This is the return investors earn by lending money to the government for 10 years. Since the government has very low default risk compared with a company, this yield is often treated as a benchmark.

The logic is simple. If government bond yields rise, safer bonds become more attractive. REITs cannot suddenly increase the rent they earn from their buildings. So one way their own yield can become more attractive is if their market price falls. In theory, the opposite should happen when government bond yields fall.

We split the four years into three periods: before the RBI started hiking rates, during the rate hikes and after the hikes ended.

March 2021 to April 2022

Before the RBI started hiking rates, the 10-year G-sec yield rose from 6.18% to 7.14%. If interest rates were the only thing moving REITs, their prices should have fallen.

But they did not.

The average REIT price rose 28%. Brookfield gained 48.7%, while Embassy rose 18.3%.

Embassy’s own yield actually fell from 7% to 5.74%. So investors were willing to pay more for the same income even though government bonds were also offering higher yields.

One possible reason was offices reopening after Covid. During the pandemic, there was a fear that work from home could permanently reduce demand for offices. As companies brought employees back, that fear reduced and investors may have become more positive about office REITs.

Developer/realty stocks also rallied strongly. Prestige and Oberoi rose 55.3% and 67%, while the three developers gained around 50.6% on average. The broader post-Covid stock-market rally and company-specific factors could also have helped.

So this period showed us that interest rates were clearly not the only thing moving REIT prices.

April 2022 to February 2023

This is where the relationship became much clearer. The RBI raised the repo rate from 4% to 6.5%. The 10-year G-sec yield stayed around 7.1% to 7.5%, much higher than the roughly 6% levels seen in early 2021.

All three REITs fell between 16% and 21%, with the average down almost 19%. Developer/realty stocks also fell, but by much less. The Nifty 50 actually rose 1.2%.

What makes this interesting is that the REITs were still paying almost the same amount of cash.

Embassy continued paying around Rs 5.26 to Rs 5.46 per unit every quarter, while Mindspace and Brookfield also had fairly steady payouts. The buildings were still earning rent.

What had changed was the return available elsewhere. Government bonds were now offering higher yields.

Since REITs could not suddenly raise rents, their prices fell. That pushed their own yields higher and made them more competitive with bonds.

Embassy’s yield rose from 5.74% to 7%, while Mindspace’s yield rose from 5.35% to 6.47%. This is similar to how bonds behave: when market yields rise, prices tend to fall. During this period, REITs behaved a lot like fixed-income investments.

Higher interest rates can hurt developers too because home loans and borrowing become more expensive. Developer/realty stocks did fall, but less than REITs. Strong sales, new projects and other company-specific factors may have helped cushion the fall.

February 2023 to March 2025

Then the direction changed again. The 10-year G-sec yield fell from 7.46% to 6.58%. The average REIT price rose 18.9%.

By March 2025, REIT yields had fallen to around 5.4% to 6.5%, compared with a G-sec yield of 6.58%.

Developer/realty stocks, however, rallied much more. They rose 125.4% on average. DLF and Oberoi nearly doubled, while Prestige nearly tripled.

One possible reason was that lower interest rates made equities more attractive and supported borrowing and economic activity. The broader stock market was also doing well, while earnings, valuations and company-specific factors could have added to the rally.

So what did we learn?

Interest rates did matter for REITs.

During the rate-hike period, they behaved a lot like fixed-income investments.

But the first period showed that the relationship was not perfect. Office demand, rents and leases, new property acquisitions, taxes and other business factors could still move REIT prices.

Developer/realty stocks behaved differently. They moved in much bigger swings and appeared to respond more to the broader stock market and company growth. They also fell when rates rose, but less than REITs.

And that led us to the next question.

So far, we have only compared prices. But REIT investors also receive regular cash payouts. What did investors actually earn after including those payouts?

What did you actually earn?

So far, developer/realty stocks looked much better than REITs. But comparing only their price returns is not completely fair.

Developers can keep most of their profits and use that money to buy land or build new projects. REITs work differently because they have to distribute a large part of the cash they generate to investors.

So for a developer, more of the return can show up in the share price. For a REIT, a bigger part can come as regular cash payouts. That means looking only at price can make REITs look worse than they actually performed.

Comparing total returns

To make the comparison fair, we calculated the total return of all six investments from March 2021 to March 2025. We used their actual, unadjusted market prices and added every dividend or REIT distribution ourselves.

For the Nifty 50, we used the Nifty 50 TRI, which already includes dividends. We also assumed that every payout was immediately reinvested into the same investment.

And this made a big difference for REITs.

Embassy’s price rose only 12.3%, but it also paid Rs 87.73 per unit over the four years. Once those payouts were reinvested, its total return became 44.3%.

Brookfield’s price rose 29.8%, but its total return was 68.4%, almost the same as the Nifty 50 TRI. Mindspace’s total return was around 60%.

Developer/realty stocks still did better. DLF’s price rose 137.1%, and after including dividends, its total return became 143.7%.

The difference was much smaller because developers do not have to regularly distribute cash the way REITs do. So even after including payouts, developer/realty stocks still outperformed the REITs in our study, but the gap became smaller.

For example, DLF’s lead over Embassy was around 125 percentage points when we looked only at prices. On total returns, that gap fell to around 99 percentage points.

So the takeaway is simple: for REITs, price return alone does not show the full picture. The cash payouts are an important part of what investors actually earn.

Conclusion

Even though REITs and realty stocks both come under real estate, investing in them is very different.

When you buy a realty stock, you are investing in a company just like you would with any other stock. Its share price can move because of its sales, profits, new projects, debt, future growth and what is happening in the broader stock market.

The company can also keep most of the money it earns and use it to buy more land or build more projects. If investors believe this will lead to higher profits in the future, the stock price can rise.

A REIT works differently. It owns properties that earn rent and distributes a large part of the cash it generates to investors. So a bigger part of an investor’s return may come through regular payouts, rather than only through a rise in the REIT’s price.

And that is exactly what we saw in our study.

Housing prices alone did not explain the movement of either REITs or realty stocks. Realty stocks showed much bigger swings and appeared to react more like regular stocks, with company performance and the broader market playing an important role.

REITs behaved differently. At times, especially when interest rates were rising, they behaved more like fixed-income investments. But office demand, rents and other business factors mattered too.

We also saw why comparing only prices can be misleading. Once the cash payouts were included, the gap between REIT and developer-stock returns became smaller.

So the two may both give you exposure to real estate, but they give it to you in very different ways. A realty stock is an investment in a business that builds and sells property. A REIT is largely an investment in properties that earn rent and distribute cash.

And our study shows that neither can be understood by looking at housing prices alone.

Note

• The study covers only March 2021 to March 2025 and one major rate-hike cycle.

• The sample is small: 3 REITs and 3 developer/realty stocks.

• Study 1 has only 16 quarters, and house prices fell in just 4 of them.

• The RBI HPI tracks residential property prices, while the REITs mainly own offices.

• Study 2 depends on the chosen interest-rate periods. Different dates could change the exact numbers.

• Mindspace and Brookfield yield data starts later because four quarters of payout data were needed.

• Total returns assume every payout was reinvested at the ex-date closing price.

• Taxes and transaction costs are not included.

• Data came from ACE Equity, NSE and Investing.com, so figures may differ slightly from official sources.

• The study shows patterns, not causes. Other factors could also have moved prices.

• Past performance does not guarantee future results.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

When the Titanic left Southampton, it was carrying more than 2,000 people and had 20 lifeboats on board.

That sounds low for such a huge ship. But Titanic was actually following, and even exceeding, the British lifeboat rules that existed at the time.

The problem was that those rules had been written years earlier, when ships were much smaller. Once a ship crossed a certain size, the number of lifeboats required did not keep increasing in proportion to how much bigger the ship became.

Titanic was far bigger than the ships those rules had originally been designed around.

So the number was not random. It came from a rule that had once been set for a reason.

The problem was that the ship had changed a lot, while the reference being used to judge had not changed with it.

And that is the interesting thing about old reference points.

Once a number becomes important, we tend to keep coming back to it even much later, sometimes after everything around that number has changed.

The same habit of holding on to an old number can show up with IPOs.

Imagine a company comes out with an IPO at an issue price of Rs 100.

After listing, the stock moves up. Say it reaches Rs 140 and, over the next few months, goes on to Rs 200.

Maybe you did not get shares in the IPO. Or maybe you simply did not buy the stock after it was listed.

Then, a few months or years later, you notice that the same stock has fallen back to Rs 103.

Now Rs 103 can feel different from just another market price.

You remember that the IPO itself came at Rs 100. You also remember that the stock had traded much higher after that.

So it is easy to wonder:

“If this company originally came to the market at Rs 100, am I now getting another chance to buy it around that same price?”

That is the idea we wanted to test.

So we asked a simple question:

If an IPO first went up meaningfully and then later came back near its issue price, what happened if you bought it there?

Was it really a second chance?

The Study

To test this, we started with 492 mainboard IPOs whose issues opened between 2015 and 2025. We had a usable price history for 480 of them, using data available up to 17 September 2026.

But we did not want to count stocks that simply stayed around their issue price.

So first, the stock had to close at least 20% above its issue price. If the IPO came at Rs 100, the stock had to first close at Rs 120 or more. That could happen on the listing day itself or sometime later.

Only after that did we look for the first time the stock came back near its issue price.

For our main study, “near” meant within 5% above or below the issue price. So for a Rs 100 IPO, we looked for the first close between Rs 95 and Rs 105.

We then treated that as the entry point and checked what happened after 1 month, 3 months, 6 months, 12 months and 24 months.

And if a stock came back near its issue price more than once, we only used the first revisit.

So if a stock went from Rs 100 to Rs 160, came back to Rs 103, went up again and later fell to Rs 98, we used Rs 103.

That way, we were looking at the first real second chance an investor would have seen, not picking a better price later with hindsight.

We also ran the same study using the next trading day’s opening price as the entry, and repeated it with a wider 10% band around the issue price.

How often did this happen?

Of the 480 companies in the study, 401 closed at least 20% above their issue price at some point after listing.

We used that 20% rise as the first filter because we did not want to include stocks that simply stayed around their IPO price from the beginning. We wanted stocks that had first moved clearly above the issue price and then later came back near it.

Out of those 401 stocks, 270 later returned within 5% of their issue price.

That works out to roughly 67%, or about two out of every three stocks that had first risen at least 20%.

So the situation we are studying was actually quite common.

The other 131 stocks did not return to this 5% range during the period we followed them. This does not mean they will never return. Some of the newer IPOs have simply had much less time than companies that listed several years ago.

The time taken to come back also varied quite a bit.

In 21 cases, the stock returned close to its issue price within 30 days of listing.

So this was not only something that happened immediately after listing. In many cases, the stock stayed away from the issue price for months or even years before eventually coming back.

What is interesting here is simply how often the original issue price came back into the picture. Even after a stock had first moved at least 20% higher, roughly two-thirds of these companies later traded close to that starting price again.

Our data does not tell us why each stock returned there. The reason could be different for every company. But it does show that returning near the issue price after an earlier rise was common enough to study separately.

The bigger question is what happened after investors got that second chance near the IPO price.

How much had they already fallen?

The next question was how far these stocks had already fallen before coming back near their issue price.

For each of the 270 stocks, we took the highest closing price it had reached before the revisit and compared it with the price on the day it came back into the 5% range.

Each bar = number of stocks (out of 270), grouped by how far the price had fallen from its earlier peak by the time it came back near the issue price.

So 159 stocks had already fallen at least 30% from their earlier high, and 59 had already fallen by at least half.

That is important because two stocks could both be back near their issue price and still have had very different journeys.

One may have fallen only a little from its earlier high. Another may have already lost half its value.

So being “back near the IPO price” did not tell us how much the stock had already fallen.

But that still leaves the more important question.

Once it came back near the issue price, was it actually a good place to buy?

What happened a year after buying?

For 209 of the 270 stocks, we had enough data to follow them for a full year after the first revisit.

Of these, 90 were higher and 119 were lower than their starting price one year later.

So 43.1% went up and 56.9% went down.

The outcomes were very different across stocks.

32 stocks gained at least 50% over the year.

At the other end, 70 stocks lost at least 20%, including nine that lost at least half their value.

So buying near the issue price did not always go badly. Some stocks did very well.

But the main result is that more stocks finished the year lower than higher.

How much further did prices fall along the way?

The one-year return still does not show the full experience.

A stock can finish the year with a gain and still fall sharply before recovering.

So we also checked the daily closing prices during that one-year period to see how far each stock fell below the buying price.

These groups overlap. So a stock that fell 50% is also included in the 20% and 30% groups.

Of the 209 stocks, 123 fell at least another 20% below the buying price during the year. 90 fell at least 30%, while 22 fell by at least half.

Even among the 90 stocks that ended the year higher, 25 had first fallen at least 20% below our buying price.

Quess Corp is one example.

It eventually ended the year 123.9% higher.

But before that happened, the stock had at one point closed 45.5% below the price at which our study entered.

So even a stock that later became a big winner could make you sit through a very large fall first.

This is important because looking only at the one-year return can hide what happened in between.

The stock may have already fallen a lot before you bought it. That did not mean the downside was over.

How different could individual results be?

The overall numbers tell us how often stocks went up or down. But looking at individual stocks shows how different the actual experience could be.

Zomato

Zomato’s issue price was Rs 76. Before coming back near that price, the stock had closed as high as Rs 160.30.

On 22 February 2022, it closed at Rs 79.70, which meant the stock was already down about 50% from its earlier high.

But the fall did not stop there.

A year later, Zomato closed at Rs 52.95. Someone buying at Rs 79.70 would have been down 33.6% after one year, and during that year the stock had at one point closed 47.7% below the buying price.

So just because a stock had already fallen a lot did not mean most of the downside was already over.

IRFC

Now look at IRFC.

Its issue price was Rs 26. Before coming back near that level, the stock had reached an earlier closing high of Rs 35.65.

On 27 February 2023, it closed at Rs 27.10. A year later, it was at Rs 151.60, which is a 459.4% gain from the starting price.

During that year, its largest further fall below the buying price was only 5.9%.

What is interesting here is that IRFC did not just recover its old high. Going from Rs 27.10 back to Rs 35.65 would have been a gain of around 31.5%, but the stock went much further than that.

Taken together, Zomato and IRFC show why the issue-price setup alone was not enough to tell you what would happen next.

Both stocks had first moved higher and later came back near their issue price, but one kept falling while the other went on to rise several times over.

So while the rule helped us identify the same kind of starting point, it did not tell us which stock would become a winner and which one would become a loser.

Did a bigger earlier rise improve the outcome?

We also wanted to check whether a bigger earlier rally made any difference.

So we grouped the 209 stocks based on how far they had risen above their issue price before coming back near it.

The pattern was not very clear.

Stocks that had risen more earlier did not consistently have better one-year outcomes after returning near the issue price.

The group that had risen the most did show a higher share of positive outcomes, but it had only 14 companies, so it is too small to draw a strong conclusion from.

We also checked whether the listing gain made a difference.

Among stocks that had listed at least 20% above their issue price, 33 out of 80 were higher one year after the revisit.

For stocks that had listed at a smaller premium, it was 34 out of 78.

And for stocks that had listed at or below their issue price, it was 23 out of 51.

So a bigger listing gain or a bigger rally before the fall did not clearly improve the chances of a positive one-year outcome.

In simple terms, the fact that a stock had once gone much higher did not make the later return near issue price more reliable.

We also checked whether changing the entry rule made much difference.

If we bought at the next trading day’s opening price, 91 of 209 stocks were higher after one year, or 43.5%. In our main test, it was 43.1%.

We also widened the issue-price range from 5% to 10%. Here, 104 of 231 stocks, or 45.0%, were higher after one year.

So the numbers changed slightly.

But the overall result did not.

Even after changing the entry price or widening the range, fewer than half the stocks were higher after one year.

Did holding for longer help?

We also checked what happened over different holding periods.

At first, it may look like waiting two years helped, because the share of positive outcomes rises from 43.1% after one year to 51.0% after two years.

But these are not the same group of stocks.

Only 145 stocks had enough data for a full two-year comparison, while the one-year group had 209.

So we also looked only at those same 145 stocks.

Among them, 72 were higher after one year and 74 were higher after two years.

That means the positive share moved only from 49.7% to 51.0%.

So for this same group of companies, waiting another year did not suddenly turn the outcome clearly positive. It remained close to half and half.

Among these 145 stocks, 74 also reached their earlier closing high at some point during those two years, although they did not necessarily stay there.

So, what does this tell us?

The issue price is the price at which shares were offered to investors in the IPO. At that time, investors were looking at the company’s financials, growth expectations and market conditions as they existed then.

By the time the stock comes back near that price, months or even years may have passed. The company’s earnings, growth outlook and the valuation investors are willing to pay could all be very different.

Our study does not tell us which of these things changed for each company. We did not study profits, debt, growth or valuation. We only looked at what happened to the share price after this setup appeared.

And the results were mixed.

Some stocks went on to become large winners. Others kept falling. A bigger earlier rally did not show a clear, consistent advantage, and changing the entry rule or widening the price range still left fewer than half the stocks higher after one year.

So the issue price can still be a useful reference because it tells you the price at which the shares were originally offered in the IPO.

But it does not automatically tell you whether the stock is cheap when it comes back there.

The issue price tells you what investors paid in the IPO. It does not, on its own, tell you what the business is worth today.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

That is the Apple phone unveiled last week, its first foldable iPhone.

And like most new Apple launches, one of the first things people started looking at was the price.

Let’s not get into whether the phone is overpriced or underpriced. There is something more interesting we can learn from how differently the same phone is priced across countries.

In India, the 256GB version starts at around Rs 3 lakh. But the exact same phone costs much less on the other side of the world.

In the UAE, for example, Apple lists the 256GB version at AED 8,499, which is around Rs 2.22 lakh at the current exchange rate.

That is almost a Rs 78,000 difference for the same phone.

Now, if you are anything like us, one thought is pretty obvious: why can’t I just buy it there and sell it here?

On paper, that looks like Rs 78,000 sitting there for you to make.

But obviously, it is not that simple.

Once you add customs, taxes, travel costs, currency changes and even the difficulty of selling it at the Indian price, that difference gets smaller.

So seeing a price difference is one thing. Actually being able to capture that difference is another.

There is something similar to this that happens in the stock market.

Take a stock trading at Rs 100 today.

If you buy it normally in the stock market, you pay Rs 100. That is the cash market.

But the same stock can also have a futures price.

Think of the futures price as the price at which traders are agreeing today to buy or sell that stock at a later date.

So while the stock is available for Rs 100 today, its futures price for a later date might be Rs 102.

That means the same stock has two prices at the same time:

• Rs 100 in the cash market

• Rs 102 in the futures market

So for the same stock, there is a Rs 2 price difference between the cash and futures markets.

Trying to earn from a price difference like this is called arbitrage.

And there are mutual funds that mainly look for these kinds of opportunities. They are called arbitrage funds.

That leads to the next question: when are these differences more likely to become attractive?

One possibility is when markets become more uncertain and prices start moving sharply.

And that is where India VIX helps us.

India VIX tells us how much movement the market expects in the Nifty over roughly the next month. A low VIX means the market expects relatively calmer movement. A higher VIX means it expects bigger swings.

So the idea is If VIX rises and markets become more volatile, maybe arbitrage funds get better opportunities to earn.

But does that actually happen?

That is what we decided to find out.

The Study

We started with 40 Direct Growth arbitrage funds, along with India VIX and Nifty 50 data.

Out of these, 39 funds had enough NAV data for at least one complete monthly period.

For every month, we took the first available NAV and compared it with the first available NAV of the next month.

So if we entered a fund in early May, we checked its NAV again in early June.

This gave us 3,417 actual fund returns across 164 monthly entry periods.

We then compared each fund’s return with its return in the previous monthly period.

For example, if a fund returned 0.40% in one period and 0.55% in the next, its return had improved. If it moved from 0.55% to 0.40%, its return had fallen.

Both returns could still be positive. We were simply checking whether the return became higher or lower.

To do this, a fund needed two back-to-back monthly returns. The first usable return for each of the 39 funds had nothing earlier to compare with.

That left us with 3,378 cases across 163 monthly periods, from February 2013 to August 2026.

One case means one fund compared across two back-to-back monthly periods.

We then did the same with VIX. If the VIX reading before the new period was higher than before the previous period, we said VIX rose. If it was lower, we said VIX fell.

Then we asked one simple question:

When VIX rose, did the same arbitrage fund earn more or less than it had in the previous period?

Did the individual funds tell the same story?

We then checked the funds one by one.

There were 77 months when VIX had risen compared with the month before.

Now imagine one of those months had just three arbitrage funds:

• Fund A

• Fund B

• Fund C

For that month, we would ask:

Did Fund A’s return improve compared with the previous month?

Did Fund B’s return improve?

Did Fund C’s return improve?

So that one month gives us 3 results.

If the next time VIX rose there were four funds available, that month would give us 4 more results.

We repeated this for every fund available in every one of the 77 months when VIX rose.

The funds are repeated across months. We are not talking about 1,560 different funds.

There were only 39 usable funds in the entire study.

But because the same funds were checked again every time VIX rose, those 77 months together gave us 1,560 cases.

In simple terms:

One case = one fund compared across two back-to-back monthly periods.

Then we checked whether that fund’s return was higher or lower than it had been in the previous monthly period.

After VIX rose, the fund’s return improved in 552 of 1,560 cases, or 35.4%.

In the other 1,008 cases, the fund earned less than it had in the previous period.

When VIX fell, returns improved in 1,160 out of 1,818 cases, or 63.8%.

So in our data, arbitrage-fund returns were more likely to become lower than higher after VIX rose.

We also checked the funds individually to make sure this result was not being caused by just one or two schemes.

Twenty-seven funds had enough rising-VIX months for us to study them properly.

For all 27 funds, there were more rising-VIX months in which their return became lower than rising-VIX months in which their return became higher.

This does not mean rising VIX caused arbitrage-fund returns to fall. It simply tells us that the pattern was visible across many different funds.

A rising VIX is not the same as a high VIX

There is an important difference between a rising VIX and a high VIX. For example, a move from 12 to 16 means VIX has risen, while a move from 35 to 30 means VIX has fallen, even though 30 is still a much higher level.

Our main test looks at whether VIX rose or fell before the investment started. But we also wanted to see what happened when investors entered at different VIX levels, so we divided the observations into four groups

We used simple VIX ranges of below 15, 15–20, 20–30 and 30 or above. These are only used to make the historical comparison easier to understand; they are not official investment thresholds.

Negative returns were rare across most VIX levels.

When VIX was below 30, only 3 out of 3,341 fund observations were negative.

But when VIX was 30 or above, 3 out of 76 observations were negative.

So even though arbitrage funds were positive in most periods, very high VIX did not guarantee a positive return.

This table only tells us whether the return was positive or negative. It does not tell us how large the return was.

One example from the data

Before the May 2021 investment period, VIX was 23.03. Before the June period, it had fallen to 16.89.

There were 24 funds with returns available for both periods, and all 24 earned more in the June period than in the May period, even though VIX had fallen.

For example, Aditya Birla SL Arbitrage Fund, Direct Growth returned:

So in this example, VIX fell, but the fund’s return increased. One example does not prove a rule, which is why the study looks at every eligible period across all the funds rather than relying on one month.

So what should we take away?

Arbitrage funds try to earn from price differences between the cash and futures markets.

VIX tells us something different. It shows how much volatility the market expects over the next 30 days.

A higher VIX can mean markets are more uncertain, but it does not automatically mean that the cash-futures price gap available to an arbitrage fund will be larger.

In our study, VIX by itself was not a reliable signal for deciding when to enter an arbitrage fund.

When VIX rises, it can seem logical to assume that arbitrage funds will get more opportunities and earn better returns. But our data did not show that consistently.

So waiting for VIX to become high before investing, or moving money into an arbitrage fund just because markets have become more volatile, may not be very useful.

A better way to look at an arbitrage fund is to start with why you are investing in it.

If you are using it to park money for a relatively short period, things like how long you plan to stay invested, the fund’s exit load and the possibility of a small negative return over shorter periods are also important to consider.

Arbitrage-fund returns depend on whether the fund can actually find and capture useful price differences between the cash and futures markets.

VIX only tells us how much volatility the market expects. It does not tell us how large those cash-futures price gaps will be.

So instead of asking:

“Is VIX high enough for me to invest in an arbitrage fund?”

The more useful question is:

“Does an arbitrage fund make sense for the period and purpose for which I want to use this money?”

Note

• A higher VIX does not automatically mean a better arbitrage opportunity. Futures can trade close to cash prices or even below them, and different funds may hold different stocks, futures and debt instruments.

• The study uses monthly returns, so short-lived arbitrage opportunities within a month may not be captured. The monthly holding periods can also differ slightly because of weekends and holidays.

• The fund universe changes over time, and the supplied data may not include every arbitrage fund that closed or merged during the period.

• The VIX ranges used in the study are only for comparison and are not official investment thresholds. The VIX 30+ group is also relatively small, so that result should be read cautiously.

• NAV returns already reflect fund-level expenses, but investor-level costs such as taxes and exit loads are not included.

• These are historical observations and should not be treated as a prediction of what will happen the next time VIX rises.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

And there is a reason for that.

India imports more than 80% of the crude oil it uses. So when oil gets expensive, India has to spend more dollars on imports.

That can put pressure on the rupee, increase fuel and transport costs, push up inflation and raise costs for companies.

But oil shocks usually come with something else too.

Wars, sanctions, supply cuts and recessions can push crude prices higher while also creating fear across global markets.

So when oil shoots up, investors are often dealing with expensive oil and a broader crisis at the same time.

One of the clearest examples is what happened in 2008.

Oil prices had surged due to concerns about the global economy. On 3 July, Brent crude closed at $143.95 a barrel, its highest price ever at the time.

Goldman Sachs was even talking about oil reaching $200.

For India, the worry was obvious. Oil was extremely expensive. The rupee was weakening. Companies were facing higher costs.

And the stock market was already falling.

Nifty had already fallen over 36% from the start of the year. It fell another 20% over the next six months. A big spike in oil prices meant a sharp fall in the stock market.

This pattern has repeated multiple times, both before and after 2008. Many investors are faced with this dilemma when oil prices rally a lot.

The news is alarming, and the markets are falling. The panic can nudge the investors to sell their positions.

In a situation like that, it is easy to feel like things could get worse.

The logic here is that if higher oil prices can hurt companies by raising costs, and if profits come under pressure, stocks could fall further.

So selling your portfolio and waiting for things to calm down can feel like the safer move.

But does panic-selling during an oil shock actually end up doing you good?

Or do you end up selling after the fall has already happened and then miss the recovery?

We decided to test this belief. First, does a massive oil shock really mean your portfolio is doomed over the long run? And second, did the belief on the most vulnerable sector and the safer sectors hold?

The Study

We looked at 25 years of Brent Crude spot prices from the US Energy Information Administration (EIA) and found 8 oil shock events.

These 8 events signified any time oil prices surged by 25% or more from a local trough to a peak within a short timeframe.

Why 25% specifically?

Because a slow rise in oil over a long period usually gives people and businesses time to adjust. What tends to create more panic is when oil suddenly jumps in a short period of time.

We also kept a six-month gap between two oil shocks, so the same volatile period would not get counted more than once.

This way of shortlisting gave us 8 distinct oil shock events from 2000 to 2023.

For each oil shock, we picked two dates using EIA daily Brent data:

• Trough: the lowest Brent price before the sharp rise began.

• Peak panic date: the highest Brent closing price during that shock.

From the peak panic date, we tracked the Nifty 50 and four sector indices after 1 month, 6 months, and 12 months.

If the date fell on a weekend or market holiday, we used the next trading day’s closing price.

All index data came from NSE daily closing prices.

What happened to the Nifty 50?

The average oil surge across these 8 events was 54%.

These weren’t small moves. Each one dominated headlines and triggered real fear in the market.

The first month was usually weak

One month after oil prices peaked, Nifty was lower in 5 out of 8 events. The median return was around -4%.

So, even after oil had stopped rising, the market often remained weak for some time.

There are several possible reasons. India imports a large part of the crude oil it needs. So higher oil prices can increase the country’s import bill, put pressure on the rupee and make transport and other costs more expensive.

Companies may also have to spend more on raw materials and transportation. If they cannot pass these higher costs on to customers, their profit margins can come under pressure. Consumers may also cut spending as prices rise.

But we cannot say that oil alone caused the market to fall. These periods also had wars, recessions, policy changes and several other events affecting markets.

All the data tells us is this: one month after oil peaked, Nifty was down more often than up.

Six months later, there was no clear trend

Six months after the oil peak, Nifty was higher in 4 out of 8 events and lower in the other 4.

By then, many other things were affecting the market too. Interest rates, company profits, economic growth, liquidity and demand had all started to matter.

Oil was still important, but it was no longer the only major factor.

One year later, Nifty was higher more often than not

One year after the oil peak, Nifty was higher in 5 out of 8 events.

That is quite different from the first month, when Nifty was lower in 5 out of 8 events.

But this does not mean markets always recovered.

Nifty was still down after one year in 3 events. Two of those falls were very large. It was down 28.1% after the 2000 oil shock and 51.7% after the 2007 oil surge.

But oil was not the only reason for these falls. The 2000 period came around the dot-com crash, while the 2007 event was followed by the global financial crisis.

The same applies to the strong years. Nifty rose 83.2% after the 2003 oil peak and 32.8% after the 2023 event. Those gains also happened because of many factors, not just because oil prices had peaked.

What does this tell us?

A sharp rise in oil prices can create real pressure on the Indian market, especially in the short term.

But an oil shock alone does not tell us where Nifty will be one year later.

The market may recover, stay weak or fall further depending on what else is happening in the economy and the world.

What happened across sectors?

The Nifty shows what happened to the overall market. But different sectors can react very differently when oil becomes expensive.

So we looked at four major sectors: Auto, Banks, IT and Pharma.

Auto can be affected because higher fuel prices can hurt vehicle demand and increase input costs. Banks can be affected if higher inflation leads to changes in interest rates and borrowing conditions.

IT and Pharma earn a large part of their revenue from outside India, so a weaker rupee can sometimes help them.

For this part, we looked at the six oil shocks from 2007 onward because we had consistent data for all four sectors from this period.

Auto was not always the worst hit

Auto looks like one of the most obvious sectors to suffer when oil prices rise.

Higher fuel prices can make owning and using a vehicle more expensive. At the same time, materials such as plastics and synthetic rubber can also become costlier.\

But one year after the oil peak, Nifty Auto was actually higher in 4 out of 6 events.

The returns after a year, were very different from one event to another. Auto fell 53.9% after the 2007 oil peak and 22.7% after the 2018 event. But it rose 38.7% after the July 2008 peak and 69.6% after the 2023 oil squeeze.

This does not mean auto stocks are safe when oil prices rise.

It simply shows that a sector being badly affected by expensive oil does not automatically mean its stocks will perform badly over the next year. A lot depends on other factors and events in the economy and markets.

Banks depended a lot on what else was happening

Banks showed a similar pattern.

Nifty Bank was higher one year later in 4 out of 6 oil shocks. But the returns differed sharply across events.

The best examples are 2007 and 2008.

After the November 2007 oil peak, Bank Nifty fell 53.6% over the next year.

After the July 2008 oil peak, it rose 56% over the next year.

Oil prices were a concern in both periods. But the overall situation was very different.

By late 2007, the global financial crisis was getting worse. By July 2008, banking stocks had already fallen sharply.

We cannot say exactly how much of these returns came because of oil. But one thing is clear: oil alone could not explain what happened to banking stocks.

IT was not much of a shield

IT is often expected to benefit when the rupee weakens because large Indian IT companies earn a lot of money in dollars.

But that did not lead to strong stock returns in these oil shocks.

Nifty IT was higher one year later in only 1 out of 6 events.

It was also higher after one month in only 2 out of 6 events and after six months in only 2 out of 6.

A weaker rupee can help IT companies. But many other things matter too, such as global technology spending, company profits and stock valuations.

So a weaker rupee alone was not enough to protect IT stocks.

Pharma was mixed too

Pharma is also seen as a relatively defensive sector.

People still need medicines even when the economy slows down, and many Indian pharma companies also earn money overseas.

But the results were mixed.

Nifty Pharma was higher one month later in 3 out of 6 events.

One year later, it was higher in only 2 out of 6.

So neither IT nor Pharma worked as a reliable protection against oil shocks. Sometimes they performed well and sometimes they did not.

Conclusion

So what if you had sold your entire portfolio every time oil prices peaked?

If you sold and stayed out of the market for the next 12 months, you would have missed gains in 5 out of 8 events because Nifty ended the year higher.

But selling would have helped in the other 3 events, including the huge market falls after the 2000 and 2007 oil shocks.

So selling was not always a bad decision. It was simply not a strategy that worked consistently.

In the short term, markets were usually weak. One month after the oil peak, Nifty was lower in 5 out of 8 events.

But over longer periods, oil alone became less useful in telling us where the market would go. Other things such as the economy, interest rates, company earnings and global events started to matter much more.

The sector data tells the same story.

Auto looks like one of the obvious losers when oil gets expensive. Yet it was higher one year later in 4 out of 6 events.

IT can benefit from a weaker rupee. Yet it was higher one year later in only 1 out of 6.

So a sharp rise in crude oil is definitely something Indian investors should pay attention to, especially because India imports a large part of the oil it uses.

But history does not show that a rise in oil prices, by itself, was a reliable reason to sell Indian stocks.

And there is one more important point.

In this study, we assume that an investor somehow managed to sell on the exact day oil reached its peak.

In real life, you do not know that it is the peak until much later.

Notes

• Brent crude oil prices are taken from the US Energy Information Administration (EIA). Nifty and sector index levels are based on NSE daily closing data.

• All returns are based only on changes in index prices. Dividends are not included.

• We have used closing prices, so intraday movements are not captured.

• The 2018 Iran oil shock rose 24.2% in our data. So the 25% oil-shock filter should be treated as an approximate cutoff, not an exact one.

• The Nifty analysis covers 8 historical oil shocks. The sector analysis covers 6 events from 2007 onward. Since the sample is small, these results should be treated as historical observations, not as a prediction of what will happen in the next oil shock.

• The study does not assume when an investor would buy back into the market after selling. It also does not include returns earned on cash, taxes or transaction costs.

• Past performance does not guarantee future results. A future oil shock could play out very differently, especially if the supply disruption is larger or lasts much longer.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

It was not just property prices. Even the stock markets hit all-time highs.

The Nikkei 225, Japan’s main stock market index, rose to a record high in December 1989.

It was the peak of Japanese markets for the next 34 years. It reached that level again only in 2024.

This rise in land and share prices gave banks more confidence to lend to companies.

The idea was even if the company struggled to repay, the property or other assets it had put up as collateral would be valuable enough to cover the loan.

Companies took advantage of this easy money.

They borrowed more, bought property, and expanded into new businesses. This led to a rise in corporate debt.

This created a cycle.

As asset prices rose, companies could borrow even more. That extra money then helped push property and stock prices even higher.

But the whole system depended on prices continuing to rise.

By the early 1990s, the bubble burst. Property prices fell, the stock market crashed, and the assets backing many loans suddenly became worth much less.

Companies, however, were still left with the large debts they had built up during the boom.

Normally, some of these companies would have gone bankrupt.

But Japanese banks continued lending them money instead of letting them fail.

Why? Because if the banks admitted that these companies could not repay their loans, the banks would have to record big losses.

So the banks kept supporting companies that were already weak and struggling.

These companies stayed open, even though they were not making enough money to pay their debts properly.

These were called zombie companies.

They were not necessarily bankrupt. Their factories could still be running, employees could still be working, and they could still be earning revenue.

The problem was that a large part of what they earned was going towards paying interest. That left very little room if the business had a bad year.

And that leads to a simple question when looking at any company:

How much does the business earn compared with the interest it has to pay?

That is what the Interest Coverage Ratio, or ICR, measures.

If a company earns twenty times what it needs to pay in interest, it can still pay the interest comfortably if things go wrong than a company that can barely cover its interest once. This is because the safety cushion for the company earning more is higher.

That makes higher ICR sound like a good way to pick financially stronger companies.

But does a bigger interest cushion also mean a better stock?

So we tested two things:

Did companies with higher ICR give better stock returns?

And even if they did not, did a higher ICR at least reduce the chances of a very large fall?

The Study

We decided to test this using companies from the Nifty 100.

But we could not simply compare the ICRs of all 100 companies.

We excluded banks and lending-focused financial companies because borrowing and lending money is part of their core business. Their ICRs are not directly comparable with companies in sectors like manufacturing, IT, pharma or consumer goods.

We also removed companies that did not have enough historical ICR and adjusted share-price data to run the study from 2016 onwards, including more recently listed companies.

That left us with 59 non-financial companies.

The next question was when to start measuring the stock’s return.

Suppose a company reports an ICR of 10x for FY2018. Investors would not have known this at the start of FY2018 because the company reports it after the financial year ends.

Therefore, we used the ICR only after it became publicly available.

For each company, we collected the annual ICR from 2016 to 2025. We then waited 60 days after the financial year ended to allow time for the company to publish its annual results.

After those 60 days, we took the adjusted closing price on the next trading day and treated that as the price at which an investor could act on the ICR.

We then tracked how that stock performed over the next 12 months.

We basically tried to answer two questions:

Did companies with higher ICRs give better returns over the following 12 months?

And even if they did not, were they at least less likely to suffer a very large fall?

Results

1. Higher ICR did not give better returns

If a higher ICR helped in picking better stocks, the companies with the biggest interest cushion should have gone up more often.

But that did not happen.

Across all 590 readings, the stock was higher one year later 68% of the time.

And this barely changed depending on the ICR.

Companies with an ICR above 20x rose 67% of the time. Companies with an ICR below 1.5x rose 74% of the time.

So having a bigger interest cushion did not make the stock more likely to go up.

But simply going up was also a fairly low bar.

Stocks in our sample were positive quite often during the period we studied. So we asked another question.

Did companies with higher ICRs at least do better than the middle stock in our sample that year?

Again, they did not.

The High group beat the middle stock 45% of the time. The Safe group did so 47% of the time, the Moderate group 52%, and the Danger group 65%.

The Danger group was also much smaller, with only 46 readings. So the point is not that low-ICR stocks were better. The important thing is that there was no pattern where returns improved as ICR increased.

This does not mean that low-ICR companies were better stocks to buy.

It simply means that a bigger interest cushion did not give us a reliable advantage when it came to returns.

Cummins India is a good example.

In the 2019 reading, the company was covering its interest almost 65 times over.

That is an extremely comfortable ICR.

But over the following 12 months, the stock still fell by around 52%.

The company could comfortably pay the interest on its debt.

But that did not tell us where its share price would go next. And that is because ICR only tells us one part of the story.

It tells us how comfortably a company can pay its interest.

It does not tell us whether the stock is expensive, how fast the business will grow, whether profits will disappoint, or what investors will expect from the company next.

So a financially comfortable company does not automatically become a better-performing stock.

2. But higher ICR did reduce the chance of a very large fall

This is where the result changed.

We looked at how far each stock fell from a peak to a later low during the following 12 months.

We then counted how often that fall crossed 30%, and how often it crossed 50%.

Here, a higher ICR did make a difference.

In the Danger group, ICR was below 1.5x. That meant there was relatively little room between operating profit and the interest bill. Some readings were below 1x, where operating profit did not fully cover the interest cost.

Among these companies, 43% saw their share price fall by 30% or more. Around 22% saw a fall of 50% or more.

In the Moderate group, those numbers fell to 31% and 10%.

In the Safe group, they fell further to 27% and 4%.

And among companies with an ICR above 20x, 24% saw a fall of 30% or more, while only 3% saw a fall of 50% or more.

So as the interest cushion increased, very large falls became less common.

This does not mean that high-ICR stocks could not fall.

They did.

But the chances of suffering the really deep falls were much lower.

So while ICR did not tell us which stock would rise, it did tell us something about the risk of a very large fall.

This was the clearest pattern in the study: very large falls became less common as ICR increased.

But there was another interesting part of the result.

Some of the biggest winners also came from the weakest ICR group.

CG Power had negative interest cover in the 2020 reading. Its share price went from around Rs 6 to around Rs 84 over the following year.

Tata Steel had an ICR of around 0.8x and its stock roughly tripled over the next 12 months.

That does not mean investors should look for companies with weak interest coverage.

The same low-ICR group also saw the highest number of very large falls. So, this group contained both some of the biggest winners and some of the biggest losers.

So ICR was giving us a much clearer signal about risk than about returns.

But before treating that as a universal rule, there was one more thing we had to understand: ICR itself looks very different depending on the kind of business a company runs.

So why does ICR behave this way?

Part of the answer is that ICR is not really a free-floating number. It is closely tied to the kind of business a company runs.

Some businesses generally need less borrowing compared with the profits they earn. Many IT, FMCG and pharma companies fall into this group, so their interest bills tend to be small compared with their operating profits. That naturally pushes their ICR higher.

Other businesses need much more money upfront. Power plants, telecom networks, roads and metal plants are expensive to build, so companies in these sectors often rely more on borrowing. Their interest bills are therefore larger and their normal ICRs tend to be lower.

We could see this clearly in our data. The typical ICR was around 39x for IT and 24x for FMCG. At the other end, it was around 2.2x for power and 1.7x for telecom.

Here, “typical” means the median ICR across the readings in that sector.

Sort companies by interest cover and you are, to a large extent, sorting them by industry.

This means that when we divided companies based on their ICR, we were not only separating companies based on their ability to pay interest.

We were also partly separating different kinds of businesses.

The High-ICR group contained more asset-light businesses.

The lower-ICR groups contained more capital-heavy businesses.

This matters because it gives us another way to understand the results.

Part of the difference we saw between high- and low-ICR companies may simply come from the kinds of businesses sitting in each group.

A company with an ICR of 5x may look weak next to an IT company with an ICR of 40x. But 5x may be much more normal for a capital-heavy business that needs large amounts of borrowing to build plants, networks or infrastructure.

So ICR is more useful when we ask not only whether the number is high or low, but also whether it is comfortable for that particular kind of business.

This also helps explain why a high ICR did not automatically lead to better stock returns. Different sectors go through different cycles. During the period we studied, several capital-heavy sectors also went through strong rallies even though their ICRs were naturally lower.

So part of what ICR was telling us was simply what kind of business we were looking at.

A high ICR often pointed towards a business that needed less borrowing relative to its profits. A lower ICR often pointed towards a business that needed more capital and therefore carried a larger interest bill.

Neither of those, on its own, tells us where the share price will go next.

So ICR turned out to be useful, but in a narrower way than we first expected. It did not help us reliably pick the better-returning stock.

What it did tell us was how much room a business had to deal with its interest bill and, in our data, companies with a larger cushion were much less likely to suffer the very deepest falls.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

In August 2026, SEBI released a study on individual traders in the equity derivatives market. It found that the number of active individual traders fell about 20% in one year, from 98.1 lakh in FY25 to 78.6 lakh in FY26. The number of first-time traders also fell by around 40%.

So fewer people were actively trading in derivatives than a year earlier.

At the same time, another way of investing kept growing: SIPs.

A systematic investment plan, or SIP, lets you invest a fixed amount into a mutual fund regularly, usually every month.

The mutual fund then pools this money from investors and invests it based on the type of fund. An equity fund, for example, can invest that money in shares, including companies that are part of the Nifty 50.

This is why SIPs are often seen as a steady source of money coming into the stock market.

And Indians are putting more money into SIPs than ever before.

In March 2026, monthly SIP contributions reached a record Rs 32,087 crore. By FY2025-26, AMFI counted around 9.7 crore contributing SIP accounts.

These are SIP accounts, not individual investors. And the people investing through SIPs are not necessarily the same people trading in derivatives.

Still, the broader trend is interesting.

While the number of individual derivatives traders was falling, SIP investing kept growing.

That has also created a common belief about the market.

If thousands of crores keep coming into mutual funds every month through SIPs, there should be some regular buying in the market. So when stocks fall, this steady flow of money should help support prices.

And as SIP contributions become larger, there is more buying happening in the markets. You might expect big market falls to become less common or less severe.

But March 2026 showed why that idea is worth testing. In the same month that SIP contributions hit a record, the Nifty 50 fell 11.3%, its worst monthly fall since March 2020.

One month, however, cannot answer the question.

SIP contributions do not go straight into the Nifty 50. The money goes into different kinds of mutual funds, can be invested at different times, and funds may also be dealing with redemptions or holding some cash.

So the bigger question is what has happened over time.

As SIP contributions have grown, have market falls become less frequent or less severe? And after a fall, has the market recovered faster?

In other words, is there really something behind the idea that SIP money has become a floor for the market?

We tested this using 10 years of SIP contribution data and Nifty 50 returns.

The Experiment

We took AMFI’s monthly SIP contribution data from April 2016 to July 2026 and matched it with the Nifty 50 return for each month.

That gave us 124 months of data.

We then looked at four simple things. First, whenever SIP contributions hit a new record, did the Nifty go up or down? And was that any different from other months?

Second, how often did the Nifty fall by at least 2%, 5% or 10%? We checked both the month-end return and the lowest daily close reached during that month.

Third, when the Nifty fell, did SIP contributions still increase from the previous month?

And finally, when a record SIP month ended with the Nifty falling, what happened to the market 1, 3, 6 and 12 months later?

The first question was our main test, and we decided on it before looking at the results.

The chart shows how SIP contributions kept rising through most of the period, even when the Nifty was going through sharp falls.

The black dots mark every month when SIP contributions reached a new record. The shaded red areas mark periods when the Nifty was down more than 10% from its earlier high.

Record SIP months were common, but they were not safer

We first looked at every month when SIP contributions reached a new record.

That happened quite often. After April 2016, 74 of the next 123 months set a new high in SIP contributions, about three out of every five months.

So we wanted to see whether the market behaved any differently during these record SIP months.

It did not.

The Nifty fell in 43% of record SIP months, compared with 41% of other months. That is almost no difference.

And the same pattern held when we looked at bigger falls.

Even when SIP contributions were at a record, the Nifty could still fall sharply during the month. So record SIP contributions were clearly bringing more money into mutual funds, but those months were not noticeably safer for the market.

Simply knowing that SIP contributions had hit a new high did not make a falling month any less likely.

SIP contributions continued to rise during major market declines

Since April 2016, the Nifty went through five declines of more than 10% from a market high to a later low.

In every one of those declines, monthly SIP contributions were actually higher by the time the market reached the bottom than they were when the fall began.

These are multi-month, peak-to-trough moves on month-end closes, a different measure from the single-month returns used elsewhere. Measured on daily closes, the same declines were deeper still.

So SIP investors kept contributing even while the market was going through large falls.

Another way to test the SIP-floor idea is to compare months with low and high SIP contributions.

If larger SIP contributions were making the market more stable, then months with the highest SIP contributions should have been less likely to end in a fall.

We divided all 124 months into three roughly equal groups based on SIP contribution levels: low, middle and high.

The Nifty fell in 38% of low-SIP months, 46% of middle-SIP months and 41% of high-SIP months.

So months with the highest SIP contributions were not clearly less likely to fall than months with lower SIP contributions.

Looking at both the larger market falls and the different levels of SIP contributions, the result was similar. SIP contributions kept coming in during falling markets, but higher contributions did not make falling months clearly less common.

There is one limitation to this comparison. SIP contributions generally grew over time, so the low, middle and high groups also roughly represent different periods of the market. That means we cannot say SIP contributions caused, or prevented, any particular market move.

Also, these are gross SIP contributions. They do not include redemptions by other mutual fund investors.

So the data only shows that SIP contributions continued during market falls. It does not mean there was no selling happening at the same time.

SIPs did not stop the fall, but investors kept investing through it

This is where the SIP story becomes more interesting.

The data does not show that SIPs prevented market falls. But it does show that contributions often kept rising even while the Nifty was falling.

Out of the 52 months when the Nifty ended lower, SIP contributions were still higher than the previous month in 33 cases. The same pattern continued during sharper falls too.

That is an important difference.

The strength of a SIP is not that it stops the market from falling. It is that the investment can keep happening without the investor having to make a fresh decision every time the market turns negative.

When markets fall, a person investing manually may hesitate, wait for things to improve, or simply stop buying for a while. A SIP keeps following the standing instruction unless the investor actively changes it.

So the clearest evidence of SIP resilience in this data is not a market floor. It is that investors kept contributing even through periods when the market was falling.

After a fall, the Nifty was usually higher later

There is one more part of the SIP-floor idea to test.

Even if SIP money does not stop the market from falling, perhaps it helps the market recover faster afterwards.

So we looked at the 32 record SIP months in which the Nifty ended lower and checked where the index was 1, 3, 6 and 12 months later.

A year later, the Nifty was higher than that falling month’s close in 28 of 30 completed cases, or 93%.

But the result was almost exactly the same when we looked at all falling months, not just record SIP months. After 12 months, the Nifty was higher in 43 of 46 cases, also 93%.

So record SIP months did not show a clearly better recovery pattern.

One thing to keep in mind is that “higher” only means the Nifty was above the close of the falling month. It does not necessarily mean the market had recovered everything it had lost before that month.

The broader point is that a bad month was often followed by a higher Nifty over the next year. But record SIP contributions did not make that recovery clearly faster.

For an SIP investor, the benefit was different. The investment simply kept going through the fall, even when nobody knew how quickly the market would recover.

So why doesn’t record SIP money stop the market from falling?

The main reason is simple. SIP contributions are not the same as direct buying of Nifty 50 stocks.

The SIP number reported by AMFI includes money going into different kinds of mutual funds, including equity, debt, hybrid, gold and international funds. So the entire monthly SIP amount does not go into the stock market.

It is also a gross contribution number. It tells us how much money came in through SIPs, but not how much other investors withdrew from mutual funds or how much funds actually invested in equities that month.

Timing matters as well. The Rs 32,087 crore contributed in March 2026 did not enter the market on one day. SIP instalments came in on different dates throughout the month, while stock prices were changing every day.

And regular buying does not mean prices cannot fall. If there is enough selling pressure, prices can still move lower. SIP money may provide some support, but it cannot stop the market from falling.

That is also what the data showed. Record SIP months were not clearly safer for the market. Large falls still happened even as SIP contributions kept rising, and the Nifty did not recover noticeably faster after record SIP months.

But that does not mean SIPs do not matter. Their strength is simply somewhere else.

SIP money may not stop the market from falling, but it can help investors keep investing through those falls.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

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Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

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Research Analyst Disclaimer

In 2026, the International Air Transport Association expects a record 5.1 billion passengers to travel by air. Planes were also very full, with about 84% of seats filled on average, the highest level ever.

At first, this sounds like great news for airlines.

But it isn’t that simple.

Fuel prices went up because of the war in the Middle East. At the same time, Boeing and Airbus were not able to deliver enough new, fuel-efficient planes, so airlines had to keep using older ones that cost more to run. On top of that, low-cost airlines kept ticket prices low because of competition.

So even though more people were flying, airlines were not necessarily making much more money.

IATA eventually cut its expected industry profit margin for the year from 3.9% to just 2%.

In other words, more activity did not automatically mean more profit.

This brings us to a belief many investors naturally hold about the economy and the stock market.

When India’s GDP grows quickly, it simply means more activity is happening in the economy. People are spending more, companies are selling more, factories are producing more, and businesses are expanding.

So the logic feels straightforward.

If the economy is doing well, companies should do well. And if companies are doing well, their stock prices should rise too.

But the real question is, does it actually work like that?

Does faster GDP growth really mean better stock market returns?

We decided to test this.

And we looked at the relationship in two simple ways: first, whether GDP growth and Nifty returns actually move together year by year, and second, whether the companies in the stock market even reflect the same parts of the economy that GDP is measuring.

The Study

We looked at 21 Indian financial years, from FY06 to FY26, and compared two numbers for each year: the Nifty 50’s annual return and India’s real GDP growth.

Then we asked a simple question:

How often did GDP and the Nifty move in the same direction?

But there was one problem.

The Nifty goes up in most years anyway. So even if stocks rose in most years when GDP grew, that alone would not prove that GDP was a useful signal.

So we also compared the result with the Nifty’s normal base rate — how often it went up regardless of GDP.

This helps us see whether knowing that the economy was growing actually told an investor anything extra about what the stock market would do.

What We Found

1. At first, GDP and the Nifty seem to move together

In 14 of the 21 years, GDP and the Nifty moved in the same direction. In the other 7 years, they did not.

At first, that looks like a solid relationship. But it is also important to know that the Nifty rose in 15 of the 21 years anyway.

GDP grew in 20 of those 21 years, and the Nifty rose in 14 of those 20 years. That is a 70% success rate.

The Nifty’s normal base rate was 71%.

So knowing that GDP grew did not really improve your chances of predicting whether the Nifty would rise.

Most years look like they support the idea that a growing economy goes with a rising market. But the highlighted years show why that relationship is not dependable and FY16 and FY21 are the clearest examples.

2. Some years showed a very different picture

FY16 is a good example.

India’s economy grew by 8%, but the Nifty fell 9.87%.

One reason was that the market had already risen 26.33% in the previous year, partly because investors were expecting strong growth. By FY16, concerns about China’s slowdown and weak corporate earnings became more important.

FY21 showed the opposite.

India’s GDP fell 5.78% because of the Covid lockdowns.

Yet the Nifty rose 77.99% during the same financial year.

The market had already crashed when investors feared the worst in March 2020. It then started rising as investors expected the economy to recover.

This highlights an important difference.

GDP tells us what happened in the economy. The stock market tries to price what investors think will happen next.

3. GDP is only one of many things that move stocks

Stock prices also depend heavily on company profits.

For example, during the mid-2010s, India’s GDP was growing at around 7–8%, but earnings of listed companies barely grew as banks dealt with large bad loans.

Then, in 2019, the government cut corporate tax rates. That directly improved company profits even though GDP growth did not suddenly jump.

Valuations also matter.

A company’s share price depends not only on how much it earns, but also on how much investors are willing to pay for those earnings.

That can change because of interest rates, foreign investor flows, wars, crises and other market developments.

So even when GDP is growing strongly, another factor can become more important for the stock market in that particular year.

What the Market Is Made Of vs What the Economy Is Made Of

There is another reason GDP and the Nifty may not move together, and it comes down to a basic but important point: they are measuring two very different things.

GDP is designed to capture the entire Indian economy — everything from farming and manufacturing to services, government activity and informal work. If you break India’s economy into broad buckets, it usually comes down to three parts: agriculture, industry and services.

Over the last two decades, this structure has been fairly stable. Services have consistently been the largest share, contributing around 60% of total output, while agriculture and industry have each stayed roughly around 20%.

India’s GVA by broad sector, FY06 vs FY26. The economy’s shape barely changed. Source: MoSPI National Accounts (series change noted below).

The Nifty, however, is not built to reflect this structure. It is not a miniature version of the economy. It is simply an index of 50 large, listed companies.

And that difference matters.

For example, agriculture still accounts for nearly 18% of India’s economy, but there is no agriculture company in the Nifty 50 at all. In other words, a sector that represents almost one-fifth of economic activity is completely missing from the stock market index.

This is the first clear gap between the two: large parts of the real economy are not represented in the market at all, while some sectors are heavily overrepresented.

Even services mean different things

Services are the biggest part of both India’s economy and the stock market index. So at first glance, it feels like they should move together.

But the important point is this: “services” does not mean the same thing in both places.

In the real economy, services are everything people use in daily life. This includes small shops, local transport, restaurants, hotels, delivery services, and also government services like schools, hospitals, and administration. A large part of this is informal and spread across millions of small businesses and workers.

In the stock market (the Nifty), “services” is a much narrower and more corporate group. It is mostly made up of large listed companies, especially banks and IT companies.

This creates a mismatch.

GDP growth vs Bank Nifty and Nifty IT returns, FY06–FY26. Source: NSE, World Bank WDI.

Take IT companies first.

Big Indian IT firms earn most of their money from clients outside India — mainly in the US and Europe. So their performance depends more on global technology demand and the value of the dollar compared to the rupee, rather than how fast India’s own economy is growing. Because of this, there were several years in our study where Nifty IT fell even though India’s GDP was growing.

Now look at banking.

Banks are closely linked to the Indian economy because they lend to Indian households and businesses. So in that sense, they do reflect domestic conditions.

But in the stock market, banks are extremely important because they take up a very large share of the index.

Today, financial services make up about 37% of the Nifty. In comparison, in the broader economy, the entire category that includes finance, real estate, IT and professional services is only about 27% of total output (GVA) — and banking is just one part of that.

So what this means is:

• The economy is made up of millions of small and large service activities spread across the country.

• The Nifty is heavily influenced by a small number of large financial and IT companies.

Because of this difference, the stock market can move in ways that do not always match what is happening in the broader economy.

The Nifty itself has also changed.

The Indian economy has stayed broadly similar in its structure over time, but the Nifty has changed a lot in what it represents.

For example, in 2005, oil and gas companies made up about a quarter of the entire index, making them the most important part of the Nifty. By 2025, their share had dropped to around 10%.

At the same time, financial companies became much more important. Their weight in the index rose from about 13% to 37%.

IT companies moved in the opposite direction, falling from around 20% to 10%.

Nifty 50 sector weights, 1995–2025. Unlike the economy, the index reshaped itself completely. Source: NSE Nifty 50 whitepaper.

What this means is simple: the Nifty today is not the same “basket” it was 20 years ago.

So when we compare GDP with the Nifty, we are not really comparing the same thing.

GDP is a picture of the entire Indian economy — everything from farming and factories to shops, services and government activity.

The Nifty, on the other hand, is just a group of 50 large listed companies, and some sectors like banking and finance have a much bigger influence on it than others.

Because of this difference, it is natural that the Nifty and GDP do not always move together.

Note

Nifty returns exclude dividends. This changes the return level slightly, but not whether the market rose or fell.

GDP and Nifty are measured differently. GDP is inflation-adjusted; the Nifty is not.

GDP data comes from the World Bank. FY26 growth is estimated at 7.57%, versus India’s provisional 7.7%.

GVA methodology changed over time. So FY06 and FY26 sector shares are useful for broad comparison, not exact like-for-like comparison.

Nifty and GVA sectors are classified differently. The sector comparison is therefore directional, not exact.

Nifty sector-weight methodology also changed. Older data uses full market cap, while newer data uses free-float market cap.

Moving together does not mean GDP caused the market move. That is why we compare the result with the Nifty’s normal base rate.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

Its author, Robert Armstrong, was trying to make sense of something unusual happening in the US stock market.

It started on 2 April 2025.

Donald Trump announced what he called “Liberation Day” tariffs. The US said it would put a 10% tax on most imports, and even higher tariffs on some key trading partners.

Investors around the world quickly got worried.

People started thinking that other countries might hit back with their own tariffs, US companies could end up paying more for imported goods, global trade might slow down, and overall economic growth could take a hit.

So markets reacted badly.

But just a few days later, on 9 April, Trump put a 90-day pause on many of the higher tariffs. The 10% base tariff stayed in place, and China was still treated differently as tensions between the two countries continued. Over the next few weeks, the administration also signalled openness to negotiations and made small adjustments to its stance.

And markets recovered.

Armstrong pointed out something interesting. It wasn’t like the problem had actually gone away. The tariffs were still in place, and there was still a lot of uncertainty.

But investors had started to see a pattern.

Trump would announce a big tariff, and markets would drop right away because people worried things would get much worse.

Then, after a few days or weeks, the policy would often get softened. Tariffs might be pushed back, some goods might get exemptions, or talks would start to change the plan.

Armstrong gave this pattern a name: TACO — Trump Always Chickens Out.

The phrase quickly caught on, and traders started calling it the “TACO trade”.

The phrase itself was cheeky. But the problem it described was very real for investors.

When a tariff was announced, nobody knew what the final policy would actually look like.

It could stay as announced, be reduced or delayed, include exemptions, or escalate if the other country retaliated.

The problem was that investors had to make a decision before they knew which of these would happen.

And that is what made the TACO trade interesting.

It was not saying that tariffs did not matter. They did. Even if Trump later softened his position, existing tariffs and all the uncertainty around them could still hurt businesses and the economy.

The real question was different:

How much of the fear on announcement day would actually turn into real economic damage?

Sanctions create a very similar problem.

Suppose the US announces sanctions on a Russian oil producer. The sanction is real, but for an Indian investor the impact is uncertain. Will oil prices rise? Will India lose access to discounted Russian oil? Will refiners face higher costs, and if so, will it actually be enough to hurt profits or stock prices?

The headline hits immediately, but the real economic impact takes time to show up.

So we asked a simple question: when major sanctions are announced, should Indian investors actually sell? Does the Nifty keep falling? Do the most exposed sectors get hit harder? Or does the initial fear fade away?

To find out, we studied eight major sanctions shocks over the last decade and a half.

THE EXPERIMENT

But first, we had to decide which sanctions to study.

That was not as simple as it sounds.

Governments announce thousands of sanctions and restrictions. But most are very narrow — one person gets added to a sanctions list, one company gets blocked, or one ship loses access to insurance.

These usually don’t move a broad market index like the Nifty, so including them would just add noise.

So we used two simple filters.

First, the sanction had to be big enough to matter economically — something affecting banks, oil exports, key technology, or trade with India, not just a single person or vessel.

Second, there had to be a clear way for the shock to reach Indian companies.

That could happen through oil prices, technology and supply chains, the global financial system, or direct trade pressure on India.

If a sanction sounded dramatic but had no obvious connection to Indian businesses, we left it out.

Using these filters, we picked eight major sanctions episodes.

We also made sure they covered different kinds of shocks (energy, technology, banking and direct trade) instead of studying eight versions of the same problem.

And when several sanctions were announced within a few days as part of the same crisis, we counted them as one episode. Otherwise, one crisis could end up appearing several times in the study.

Most importantly, we fixed these events before looking at what happened to the Nifty afterwards because otherwise it would be easy to pick only the sanctions that coincided with big market falls.

We did not do that.

Some of the events in our list barely moved the Nifty. The Huawei blacklist is one example. The Russian oil price cap also produced very little immediate market reaction.

We kept them anyway. Removing events where markets didn’t react would bias the results and create a false pattern. The eight events start in 2018 because that’s when we have reliable daily Nifty and sector data.

For every event, Day 0 is the first trading day in India after the announcement.

We then asked three simple questions: how much did the Nifty fall over the next 20 trading days, how long it took to recover, and what would have happened if an investor sold on Day 0.

We also compared the most exposed sector in each case with the Nifty to see if it actually underperformed the broader market.

One important exception is February 2022, when Russia was sanctioned at the same time as the Ukraine invasion, making it hard to separate the impact of the two events.

How much did the market actually fall?

The real question is not what happens on the day of the news, but how much damage actually sticks after things settle.

In 7 of these 8 episodes, the Nifty did fall below its pre-event close at some point over the next 20 trading sessions. So there was usually some weakness after the sanctions news. But importantly, this weakness was often short-lived or shallow.

When we look at the depth of those moves, a clear pattern emerges.

The first thing we noticed was that most sanctions did not lead to a big market fall.

In 5 of the 8 cases, the Nifty never fell more than 2% below where it had started.

In other words, even after a full month of trading, the market was still broadly holding its ground. The Huawei episode was even milder. The Nifty never actually closed below its pre-event level, suggesting that the event barely had any lasting impact on the market.

Then came the mid-range reactions.

In 2 cases, the Nifty fell between 2% and 5%. After the US exited the Iran nuclear deal in 2018, the Nifty fell by a maximum of about 2.7%, as investors worried about oil prices and global supply. The December 2022 Russian oil price cap led to a bigger fall of about 4.8%. This came at a time when global inflation was already high and energy markets were sensitive, so the shock had more room to affect equities.

And then there was one clear outlier. Only 1 of the 8 episodes saw the Nifty fall more than 5%. In February 2022, the Nifty fell about 7% from its 23 February close. But this was not a clean sanctions-only event. Russia’s invasion of Ukraine happened at the same time, triggering a much broader sell-off across global markets. So it is difficult to say how much of that 7% fall came from the sanctions alone.

Each bar in the chart shows the maximum fall (peak-to-trough) in the Nifty over the 20 trading sessions after each event, measured against the Nifty’s closing level on the day just before the event or announcement happened. It helps compare how deep the market actually went in each case, rather than just how it reacted on the day of the announcement.

A 1% or 2% move might not sound dramatic, but in market terms it still reflects real uncertainty. The key takeaway, however, is that the initial fear in headlines was usually much larger than the actual, sustained impact on the index.

How long did the fall last?

How far the market falls is only one part of the story. A small fall can still hurt if the market stays down for a long time.

So for the 7 events where the Nifty fell below its level before the announcement, we looked at how long it took to recover from its lowest point and close back at or above that starting level.

In most cases, the recovery was fairly quick.

4 of the 7 events recovered within 5 trading sessions of hitting their low. The Nifty recovered in just 1 session after the Iran sanctions were fully reimposed, 3 sessions after the Rosneft-Lukoil sanctions, and 5 sessions after both the China chip controls and the 2025 India tariff.

Another 2 recovered within 8 sessions. The 2018 Iran nuclear-deal exit took 6 sessions, while the February 2022 episode took 8.

That leaves just 1 clear exception: the December 2022 oil price cap, which had still not recovered 60 sessions later. We look at it separately below

This shows how many trading sessions it took for the Nifty to recover from its lowest point back to its pre-event level. In other words, it measures how long the market stayed weak after the initial fall before getting back to where it started. Huawei is excluded because the Nifty never fell below its pre-event close.

But there is an important distinction here. When we say the Nifty “recovered”, we only mean that the index returned to its pre-event level. It does not mean the sanctions disappeared or stopped having an economic impact. The market was also reacting to many other things at the same time.

Did bigger falls take longer to recover?

The next question is an intuitive one.

If the market falls more after a sanctions shock, does it also take longer to recover?

If that were true, the size of the fall could at least tell investors something about how serious the weakness might become.

But in these 8 episodes, that pattern does not really show up.

This table shows each event in order of how much the Nifty fell. For every case, it also tells you when the lowest point happened, how many days the index stayed below its starting level, and how long it took to recover back to that level.

Take February 2022. It produced the deepest fall in the entire sample, with the Nifty dropping about 7%. Yet once the market hit its low, it took only 8 trading sessions to get back to its pre-event level.

Now compare that with the December 2022 oil price cap. The Nifty fell less, about 4.8%, but it still had not recovered 60 sessions later.

The smallest fall did recover the fastest: after the full Iran sanctions were reimposed, the Nifty’s deepest decline was just 0.7%, and it recovered in 1 session. But beyond that, there is no clear pattern where a deeper fall automatically meant a longer recovery.

The timing of the lowest point was unpredictable too.

In 7 of the 8 episodes, the Nifty did not hit its lowest point on Day 0. The low came anywhere from 1 to 14 trading sessions later.

So the market’s first reaction did not tell you how far it would eventually fall, how long the weakness would last, or even when the worst point would arrive.

Sessions after each shock when the Nifty reached its lowest point. In most cases, the low did not arrive on Day 0.

What if you sold when the news came out?

So far, we’ve looked at what the market did after these events. But for an investor reading the headline, the decision is either sell or stay invested?

To test that, let’s assume you sold the Nifty at the end of Day 0 because the sanctions news felt scary.

We then looked at what actually happened over the next month.

In 6 of the 8 cases, the Nifty was higher 20 trading sessions later than it had been when you sold.

Only 2 of the 8 cases ended the month below the Day-0 selling level

This shows where the Nifty was 20 trading sessions after Day 0 — i.e., the level an investor would have been at if they sold immediately after the news and checked back a month later.

But there is an important catch here.

Looking at it does sound impressive. The Nifty was higher after 6 of the 8 sanctions events. But that alone doesn’t mean much, because markets usually go up over time anyway.

In our full dataset of 4,331 starting points, we looked at what happens if you pick any random day and check where the Nifty is 20 trading sessions later. In about 61.3% of those cases, the index was higher than where it started.

Basically it means that in most normal periods, the market tends to go up over a one-month horizon. So when we see that 6 out of 8 sanctions events also ended higher after 20 sessions, it is not unusual by itself; it is actually quite close to how the market behaves in general.

So this does not mean sanctions were good for the market. It simply means that these sanctions episodes were not followed by unusually bad one-month returns.

In other words, a rule that says “sanctions have been announced, so sell for the next month” would not have worked consistently in this sample.

And if we extend the period further, there is no clear pattern either. After 60 trading sessions, the Nifty was higher in only 4 of the 8 cases.

To understand whether that is unusual, we compare it with normal market behaviour. In the full historical data (across all time periods, not just sanctions events), the Nifty is higher after 60 trading sessions about 66.5% of the time (about 2 out of every 3 times, the market is higher 3 months later anyway).

So neither the one-month nor the three-month numbers give us a reliable rule for what the market does after sanctions.

Conclusion

Across the eight events we studied, the Nifty’s response was generally modest. In 5 out of 8 cases, the maximum fall over the next month stayed within 2%, and only one case saw a decline greater than 5%. Of the events where the index did fall, six of seven recovered their pre-event level within eight sessions of the low, and in 6 of 8 cases, an investor who sold on Day 0 would have been worse off a month later.

Taken together, the pattern suggests that sanctions headlines do not consistently lead to large or persistent equity drawdowns in India.

That said, the reaction is not random or meaningless. It happens because the market has to price something it doesn’t fully understand yet.

On Day 0, investors react to what could go wrong, like trade getting disrupted, payment issues, supply chains breaking, or tensions getting worse. But at that point, nobody knows how much of it will actually happen.

As more details come out, companies adjust, exemptions get added, and global trade finds new routes. That’s why the initial fear often fades.

So the first reaction is often bigger than what actually ends up happening, because the market is reacting to many possible bad outcomes, most of which don’t fully play out.

That said, this is not a reason to ignore these events. The headline is just the starting point. What matters more is what the restriction actually changes for the companies involved, how directly they are affected, and whether the market has already priced in a worst-case scenario.

Sanctions can definitely have real economic impact. But in this sample, the initial fear on announcement day was not a very reliable guide to how much Indian equities would fall, or how long any weakness would last.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

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They were at 17 runs for 5 wickets. Five batsmen were already out, and the World Cup campaign looked almost over.

Then Kapil Dev came in and scored 175 not out. India finished with 266 runs.

Now, if you only saw the final score, which was 266, you might think the whole team batted well.

But that was not the case.

Kapil Dev alone scored 175. The next-highest score was just 24. Most of the team contributed very little.

So the final total looked strong, but it did not show what actually happened inside the team.

That idea of a single headline number hiding a very uneven reality is also useful when we look at markets, though in a slightly different way.

On 2 January 2026, the Nifty 50 closed at an all-time high. It was up 0.7% that day, and the market looked strong on the screen. Since this milestone came after over one year of underperformance, it seemed like the worst was over.

But many investors did not feel this way. Most noticed that their portfolios were red or flat despite Nifty being at an all-time high.

Some were green, but investors were still confused because the Nifty went up 0.7% on that day, but their portfolio was up 0.2%. Technically, they knew they made money. But it did not align with what they saw on the screen for Nifty 50.

So how can the Nifty be at a record high level while your portfolio is not?

Because, like a cricket total, the Nifty is one number. And one number can hide how the individual players did.

To understand this, we decided to study the biggest rallies and falls of the last 15years to see whether the Nifty 50 is a good indicator of portfolio performance.

The study

We took fifteen years of Nifty 50 data and identified its 3 biggest rallies and falls. A rally began when the Nifty started rising from a recent low. If it gained at least 10% before reaching its peak, we counted it as a rally. The rally ended once the index fell 10% from that peak.

Similarly, a downturn began when the Nifty started falling from a recent peak. If it declined by at least 10% before reaching the bottom, we counted it as a downturn. The move was considered over once the index recovered 10% from the bottom.

For each of these six moves, we did two things.

First, we compared the Nifty 50 with two broader indices, the Nifty Midcap 150 and the Nifty Smallcap 250, over exactly the same dates. This helped us to see whether mid-sized and smaller companies performed differently from the large companies in the Nifty 50.

Second, we looked inside the market itself.

For each period, we took the Nifty 100 stocks that were part of the index on the first day of that move. We did not use today’s list, because that would allow hindsight to affect the results.

We then calculated the return of each stock over the period and counted how many stocks beat the Nifty and how many lagged behind it.

A few companies from the older periods have since been delisted, merged or renamed and could not be priced. So, depending on the period, our analysis includes between 84 and 96 stocks instead of the full 100.

Everything went up. Some things went up much more.

So in the three rallies we studied, the broader market did not merely follow the Nifty. It amplified the move.

An investor with a meaningful mid-cap or small-cap allocation could therefore have experienced a much stronger rally than the Nifty suggested.

But this cannot be turned into a general rule that the broader market always moves more sharply.

The three falling periods produced a more mixed result.

During the covid crash, Nifty fell 35.40%, and the small-cap index fell slightly more at 36.60% in the same period. This makes sense with the high-beta theory.

Beta measures the stock’s volatility. The baseline for the index is typically 1. If a stock is a high-beta stock, it means that its stock price moves faster and more aggressively than the market. It amplifies the move in the market. Mid-caps and small-caps are considered to be high-beta stocks.

The midcap index however, fell only 31.60% in the 2019 downturn.

In fact, in the other two downturns of the study, both midcaps and smallcaps fell less than Nifty.

So investing in the broader markets seems like a great bet, right? Amplified returns but lesser losses? That is not always the case.

One possible reason the downturns behaved differently is that each was caused by a different type of selling.

It is important to note that the downturn in 2011 and 2015 was driven by factors that affected large-caps specifically.

The falls in 2011 and 2015 happened during global risk-off periods (the Eurozone crisis and China’s economic slowdown). Foreign investors were pulling money out of Indian stocks during these periods.

Since foreign investors typically invest in large and liquid companies, these large-cap stocks or Nifty 50 stocks, faced more selling pressure. Midcaps and smallcaps remained relatively less affected as domestic investors are the majority investors in these.

Covid crash was different, it was not a calculated sell-off but what is known as a panic sell-off.

When investors are in panic mode, the number of buyers for smallcaps goes down since it is riskier compared to large-caps.

Another factor is valuations. If midcaps and smallcaps are perceived to be valued higher than their actual worth, then any negative trigger can cause them to fall sharply.

If the valuations were cheaper, then those may not have fallen as much as the others.

That is why the mix of stocks in a portfolio matters.

An investor with more midcap and smallcap stocks may have earned much more than the Nifty during the rallies. During the falls, however, the experience depended on the particular period and the stocks they owned.

Either way, the Nifty may not have reflected what that investor actually experienced.

But this still does not explain why portfolios made up mainly of large-cap stocks can also perform very differently from the Nifty.

Why did some large-cap investors still feel left behind?

The answer lies not only in how much the market rose, but also in how unevenly those gains were spread across stocks.

Everything rose, but not equally

We decided to dig deeper and took the Nifty 100 constituents as they existed at the start of each period and measured how each individual stock performed.

We also counted how many of those stocks actually beat the Nifty and compared the index return to the return of the typical, or median, stock.

Some constituents from the older periods have since been delisted, merged, or renamed and could not be priced. These were excluded, so the number of priced stocks per period ranges from 84 to 96 rather than a full 100. The older periods are therefore based on slightly fewer stocks.

Nifty 100 stocks that beat the index vs those that lagged, in each move. The label above each bar is the share that beat it.

“Beat the Nifty” means the stock did better than the index — it fell less, or rose. A move where most stocks beat the index (like May 2022) is broad; one where few do (like May 2020) is narrow.

All 94 stocks in the covid recovery rally from May 2020 rose with Nifty. Not one fell.

However, only 38 of these actually beat the Nifty’s 109% returns. This means the majority of them, 56 of them or around 60% rose but less than the Nifty.

In the May 2022 rally, nearly 60% of the 96 stocks that we studied beat Nifty.

How is that possible?

The Nifty index is made up of the 50 biggest companies – weighted by size. So the biggest among these 50 carry a higher weight. If a handful of these heavyweights rally, it can move the Nifty index meaningfully and much faster than most of the individual stocks are actually moving.

So the median stock rose 93%, while the Nifty index rose 109%. This difference of 16% came from the few large-cap companies that did most of the heavy lifting.

Simply put, if an investor’s portfolio was made up of Nifty stocks but not the exact ones that led the rally, then their portfolio may have gone up. But not as much as the index did.

Everything went up. It just did not go up equally.

90 out of the 94 stocks in the study fell during the October 2019 downturn. Almost everything fell, but not in the same magnitude.

41 stocks fell less than Nifty, while 53 stocks fell more than the index itself. Of course most of them fell in this period but not all of them. The median stock fell 37.9%, which means the average stock fell more than Nifty itself.

So portfolios that had most of the stocks which remained resilient during the fall may have fallen less. And vice versa.

Not all rallies are built the same

Now let’s compare two rallies: the May 2020 and May 2022 rallies.

We found out that only 40% of the Nifty 100 stocks beat the index during the May 2020 rally.

On the surface the index looks strong, but most of its constituents did not keep up with it.

This is called a narrow rally. The index is pushed higher by a relatively small group of strong-performing stocks, while most others lag behind.

The 2022 rally was different. A majority of the stocks participated, and the median stock performed better than the Nifty.

This is called a broad rally, where gains are spread across a larger number of stocks.

You cannot tell whether a rally is broad or narrow simply by looking at how much the Nifty has risen. The index can show a similar gain in two periods, while the experience of individual stocks can be completely different. To understand that, you have to look inside the index.

The same is true during downturns.

During the downturn that began in March 2011, 47 of the 84 stocks in our study fell less than the Nifty. The median stock also performed better than the index.

This means the decline was not spread evenly across most stocks. A smaller group of heavily weighted stocks pulled the Nifty down more sharply.

This brings us to an important point: the Nifty is not the entire market. It is a weighted index of 50 large companies, and the largest companies have the greatest influence on its movement.

Most investor portfolios do not hold the same stocks in the same proportions as the Nifty, so their returns depend on what they own.

A portfolio with more midcaps and smallcaps may behave very differently from one made up mainly of large-cap stocks. These segments usually move more sharply than the Nifty, though not in every period.

Even two large-cap portfolios can have very different returns. One may own the few stocks driving a narrow rally, while the other may not.

No two rallies or downturns are exactly the same. A portfolio may perform well in one and lag in another, depending on its holdings and whether the market move is broad or narrow.

What This Tells Us

The Nifty tells us how a particular basket of 50 large companies performed, based on their weights in the index.

It does not tell us how the typical stock performed or how an investor’s portfolio performed.

Suppose the Nifty rises 20%, driven mainly by a few heavily weighted companies. An investor who does not own those companies in similar proportions may lag, even if most of their stocks performed reasonably well.

That does not necessarily mean the portfolio was badly constructed. It may simply mean that the portfolio and the index owned different stocks in different proportions.

The reverse is also possible. When gains are spread across a larger number of stocks, a portfolio may rise more than the Nifty without owning its biggest constituents.

So before comparing a portfolio with the Nifty, investors should first ask whether it is the right benchmark.

A portfolio with mostly midcaps and smallcaps should not be expected to move like a large-cap index. But even a large-cap portfolio may behave differently unless it holds roughly the same companies in roughly the same weights.

Conclusion

The Nifty is not misleading. It is doing exactly what it is designed to do.

The mistake is expecting one market number to describe every portfolio.

When the Nifty reaches an all-time high and your portfolio does not, it may simply be because the stocks driving the index are missing from your portfolio or are held in smaller proportions.

Similarly, when your portfolio rises more than the Nifty, it may be because you own parts of the market that are performing better or because the rally is spread across more stocks.

So the real question isn’t just whether your portfolio beat the Nifty. It is whether what you owned was similar enough to what actually drove the Nifty in the first place.

The thing is, Nifty gives us the final scoreboard. But to understand what an investor experienced, we still need to look at the players who produced it.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

It was a surprising moment because…

For most of the 20th century, Kodak was one of the world’s most recognised brands. At its peak, it controlled around 90% of US film sales.

Its business model was simple but powerful.

One might assume Kodak made most of its money from selling cameras. But the camera was only the starting point.

Once someone bought a camera, they kept coming back to buy film for every holiday, birthday or family event. And when the roll was finished, they paid again to get the photos developed.

Millions of customers repeated this cycle for years, making Kodak incredibly good at generating cash.

Then digital cameras started replacing film, and that cycle began to break.

Soon after, smartphones made taking photos even easier and more effortless. Buying film for photos became a thing of the past, and people no longer had to pay to develop pictures either.

Suddenly, people could take thousands of photos at almost no extra cost.

As this shift happened, the habit that had powered Kodak for decades began to fade.

People were taking more photos than ever, but Kodak was no longer part of the process.

Kodak was still a familiar name. Its brand, factories and patents were still intact, but the business that had supported the company for years was weakening.

As film sales and photo development declined, Kodak stopped generating cash the way it once did.

That created a much bigger problem.

Before filing for bankruptcy, the company had already warned that it might not generate enough cash to repay debt, run its day-to-day operations, invest in new products and meet its other financial commitments.

Cash Flow

A company can have a famous brand, great products, factories and valuable patents. But if it is not bringing in enough cash, the business may not be as strong as it looks

Profit tells you what a company earned on paper.

Cash flow tells you whether the money actually came in.

And the two don’t always match.

A company may record a sale today even if the customer pays months later. So the profit is already counted, but the cash has not yet reached the bank.

At the same time, money may be stuck in unsold inventory. The company still has to pay suppliers, employees and other expenses on time, even if customers have not paid yet.

So even if a business looks profitable on paper, it can still run into cash problems in real life.

That’s why investors care so much about operating cash flow.

When a company keeps generating more cash from its main business, it usually looks like a good sign. The company is not just reporting higher sales. It is actually turning those sales into money that can be used to grow, repay debt or reward shareholders.

That leads to a simple question.

If a company’s operating cash flow keeps rising, does that also make it a better investment?

So we decided to test it.

The experiment

Here’s how we tested the idea.

We started with the Nifty 100 as it stood on 31 March 2015. We used the historical list instead of today’s index so we wouldn’t benefit from hindsight by automatically excluding companies that later struggled or disappeared.

Next, we removed banks, NBFCs and other financial companies because operating cash flow works very differently for lenders.

That left us with 76 non-financial companies. Four companies didn’t have enough usable historical data—Cairn India, Aditya Birla Nuvo, GlaxoSmithKline Consumer Healthcare and Reliance Communications—leaving 72 companies for the study.

For each company, we looked at the last six years of operating cash flow available before July 2015 (FY2010-FY2015 for most companies).

We then divided the companies into four groups:

• Continuously increasing: Cash flow stayed positive and increased every year.

• Continuously decreasing: Cash flow stayed positive but declined every year.

• Mixed: Cash flow rose in some years and fell in others.

• Turnaround/Negative: One or more years had negative operating cash flow.

Finally, we assumed an investor bought all the stocks on 1 July 2015, after the financial statements were available, and tracked their returns until 24 July 2026.

Every stock was compared against the Nifty 100 Price Index over the same period. This is a price-return study, so dividends have been excluded from both the stocks and the benchmark.

Results

Perfect cash-flow growth was almost nonexistent

If you think great companies steadily generate more cash every year, the data tells a different story.

Out of the 72 companies we studied, only three managed to increase their operating cash flow every single year over the previous five annual changes:

• Bharti Airtel

• ITC

• Motherson Sumi

No company had a clean, uninterrupted decline. GlaxoSmithKline Pharmaceuticals came closest; its cash flow fell from 2010 to 2013, but its next reported figure covered a 15-month reporting-transition period and rose in raw terms, so a decline in every annual step cannot be confirmed.

Most companies fell somewhere in between.

How the 72 companies were distributed

As the table shows, 60 companies had mixed cash flows that rose in some years and fell in others. Another 9 reported negative operating cash flow at least once or were turnaround cases.

That was our first big takeaway.

Investors often picture a healthy business as one where cash flow rises smoothly year after year.

But real businesses rarely work that way.

Operating cash flow can rise or fall depending on when customers pay, how much inventory the company holds, when suppliers are paid and other short-term working-capital changes.

So even a strong company can have a weak cash flow year.

That is why an uninterrupted rise in cash flow is not normal.

It is rare.

The 3 companies with continuous cash-flow growth had very different stock outcomes

All 3 companies showed the same pattern before July 2015. Their operating cash flow increased in every annual step.

But their stock prices behaved very differently.

Motherson’s cash flow grew more than 8 times, while its stock had already risen more than 5 times.

The same cash-flow pattern produced 3 very different market reactions.

In Airtel’s case, the stock lagged. ITC’s improvement was rewarded. Motherson’s stock had already risen sharply.

So even a perfect cash-flow trend was not enough to predict how the stock would behave.

Three companies were still too few to draw a broad conclusion. So we expanded the analysis.

What happened when we looked at more companies?

We first removed 9 companies that had negative operating cash flow in at least 1 year. That left 63 companies with positive cash flow throughout.

We then removed another 8 companies because their stock-price data was affected by major corporate actions or could not be compared properly.

That left us with 55 companies for the final analysis.

We grouped them based on the overall direction of their operating cash flow:

By July 2026, here’s what we found.

The companies with broadly rising cash flow performed the best overall. 16 out of 32 beat the Nifty 100, compared with just 3 out of 10 in the falling cash-flow group and 5 out of 13 in the no-clear-trend group.

In this study, companies with rising cash flow performed the best overall.

But the study included only 55 companies, and the difference between the groups was not strong enough to prove that rising cash flow can reliably predict better returns.

So it may be a useful observation, but it should not be treated as a dependable stock-picking rule.

But the advantage wasn’t overwhelming. Even in the best-performing group, 16 out of 32 companies still failed to beat the benchmark.

In other words, rising cash flow was a useful signal, but it wasn’t enough to consistently pick winning stocks.

The result changed depending on when it was measured

The table below shows how many companies in each group were beating the Nifty 100 at different checkpoints.

The 1-year results were actually quite surprising. 9 out of 10 companies with falling cash flow beat the Nifty 100. But that early lead didn’t last.

By the 5-year mark, only 1 out of 10 was still ahead of the index. By July 2026, the number had increased only slightly to 3 out of 10.

Companies with rising cash flow followed the opposite path. They performed better over longer holding periods. Even then, the signal wasn’t overwhelming. By July 2026, only 16 out of 32 companies, or 50%, had beaten the Nifty 100.

The stock’s earlier performance also mattered

We then looked at the 32 companies whose operating cash flow was broadly rising before July 2015. Out of these, 23 stocks had already beaten the Nifty 100, while 9 had lagged the index.

Among the 23 stocks that had already beaten the Nifty 100 before July 2015, only 10 beat the index again over the next 11 years.

The 9 stocks that had previously lagged did better. Of these, 6 went on to beat the Nifty 100.

So, in this small sample, companies with rising cash flow did better when their stocks had not already performed strongly.

But this needs to be read carefully.

There were only 9 companies in the lagging group. And a stock may lag the market for many valid reasons. The company may have weak profit growth, rising debt, industry problems or concerns about what happens next.

So this does not mean every stock with rising cash flow and weak past performance is automatically a good investment.

It only tells us that earlier stock performance may have mattered. The study did not directly check whether these stocks were cheap or expensive.

Conclusion

We started this study with a simple question.

If a company keeps generating more operating cash, does that make it a better investment?

The study clearly suggests that cash flow alone is not enough.

Companies with rising cash flow performed better overall than those with falling cash flow. But even in the strongest group, only around half managed to beat the Nifty 100 over the next 11 years.

That is because cash flow tells us only one part of the story. It shows whether the business is bringing in real money from its operations. But it does not tell us whether the stock is expensive, how much debt the company has, whether margins are improving, how management uses the cash, or what investors are already expecting.

That is why a company can keep generating more cash and still disappoint shareholders. The opposite can happen too. Cash flow may weaken for some time while the stock performs well because expectations or future prospects improve.

So cash flow is a good place to begin analysing a company. But it should not be used alone.

Cash flow helps us understand the business. To understand the investment, we also need to look at valuation, debt, competition, capital allocation and future expectations.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer: View Research Analyst disclaimer

Research Analyst Disclaimer

A South Korean car-maker was listing its Indian subsidiary outside of Korean markets for the first time ever.

And that’s not all.

They aimed to raise a record-breaking amount- over Rs 27,000 crore, which made it the largest IPO in the history of Indian markets and the largest IPO in Asian markets in 2024.

This was the Hyundai Motor India IPO which grabbed both headlines and investor attention.

The company had started selling cars in India in 1996 and became the second largest car-maker in India by understanding Indian customers and Indian roads.

Hyundai Motor India was a profitable business when it came up with an IPO. The company’s net profit had grown almost 29% to Rs 6,060.04 crore in the financial year 2024.

But despite being in the spotlight and being profitable, the company listed at below its IPO price. Why did this happen?

Bigger IPOs typically get spoken about a lot, and one would assume that this results in good performance. But do bigger IPOs give investors higher returns?

The Study

We took the top 5 IPOs in terms of size every year, for the last ten years, and looked at their performance on listing day versus over a period of time.

Three companies had to be dropped for want of reliable adjusted price history, so the study covers 47 companies rather than 50. IPO data from final prospectus of companies. Prices from ACE Equities.

When a company enters or lists on public markets, three scenarios can happen. It can list or open at a price higher than its issue price, which is called the ‘listing premium’. Or it can list lower than the IPO price, which is called ‘listing at a discount’.

The issue price is the upper end of the IPO’s price band.

Sometimes companies end up opening at the exact same price as their IPO price, which is called a ‘flat listing’.

Most retail investors subscribe to IPOs hoping to benefit from what is called a ‘listing pop’. Listing pop means that the company lists at a very high premium and allows investors to exit with massive gains.

So it is fair to say that for many investors, listing gains are an important factor to consider an IPO.

Results

Six out of ten times, the biggest IPO of that particular year, failed to make listing gains.

Hyundai Motor India is a recent example of this. The biggest IPO in India ever, listed on the exchanges at a discount of 1.5%.

Another was LIC, which listed 8.6% lower. PayTM, GIC, SBI Cards- biggest IPOs of the years, ended up listing at a discount.

The only exceptions to this were Bandhan Bank, IRCTC and Mankind Pharma. Tata Capital made listing gains but the number was quite small at 1.23%.

Looking at the broader dataset, exceptional listing gains were not common, despite the size of the IPO.Only a handful of companies delivered exceptional listing gains.

Most premiums were modest: 12 companies listed below 10%, seven between 10-20%, and seven between 20-30%.

Only one IPO listed between 40-50%, while just three delivered listing gains of over 100%.

13 companies however, listed at a discount- including Paytm, GIC Re, SBI Cards, Adani Wilmar, Star Health, Angel Broking (Angel One) etc.

Why did only some of these big IPOs see massive listing gains while others failed to make any gains? There are a couple of reasons for this.

Volumes

Larger IPOs usually come to the markets with a huge volume of shares. And while the investor demand for these shares can be strong, the high volume means there is less scarcity.

Think of it this way: if there are millions of Hyundai shares available on listing day, investors can buy them more easily. But if a smaller IPO has far fewer shares available, investors have to compete harder to get them.

Prices are a function of demand and supply, after all. So high volume of shares in big IPOs lead to more availability of shares and in turn can limit listing gains and vice versa.

Valuation

Mega IPOs are often priced at valuations that many investors perceive to be close to their fair value.

So there is less incentive to bid aggressively on listing day, reducing the chances of a listing pop.

This was one of the key concerns in the Hyundai IPO. Investor discussions and brokerage reports spoke on whether the valuations allowed for any scope of gains.

Large OFS component

The IPO is made up of two parts: a fresh issue and an offer for sale. A fresh issue usually means that the company aims to use these funds for growth purposes (setting up a new factory, purchasing equipment, or for general corporate expenses).

An offer for sale, on the other hand, means that existing investors are cashing out.

Even though it doesn’t imply poor performance, some investors prefer a company raising funds to invest in growth over one that provides an exit to shareholders.

Many of the mega IPOs that underperformed on listing day, including Hyundai, LIC, and ICICI Prudential Life, were purely OFS. SBI Cards and Payments had a higher OFS component.

Market conditions

If the markets rose significantly on the listing day of a particular company, then it is likely that the company will ride the wave of investor optimism.

The converse is also true.

When Hyundai listed on the exchanges, in October 2024, the sentiment had begun to shift.

The markets and IPOs had already witnessed two blockbuster years of high returns and many began to believe that the peak was achieved.

Nifty and Sensex made lifetime highs on September 27, 2024 and underperformed for over a year after that before making another high in January 2026.

October 2024 also triggered massive foreign selling in India.

This could have also been a factor for Hyundai’s weak listing.

So while bigger IPOs generate a lot of buzz, its size doesn’t guarantee listing gains.

Factors like high volumes, valuation and the OFS component in the IPO also play an important role. That’s why only some of the mega IPOs deliver on listing.

While listing gains are important, it is not necessary that a company that did not deliver listing gains is not worth investing in at all.

Listing day is only the beginning. Can a company that disappoints on listing day, still make meaningful returns for investors over the long term?

Long term returns

We looked at the company’s performance one year after its IPO listing and its performance till 16 July 2026 since listing to judge its longer term performance.

The one-year results leaned positive, but not decisively.

28 of 44 companies remained higher than the listing day closing price a year later. 16 companies fell below it.

NOTE: All prices used are adjusted closing prices, so stock splits and bonus issues do not distort any return. Ten companies in the study had a split or bonus.

Three of the 2025 IPOs had not completed one year as of 16 Jul 2026, so they are excluded from the one year study.

Six of the 44 companies more than doubled investors’ money within a year of listing.

Here, a multibagger refers to a stock that gained over 100% from its listing day closing price.

The highest one-year return was delivered by Angel One at 396.74%, followed by IREDA at 241.96% and UTI Asset Management at 126.96%.

While some companies made sharp gains, there were many that made much smaller ones.

PB Fintech declined the most, falling 67.71% below its listing day closing price after one year. Paytm, Zomato and New India Assurance were among the other companies that also traded below their listing day price after a year.

The results suggest that listing day performance alone did not determine one-year returns. Some companies that delivered modest or no listing gains went on to outperform over the following year, while several strong listing-day performers failed to sustain those gains.

Clearly, listing day wasn’t everything.

Hyundai, which listed at a discount, was up 25.99% a year later. Bandhan Bank, which listed at a 29.33% premium, had gained only 3.23%.

But do the number of multibaggers also go up in the long term?

To get a better idea about the long term gains after a big ticket IPO, we also looked at the performance of a company since listing.

One limitation of this analysis is that companies listed recently have had less time to generate long-term returns. For companies that listed in 2025, the performance since listing covers less than a year, while for 2024 listings it covers only around one to two years.

Even with this limitation, the longer-term picture appears stronger than the one-year results.

32 of the 47 companies were above their listing day closing price on 16 July 2026. 15 were below it.

17 companies more than doubled from their listing day closing price, emerging as multibaggers. That is nearly three times the number of one-year multibaggers in our study.

Polycab India delivered the highest return, rising 1,307.04% since listing day close. It was followed by Angel One (1,113.41%), Hindustan Aeronautics (685.61%), Fine Organic Industries (516.69%) and Endurance Technologies (326.31%).

Because these companies have been listed for very different lengths of time, absolute returns are not directly comparable. On an annualised basis, Angel One compounded at 54.04% a year over about six years and Polycab at 44.01% a year over about seven.

The number of multibaggers increased from 6 after one year to 17 after listing, indicating that the biggest wealth creators in the dataset generally took longer than one year to emerge.

This was not true for all of them though.

Despite listing at a 29.33% premium and remaining above its listing day price after one year, Bandhan Bank traded 55.07% below its listing day closing price by 16 July 2026, a fall of 9.19% a year.

Vedant Fashions, New India Assurance, Bajaj Housing Finance, Tata Technologies and Swiggy were among the other companies that remained below their listing day closing prices during the study period.

It may look like the scope for gains from bigger IPOs is higher, but that is not true for all of them. Some delivered exceptional returns while others struggled to make it past the listing day price.

But how does that compare with simply buying the index?

A company being above its listing price does not tell us whether it was worth owning. The same money in a Nifty 50 index fund would also have grown over the same years.

But simply rising above the listing-day price does not necessarily mean the IPO was a better investment. The Nifty 50 was also growing during these periods. So we also checked whether each company actually performed better than the broader market over the same holding period.

So we compared every company with the Nifty 50 over exactly its own holding period, on an annualised basis.

22 of the 47 companies beat the Nifty 50. 25 did not.

The median company compounded at 6.02% a year. The Nifty 50, over those same matched periods, compounded at 10.15% a year.

Each dot represents one company. Dots on the right beat the Nifty 50, while dots on the left underperformed it.

Over one year, the picture was better: 26 of the 44 companies beat the index.

So while a majority of these IPOs did rise from their listing day price, a majority of them still did not beat a plain index fund over the same stretch of time.

The ones that did beat it, beat it by a lot. That is what a small number of very large winners does to a set of results.

There are multiple reasons for that.

Business and earnings growth are among the biggest reasons. In other words, if the earnings kept increasing over a period of time, then the prices moved higher.

For example, real estate upcycle and government spending on infrastructure and rural electrification were among reasons that supported Polycab’s growth.

When a company delivers strong consistent growth, investors are willing to pay more for those shares.

Similarly if a sector was doing well, then the company that listed from that sector did well.

The opposite is also true.

Valuation mattered a lot too. Hyundai was perceived to be priced aggressively at the time of the IPO. Some investors waited for the shares to fall and reach a valuation where the shares became an attractive buy for them.

Another factor is the boom in Indian markets post Covid. Indian markets were in a bull run- witnessing consistently rising markets till late 2024.

So while some IPOs that listed in the period were supported by optimism, some of the IPOs that came after 2024 struggled as sentiment turned weak.

What did our study actually show?

Bigger IPOs may attract the most attention, but IPO size alone does not determine returns- neither for listing gains nor for long term gains.

This was probably the biggest takeaway from our study.

Just because an IPO made money doesn’t mean it was a great investment. Quite a few stocks went up after listing, but still couldn’t beat a simple investment in the Nifty 50.

Some IPOs turned into massive winners. Some never even got back above their listing price. And many did make money, just not enough to beat the broader market.

The interesting part is that this had very little to do with how big the IPO was.

What mattered was everything that happened after the listing. Did the business grow? Did profits improve? Was the stock too expensive to begin with? Did the company deliver on what it had promised?

So the takeaway is pretty simple.

A big IPO can create a lot of excitement. But that doesn’t automatically make it a good investment. The size of the IPO tells you how much money the company raised. It doesn’t tell you how much money you’ll make. Over the long run, what matters is the quality of the business and the price you pay for it.

Note

• This study covers only the five largest mainboard IPOs from each year and is not representative of all IPOs.

• IPOs were selected within each calendar year, so companies are not directly comparable by issue size across years.

• Hexaware Technologies, ICICI Securities and Equitas Holdings were excluded and not replaced, leaving a final sample of 47 companies.

• The study counts outcomes (such as stocks that rose, beat the Nifty or became multibaggers). It does not simulate an investable portfolio.

• Listing gains assume an investor received an IPO allotment and sold at the opening price. In practice, allotment is not guaranteed.

• Long-term returns are based on the adjusted listing-day closing price. Actual investor returns may differ depending on the purchase price.

• Companies have different holding periods. CAGR improves comparability but does not eliminate differences in market cycles.

• Only 44 companies had completed one year of trading by 16 July 2026, so one-year analysis excludes the remaining three.

• A few exceptional performers can skew overall results. Most IPOs did not generate multibagger returns.

• Nifty comparisons use the Nifty 50 Price Index (not TRI). Dividends, brokerage, taxes and other transaction costs are excluded.

• The study is descriptive, not causal. It does not identify why certain IPOs outperformed or underperformed.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

The problem was called Y2K.

Many old computer systems had stored years using only two digits. So 1998 was stored as 98, 1999 as 99, and the fear was that when the year changed to 2000, some systems might read 00 as 1900 instead of 2000.

For banks, airlines, governments and large companies, that was not a small software bug. It could affect payments, records, bookings and critical business systems.

Companies around the world suddenly needed programmers who could inspect old code, fix systems and make sure nothing broke when the calendar moved to 1 January 2000.

This became a huge opportunity for Indian IT companies.

Basically, Indian firms had the engineers and global companies had the problem.

These global clients paid Indian IT companies in US dollars. At the same time, Indian IT companies built large development centres in India, where much of the work could be done at a lower cost.

This created the business model that companies like Infosys, TCS, Wipro and HCLTech still follow today. They earn a large part of their revenue from clients in the US, Europe and other overseas markets, while many employee salaries and other operating costs are paid in rupees in India.

And this is where the weak-rupee story comes from.

Indian IT is one of India’s most export-driven industries. According to RBI’s software-services export survey, India’s software-services exports were estimated at $204.7 billion in 2024-25, up 7.3% from the previous year. The survey covered companies that together represented around 90% of India’s total software-services exports, so the data gives a broad view of the industry and is not based on a small sample.

The dollar link is also real. In 2024-25, the US was still the largest destination for India’s software exports, with a 52.9% share, and the US dollar was the main invoicing currency, making up 72% of India’s software exports.

So when people say Indian IT earns in dollars, they are not wrong.

A large part of the industry’s revenue comes from overseas clients. A large part of that is invoiced in dollars. And these companies finally report their financial results in Indian rupees.

That creates a simple accounting effect.

Imagine an Indian IT company earns $1 million from a client in the US.

If the exchange rate is Rs 70 per dollar, that revenue is worth Rs 7 crore.

But if the rupee weakens and the exchange rate moves to Rs 80 per dollar, the same $1 million is now worth Rs 8 crore.

Nothing has changed for the client.

They are still paying $1 million.

The company has not won a new contract. It has not done any extra work either. The only thing that has changed is the exchange rate.

But because the rupee has weakened, the same dollar revenue becomes more valuable in rupee terms. That is why investors often believe a weaker rupee benefits Indian IT companies.

This is where the business story turns into an investing belief: if the rupee weakens, IT stocks should benefit too.

The logic is not wrong. A weaker rupee can help Indian IT companies because dollar revenue converts into more rupees.

But the investing question is not whether the dollar matters to Indian IT companies.

It clearly does.

The question is whether this relationship was useful for investors.

If the rupee weakened and an investor bought IT stocks because of that, did it actually work?

We decided to test it.

What we did…

For this study, we used daily data from 1 January 2010 to 2 July 2026.

We used three series:

- USD/INR

- Nifty IT TRI

- Nifty 50 TRI

USD/INR tells us how the rupee moved against the dollar. If USD/INR rises, it means one dollar costs more rupees. So the rupee has weakened. If USD/INR falls, it means one dollar costs fewer rupees. So the rupee has strengthened.

Nifty IT TRI was used to represent the Indian IT sector. Nifty 50 TRI was used as the broader market benchmark.

This benchmark is important.

If Nifty IT rises by 8%, that sounds good. But if Nifty 50 rises by 15% in the same period, then IT did not really do well compared to the market. It went up, but it still lagged.

So we checked two things:

1. Did Nifty IT rise during rupee moves?

2. Did Nifty IT beat Nifty 50 during those same moves?

Now, we first identified every major period when the rupee either weakened or strengthened against the US dollar.

A rupee-weakening wave is a period when the USD/INR exchange rate rises from a low point to a later high point. A rupee-strengthening wave is the opposite; it is a period when the USD/INR exchange rate falls from a high point to a later low point.

We didn’t want to count every small day-to-day fluctuation as a meaningful currency move. So we only considered moves that met a few conditions.

A wave had to:

- move at least 5% in USD/INR
- last at least 30 calendar days
- end after a 3% reversal or a long stall, meaning the exchange rate stopped making fresh highs or lows for around 90 days
- avoid very slow multi-year drifts, which were separated from sharp visible moves

This gave us the set of moves investors would actually notice on a chart.

Visual: USD/INR daily waves used in the study. Red areas are weakening waves; blue areas are strengthening waves.

When did the rupee make its biggest moves?

Using this method, we found 23 sharp USD/INR waves.

Out of these:

- 14 were rupee-weakening waves

- 9 were rupee-strengthening waves

Visual: Nifty IT minus Nifty 50 during every sharp rupee wave.

The detailed table and chart above show that the relationship was clearly mixed. In some rupee-weakening periods, such as 2013, 2014, 2018 and 2021, Nifty IT performed strongly. In others, such as 2012, 2022 and 2025, it underperformed sharply. So the simple statement, weak rupee means IT stocks rise, does not hold every time.

We needed to count this more carefully.

Did the fall in rupee actually benefit IT stocks?

We identified 14 rupee-weakening waves.

During these periods, Nifty IT rose in only 5 waves and was flat or negative in the remaining 9.

So if the question is, ‘Did IT stocks usually rise when the rupee weakened?’

The answer is no.

This is the first key finding. While a weaker rupee can benefit IT companies by increasing the rupee value of their dollar earnings, IT stocks didn’t consistently move higher during periods of rupee weakness.

But that’s only half the story.

Many of these rupee-weakening periods also coincided with broader market declines. So looking only at whether IT stocks went up can be misleading.

A better question is: Did IT perform better than the broader market?

To answer that, we compared Nifty IT with Nifty 50 across all three currency environments.

When we put the three regimes side by side, the difference becomes clear.

At first, the numbers look a little confusing.

When the rupee weakened, Nifty IT went up only 5 out of 14 times. So a weak rupee didn’t automatically mean IT stocks would rise.

But here’s the interesting part. Even though IT didn’t always go up, it still performed better than Nifty 50 in 9 of those 14 periods. In other words, IT often held up better than the broader market when the rupee was under pressure.

Now look at the rupee-strengthening periods. Nifty IT went up 8 out of 9 times. At first glance, that seems to contradict the common belief that a weaker rupee benefits IT stocks.

The answer is that stock prices are influenced by much more than just the currency.

During those same nine periods, Nifty 50 was positive in all nine and outperformed Nifty IT every single time. So IT wasn’t leading the market, it was simply rising along with a strong broader market while other sectors performed even better.

Visual: During rupee-strengthening waves, IT often rose but still lagged Nifty 50.

This doesn’t prove that a stronger rupee caused IT to underperform. But it does show that the currency story didn’t disappear. A stronger rupee didn’t stop IT stocks from going up, it simply coincided with the broader market delivering even stronger returns.

That isn’t entirely surprising.

Rupee-strengthening periods often occur during calmer or stronger economic conditions. In such environments, domestic sectors like banks, autos, industrials and other cyclicals tend to perform well. IT stocks can still generate positive returns, but they may not keep pace with the rest of the market.

This is why comparing IT with Nifty 50 is so important. Looking only at IT’s returns might suggest that IT performs well when the rupee strengthens. But once we compare it with the broader market, the picture becomes much clearer: IT stocks often went up, but they lagged the broader market every single time.

Could investors have actually used the falling rupee as a signal?

There is one practical problem with studying a rupee fall from its beginning to its end: the starting point is only obvious in hindsight.

An investor watching the market in real time would notice the move only after the rupee had already weakened significantly.

So, for every rupee-weakening wave, we identified the date when USD/INR had risen by 5% from the start of the move. We called this the confirmation date, the point when the fall had become large enough for an investor to notice.

We then asked, If an investor bought Nifty IT on the confirmation date, did it beat Nifty 50 over the next 1, 3, 6 and 12 months?

The table also shows how much IT beat or lagged Nifty 50.

For example, if Nifty IT returned 12% and Nifty 50 returned 7%, IT was ahead by 5 percentage points. Similarly, if Nifty IT returned 3% and Nifty 50 returned 8%, IT was behind by 5 percentage points.

The results were mixed.

After one month, IT beat Nifty 50 in 5 of 14 cases. After three months, it did so in 7 of 14 cases. At six months, the result weakened, with IT outperforming in only 6 of 14 cases.

The 12-month result looked stronger, with IT beating Nifty 50 in 9 of 13 cases. But one year is a long period, and by then several other factors (such as earnings, valuations, US technology spending and overall market conditions) could have influenced returns.

So the signal was not clean. If investors waited until the rupee fall became visible and then bought IT, the outcome depended heavily on the holding period.

There is also one technical limitation.

Some rupee waves only moved slightly more than 5%. In those cases, the confirmation date came very close to the end of the currency move. By the time the investor noticed the fall, most of the move had already happened.

That is why we did not treat the return from the confirmation date to the end of the wave as the main evidence.

Instead, the cleaner test was to measure Nifty IT’s performance against Nifty 50 over fixed 1-, 3-, 6- and 12-month periods after confirmation.

The takeaway is simple: a visible fall in the rupee was not a reliable signal to buy IT stocks.

What happened when the rupee stayed range-bound?

We also looked at periods when USD/INR was not moving strongly in either direction.

These were range-bound periods, when the rupee was neither weakening sharply nor strengthening sharply.

There were seven such periods.

During them:

• Nifty IT was positive in 5 of 7 cases.

• Nifty 50 was also positive in 5 of 7 cases.

• Nifty IT beat Nifty 50 in only 3 of 7 cases.

This comparison is useful because it shows that IT stocks do not need a falling rupee to rise.

IT may perform well because company earnings are improving. It may benefit from strong technology spending by global clients. It may rise because valuations are increasing or because the broader market is doing well.

At the same time, the fact that IT beat Nifty 50 in only 3 of the 7 range-bound periods suggests that a stable rupee did not give the sector any clear advantage either.

Taken together, the results tell us that currency is only one of many forces affecting IT stocks.

A falling rupee can help IT companies at the earnings level, but it was not a reliable signal for investors to buy IT stocks. And IT could still rise even when the rupee was not weakening.

The rupee matters, but it does not decide the entire outcome.

What did we actually learn?

The popular belief is partly correct.

A weaker rupee can help Indian IT companies because a large share of their revenue comes from overseas clients and is often billed in foreign currency. When those dollars are converted back into rupees, reported revenue and margins can receive a boost.

That is the business effect.

But the share-price effect is different.

Stocks do not move only because accounting numbers improve. They move based on expectations. Also, many IT companies hedge part of their foreign-exchange exposure using forwards, options and other derivatives. That can smooth or delay the benefit of a weaker rupee. If investors are worried about US client spending, weak deal conversion, margin pressure, AI disruption, or high valuations, then the currency benefit may not be enough.

That is exactly what the data showed.

During the 14 sharp rupee-weakening waves in our study, Nifty IT was positive only 5 times. So a falling rupee did not automatically mean IT stocks made money.

But Nifty IT still beat Nifty 50 in 9 of those 14 periods. So the weak rupee did seem to offer some relative support. IT often held up better than the broader market, even when it did not rise in absolute terms.

This means the rupee effect worked more like a relative advantage than a direct buy signal.

It helped the sector’s position compared to the market.

It did not guarantee that investors would earn positive returns.

The signal also became less reliable once the rupee fall became obvious. By the time investors saw the currency move clearly, part of the benefit may already have been priced in. At that point, other forces started mattering more: demand, valuations, margins, global tech sentiment, US client budgets, and the broader market cycle.

So the final answer is that a weak rupee can support IT earnings. It can make reported rupee numbers look better. It can sometimes help Nifty IT outperform the broader market.

But it is not enough, on its own, to justify buying IT stocks. The rupee is a tailwind, not a magic switch.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

It carries emotion, almost a tradition, a backup plan, family security, and so much more.

It is bought during weddings, festivals, Akshaya Tritiya, Dhanteras, or simply because someone in the family says, ‘Gold is gold’.

It will always have value.

And honestly, that thinking has been around for generations. When markets fall, currencies weaken, or life feels uncertain, gold has always had this reputation of being the asset people trust.

But the way we buy gold has changed a lot.

Earlier, buying gold meant going to a jewellery store, asking for the day’s rate, checking designs, paying GST, paying making charges, and finally taking home the gold.

It felt real because you could hold it in your hand. But it also came with the usual issues: extra charges, purity doubts, storage tension, and the fear of, “Where do we keep this safely?”

Around 2002, Benchmark Asset Management Company, which had launched India’s first ETF, Nifty BeES, in 2001, looked at India’s obsession with gold and filed a proposal with SEBI for a gold ETF in May 2002.

And suddenly, gold became a stock-market product. Instead of buying physical gold, investors could buy units of a Gold ETF on the exchange. These ETFs were designed to track the price of gold and could be bought and sold like shares.

But that created a new problem.

To buy a Gold ETF, an investor needs a demat account and has to place orders on the exchange. They also have to think about market price, liquidity, bid-ask spread, and whether the ETF is trading close to NAV.

For many long-term SIP investors, it can feel like extra work.

Which leads us to Gold Fund of Funds.

A Gold FoF, or Fund of Fund, is a mutual fund that does not directly buy physical gold. Instead, it invests in a Gold ETF, usually from the same fund house.

So, when an investor puts money into a Gold FoF, their money indirectly goes into a Gold ETF. This gives them gold exposure through the normal mutual fund route, without needing to buy the ETF directly through a demat account.

This is convenient, but it raises a simple question:

If the FoF ultimately invests in the ETF, why not buy the ETF directly?

Would the ETF investor earn more by skipping the extra FoF layer? Or would the difference be small enough for the FoF’s convenience to be worth it?

We tested this using actual data.

The Experiment

We compared two gold FoF and gold ETF pairs.We chose FoF and ETF pairs from the same fund house so the gold exposure stayed almost identical. The only real difference was the route: mutual fund SIP versus direct ETF purchase on the exchange.

The study period was from 1 January 2016 to 3 July 2026. We used 2,536 common trading days where all required data was available.

Each investor put in Rs 10,000 every month on the first trading day of the month. This gave us 127 monthly investments, or a total investment of Rs 12.7 lakh.

For each fund house, we tracked three routes.

First, the Gold FoF route. Investor A invested Rs 10,000 every month in the Gold FoF at that day’s NAV. Since mutual funds allow fractional units, the full amount was invested each month.

Second, the Gold ETF route. Investor B used Rs 10,000 every month to buy the Gold ETF at its actual NSE closing price on the SIP date. Since ETFs trade like shares, only whole units could be bought. Any leftover cash was carried forward to the next month.

Third, we added an ETF-at-NAV reference line. This was not a real investor route. Here, we assumed Rs 10,000 was invested every month at the ETF’s NAV, with fractional units allowed.

This third line was added to separate the product’s NAV movement from market-price effects. ETFs have both an NAV and a market price. Sometimes they trade above NAV, called a premium. Sometimes they trade below NAV, called a discount. The ETF-at-NAV line helps us see whether the difference came from the FoF structure or from the ETF being bought in the market at a premium or discount.

We also checked whether these FoFs were actually investing in their own Gold ETFs. And yes, they were.

Nippon India Gold Savings Fund had about 99.88% of its money in its own Gold ETF, while SBI Gold Fund had about 99.93%.

So, in practical terms, both FoFs were almost entirely invested in their own Gold ETFs, with only a very small amount kept as cash or other assets.

All FoF and ETF NAVs were taken from ACE MF. ETF market prices were actual NSE closing prices.

Study 1

First, let us look at the Nippon pair.

The ETF route finished ahead by Rs 30,387. At first, that may sound like a meaningful difference. But when you look at the final corpus of nearly Rs 39 lakh, the gap becomes quite small. Over more than ten years, this difference works out to only about 0.78%.

Now, if we look at the ETF NAV line, the picture becomes clearer. If the ETF had been valued strictly at its NAV, it would have finished at Rs 38,84,138. The Gold FoF, on the other hand, finished at Rs 38,84,361. The difference between the two is just Rs 224, which is almost negligible.

This tells us something important. In the Nippon case, the extra layer in the FoF structure did not really reduce returns. The FoF and the ETF NAV moved almost in line with each other.

So then, why did the actual ETF investor still end up slightly ahead?

The reason lies in the market price. The ETF was often available in the market at a price lower than its NAV, which gave the ETF investor a small advantage over time.

Gold BeES traded below NAV on 1,767 out of 2,536 trading days.

More importantly for our SIP investor, it traded below NAV on 91 out of 127 SIP dates.

This means the ETF investor was often buying the ETF slightly cheaper than its actual underlying value.

Across the SIP dates, Investor B received units worth about Rs 6,159 more at NAV than what she actually paid at the market price.

That small repeated discount helped the ETF route finish ahead.

So in the Nippon case, the ETF did not win because the FoF was bad.

The ETF won because the market often allowed the investor to buy it slightly below NAV.

Study 2

Now let us look at the SBI pair.Here also, the ETF route finished ahead, but only by a very small margin.

The ETF investor ended with Rs 39,58,799, while the FoF investor ended with Rs 39,50,416. That is a gap of just Rs 8,383, or about 0.21%.

So at first glance, both routes look almost the same.

But the ETF NAV line shows something interesting. If the ETF had been valued at NAV, it would have finished at Rs 39,08,357, which is lower than the FoF’s Rs 39,50,416.

At first, this looks a bit confusing. If the FoF is simply investing in the ETF, then why is the FoF NAV higher than the ETF NAV?

To understand this, we need to look at one more comparison.

From the first date to the last date, the growth looked like this:

So yes, SBI Gold Fund NAV grew faster than SBI Gold ETF NAV.

But it did not grow faster than the ETF’s market price. That is the key point.

The FoF only looked better when compared with the ETF NAV reference. Compared with the actual ETF market-price path, it was slightly behind, which is what we would expect from an extra FoF layer.

So why did the FoF beat the ETF NAV reference?

The answer likely lies in how the FoF itself is valued.

A Gold FoF holds ETF units. Mutual fund NAVs are usually calculated using the closing market value of the securities they hold. For ETFs, this typically means using the closing market price on the exchange.

So the FoF NAV may not track the ETF’s own NAV exactly. Instead, it may track the ETF’s market closing price more closely.

And the SBI data supports this clearly.

When we compare daily returns, the SBI Gold Fund NAV tracks the ETF’s market close much more closely than it tracks the ETF NAV.

A 0.9995 correlation with the ETF market close is extremely high. In simple terms, the FoF NAV was moving almost exactly like the ETF’s market price, not like its NAV.

This changes how we should read the result. Comparing FoF NAV with ETF NAV does not cleanly show the cost of the FoF layer. The ETF NAV shows the value of gold inside the ETF, while the FoF NAV appears to follow the ETF’s market price, along with small cash holdings.

This is why the final order looks like this:

ETF market price > FoF NAV > ETF NAV reference

In simple terms, both real routes were closer to the ETF’s market-price path than to the ETF NAV path. The ETF NAV line is useful as a benchmark, but it was not the actual path followed by the FoF investor.

Now let us look at the ETF’s market-price behaviour.

Across the SIP dates, Investor B picked up units worth about Rs 9,385 more at NAV than what she paid in the market.

That discounted buying was enough to cover the gap between the FoF NAV and ETF NAV, and still pushed the ETF route slightly ahead.

So again, the ETF’s small edge came from market pricing.

Why the ETF price moved away from NAV

The main reason behind the ETF’s small advantage was simple: an ETF has two prices.

One is the NAV. This is the value of the gold held by the ETF.

The other is the market price. This is the price at which people actually buy and sell the ETF on the stock exchange.

These two prices do not always match.

If the market price is below NAV, the ETF buyer gets gold exposure slightly cheaper than its actual value. If the market price is above NAV, the buyer pays extra.

In this study, investors got the cheaper deal more often.

On roughly seven out of every ten SIP dates, both ETFs were trading below NAV. That means the direct ETF investor was usually buying gold exposure at a small discount.

The early years made this even more useful. On 1 July 2016, Gold BeES traded 4.64% below NAV, while the SBI ETF traded 5.37% below NAV. So for the same Rs 10,000, the ETF investor got slightly more gold value. And when gold prices rose later, those extra units also benefited.

But this discount was not permanent.

During Covid, the situation flipped. Investors rushed towards gold ETFs, demand jumped, and ETF prices moved above NAV.

On 13 April 2020, the SBI ETF traded 14.62% above NAV. Our SIP investor did not buy on that exact day, but the pressure was still visible. On 4 May 2020, the investor paid around 3.75% above NAV in Nippon and 4.01% above NAV in SBI.

So the same price gap that helped the ETF investor for most of the period became a disadvantage when demand was very high.

The two ETFs also behaved differently because of liquidity.

Gold BeES was more actively traded, so its price stayed closer to NAV. During the study period, it moved from about 5.8% below NAV to 6.3% above NAV.

SBI ETF traded less, so its price moved around more. It swung from about 6.4% below NAV to as high as 14.6% above NAV.

That is the key takeaway. Liquidity makes a big difference.

A heavily traded ETF usually stays closer to NAV. A less traded ETF can move further away. Sometimes that gives you a bigger discount. But sometimes, it also means paying a much bigger premium.

Why does the FoF still exist?

If the ETF route finished slightly ahead, why would anyone choose the FoF?

The answer is convenience. A Gold FoF works like a regular mutual fund SIP. There is no need to place exchange orders, check premiums or discounts, think about liquidity, or worry about whole-unit buying. The full SIP amount gets invested and fractional units are allotted.

The ETF route offers more control, but it also comes with execution issues. The ETF may be bought below NAV, which helps the investor, or above NAV, which hurts. There can also be bid-ask spreads, brokerage, demat charges, and exchange-related costs, which were not included in this study.

In this study, the whole-unit issue did not matter much. By the end, the Nippon ETF route had only about Rs 102 left as uninvested cash, and the SBI ETF route had only about Rs 11 left. So leftover cash was not a major reason for the difference.

But the larger point remains.

A Gold ETF and a Gold FoF may give exposure to the same gold, but the experience of buying them is very different.

The real takeaway

Over ten and a half years, both routes worked very well. An investment of Rs 12.7 lakh grew to roughly Rs 39 lakh in both cases, which shows that gold itself did most of the heavy lifting.

The ETF route finished ahead in both comparisons, but only slightly. In Nippon, the edge was Rs 30,387, or about 0.78%. In SBI, it was Rs 8,383, or about 0.21%.

So the study does not show that FoFs failed. It shows that when the underlying gold exposure is almost the same, the final outcome can be very close.

The difference was not really about gold.

It was about the route used to access gold. The FoF route offered simplicity through a regular mutual fund SIP. The ETF route was slightly more efficient in this period, but it required buying on the exchange, where prices can move above or below NAV.

During this decade, the ETF route got a small advantage because the ETFs often traded below NAV. This allowed direct ETF investors to buy at a slight discount. But this was not guaranteed. During periods like the Covid shock in 2020, ETFs traded above NAV, making the ETF route temporarily more expensive.

For investors who want a simple monthly gold SIP, the FoF route works well. For investors who are comfortable with demat accounts, liquidity, and market-price movements, the ETF route was slightly more efficient in this study period.

In the end, the difference was not really about gold. It was about the path used to access the same gold exposure.

Note

• Gold FoF allows fractional units, so the full SIP amount gets invested

• ETF route uses only whole units; leftover cash is carried forward

• ETF NAV line assumes buying at NAV with fractional units; used only as a reference, not a real route

• ETF prices used are closing prices; actual buy price may differ during the day

• Brokerage, STT, demat charges, exit load, tax, and platform costs are not included

• SBI FoF NAV grew faster than its ETF NAV; exact reason unclear from NAV data alone

• Four incorrect ETF prices from Yahoo were corrected using NSE data (split-adjustment errors)

• Both ETFs had 1-for-100 splits (Nippon: Dec 2019, SBI: Jan 2022); adjustments made

• Real market events like 2020 premiums and 2016 discounts are included

• Study covers only two fund houses and two ETFs; less liquid ETFs may behave differently

• Physical gold, jewellery costs, GST, and making charges are not part of this study

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

The Union Budget had already been presented almost two months earlier. Most investors assumed the major tax changes for the year were already known. But while Parliament was discussing the Finance Bill, one more amendment was introduced.

It changed how many debt mutual funds would be taxed.

Now, that may sound like a technical change. But for investors who compared debt funds with bank fixed deposits, it mattered a lot.

For years, one of the biggest reasons investors preferred debt mutual funds over FDs was not just returns. It was tax.

To really see why this mattered, we need to take a step back and look at how things used to work before 2023.

Suppose you have Rs 1 lakh to invest. You can either put it in a bank fixed deposit or invest it in a debt mutual fund.

At first glance, these look like very different products. An FD is a bank deposit. A debt fund is a mutual fund.

But if you zoom out, both are connected to lending. When you put money in an FD, the bank uses its pool of deposits to lend and invest. When you invest in a debt mutual fund, the fund buys debt securities issued by companies, banks, PSUs, or the government.

So in broad terms, both are trying to earn returns from debt.

But the tax treatment was very different.

In an FD, the interest is taxed at your income-tax slab. So if your FD earns Rs 80,000 in interest and you are in the 30% slab, that interest is taxed at 30%. In other words, every year, a portion of the interest your FD generates goes toward paying taxes, reducing your effective return.

Debt mutual funds worked differently under the old rules.

If an investor stayed invested for more than 3 years, the gains could qualify as long-term capital gains. And that came with an important tax benefit called indexation.

Now, indexation sounds like a complicated tax term, but the idea was actually quite simple.

Before calculating your tax, the government allowed you to increase your original purchase price to account for inflation using something called the Cost Inflation Index.

Why did that matter?

Because if your purchase price became higher, your taxable profit automatically became smaller. And if your taxable profit was smaller, you paid less tax.

For someone in the 20% or 30% tax bracket, it meaningfully improved the final after-tax return.

That is why the FD vs debt fund comparison was never only about returns. It was also about post-tax returns.

A debt fund did not always need to beat an FD by a huge margin before tax. For a higher-tax investor, the old tax treatment itself could improve the final result.

Then came the 2023 amendment.

For fresh investments made on or after 1 April 2023, the old indexation benefit was removed for most debt mutual funds. Instead, the gains would be taxed according to the investor’s income-tax slab.

So the obvious question was, if debt funds lost their tax advantage, did fixed deposits become better?

But to answer that properly, we cannot look only at the tax rule.

We also need to look at what actually happened to returns.

Because even after losing a tax benefit, a debt fund could still beat an FD if its return was high enough. And even with a tax benefit, a debt fund could still lose if its return were weak.

So we tested this using historical FD rates, debt fund NAVs, and post-tax returns.

The Experiment

We assumed an investor had Rs 1 lakh to invest on 1 April each year.

The investor had two choices:But we did not randomly choose funds.

For every 1 April investment date, the investor looked at the previous financial year’s best-performing fund in that category and invested in that fund.

So if the investment date was 1 April 2020, the fund was selected based on its performance from the previous financial year. Then the investor held it for the full comparison period.

This reflects a common investor behaviour: looking at last year’s return table and choosing the fund that performed best.

We then compared that selected fund with an SBI FD over the same holding period.

For the 1-year part, we used liquid funds because investors often use them to park money temporarily.

For the 3-year part, we used Banking & PSU Funds because they are one of the cleaner open-ended debt fund comparisons for a conservative FD investor.

They are still not FDs. The return is not fixed, and the NAV can move. But these funds mainly invest in debt issued by banks, PSUs, and public financial institutions, so they are more suitable for this comparison than funds like Credit Risk, Gilt, Dynamic Bond, or Medium Duration Funds.

We also kept the FD comparison clean.

There was no FD rollover. If the holding period was one year, the comparison ended after one year. If the holding period was 3 years + 1 day, it ended after 3 years + 1 day.

This way, we were not asking what happens if someone keeps renewing FDs for many years.

We were asking a simpler question:

If an investor knows they need the money after one year or after three years, would the selected debt fund leave them with more money than an SBI FD over the same period?

How tax was applied

For this study, we focused on a 30% tax slab investor.

We chose this because the old debt fund tax advantage was most significant for higher-tax investors. Interest earned on fixed deposits (FDs) is taxed at the investor’s income-tax slab, so someone in the 30% slab pays much more tax on FD interest than someone in the 5% slab.

For debt funds, the tax rule depended on the investment date.

This is important because the 2023 tax change does not affect all parts of the study equally.

For Liquid Funds, the holding period was only one year, so indexation was not really relevant even under the old rules.

For Banking & PSU Funds, the holding period was 3 years + 1 day, so the old indexation benefit mattered for investments made before 1 April 2023.

That is why the study has two parts.

The Liquid Fund section checks whether a short-term debt fund meaningfully beat a 1-year FD.

The Banking & PSU section checks what changed when the old 3-year debt fund tax advantage was removed.

Result

What happened in the 1-year category?
Liquid Fund Vs FD

For the 1-year comparison, we focused only on Liquid Funds.

This is not the same as an FD. In an FD, the bank promises a fixed return. In a Liquid Fund, the fund invests in very short-term debt and money market instruments, and the return comes through NAV movement. So the return is not fixed.

But for short-term parking, Liquid Funds are one of the closest debt fund comparisons because investors often use them when they want to keep money for a short period without taking equity-like risk.

Here, the investor picked the previous financial year’s best-performing Liquid Fund on every 1 April and held it for one year. The same ₹1,00,000 was also invested in a 1-year SBI FD.

The returns below are total post-tax returns over the full 1-year holding period, not annualised comparisons beyond that period.

The result is mixed.

The selected Liquid Fund beat the FD in 5 out of 10 years. The FD beat the selected Liquid Fund in the other 5 years.

More importantly, the difference was usually small. In 2019, Quant Liquid Plan beat the FD by just 0.02 percentage points. That is barely a meaningful gap. In 2025, Sundaram Liquid Fund was selected because it was the previous year’s best-performing Liquid Fund, but it still lost to the FD by around 0.52 percentage points.

So this does not show one clear winner.

It shows that for 1-year money, picking last year’s best Liquid Fund did not reliably beat a 1-year SBI FD. Sometimes it worked. Sometimes it did not. And most of the time, the gap was not very large.

The tax-change angle is also different here.

Because this was only a 1-year holding period, indexation was not really relevant even under the old debt fund tax rules. So the 2023 tax change does not explain much in the Liquid Fund result.

The takeaway is that over a 1-year horizon, choosing the previous year’s best-performing Liquid Fund did not provide a reliable advantage over an FD. The winner varied from year to year, and the return gap was usually too small to be decisive.

If Liquid Funds are more flexible, why use an FD at all?

This is the natural question.

If the difference in returns is usually small and liquid funds offer greater flexibility, why would anyone still choose a fixed deposit?

Because the products solve different problems.An FD exists because many investors want certainty. They know the rate before investing, they know the maturity date, and they know what they will receive if they hold till maturity. For someone who needs money exactly after one year and does not want NAV movement, an FD is simpler.

A liquid fund exists for a different reason. It is useful when the investor wants to park money but is not sure exactly when it will be needed. The money may be required in a few days, a few weeks, or a few months. In that case, flexibility matters more than locking into a fixed maturity.

So the choice is not just about return.

If the investor wants a known return and a fixed date, the FD fits better.

If the investor wants flexible parking and can accept small NAV movement, the Liquid Fund fits better.

That is why both products exist.

In our data, the selected Liquid Fund beat the FD in 5 out of 10 years and lost in the other 5. The gap was usually small. So the result does not prove that one product is clearly better. It only shows that, for 1-year money, the return difference was not strong enough to make the decision purely about returns.

What happened in the 3-year ?

Banking & PSU fund Vs FD

For the 3-year comparison, we focused on banking & PSU funds.

This is not the same as an FD. In an FD, you are lending money to the bank, and the bank promises a fixed return. In a Banking & PSU Fund, the fund buys debt issued mainly by banks, public sector companies, and public financial institutions. The return is not fixed because the fund’s NAV keeps moving.

But for a conservative FD investor, this is still one of the cleaner debt fund comparisons because the broad exposure is to relatively stable borrowers, not to credit-risk funds, gilt funds, or long-duration interest-rate bets.

Here, the investor picked the previous financial year’s best-performing Banking & PSU Fund on every 1 April and held it for 3 years + 1 day. The same Rs 1,00,000 was also invested in a matching SBI FD for the same period.

The returns below are total post-tax returns over the full holding period, not annual returns.

This result is much cleaner than the 1-year liquid results.

In every completed 3-year period from 2016 to 2023, the selected Banking & PSU Fund beat the matching SBI FD after tax for a 30% taxpayer.

But the important part is not just who won. It is why the fund won.

For the 2016 to 2022 start years, the investment was made before the 2023 tax change. So the fund had two things working for it: the actual return of the fund and the old indexation benefit.

The 2023 row is different.

That investment was made after 1 April 2023. So the old indexation benefit was not available. Under Section 50AA, gains from specified mutual funds acquired on or after 1 April 2023 are taxed at the investor’s applicable slab rate, even if they were held for more than three years.

So UTI Banking & PSU Fund still beat the FD in the 2023 window, but the gap was smaller. The fund gave 16.42% after tax over the full holding period, while the FD gave 14.41%. That is a difference of only 2.01 percentage points.

Now, to see what the tax change actually did, we can run one counterfactual.

What if the old tax benefit had still applied in 2023?

This is only an illustration, because the Apr 2026 redemption would technically need the FY2026-27 CII, and the official CII table currently lists values only up to FY2025-26, where the CII is 376.

So using the latest available CII value as a conservative placeholder, the picture would have looked like this:This is the tax change in one table.

Under the actual post-2023 rule, the selected fund still beat the FD. But it beat it by around Rs 2,009 on a Rs 1 lakh investment.

If the old indexation-style benefit had still been available, the same fund would have beaten the FD by roughly Rs 6,166 using the conservative CII placeholder.

So the 2023 tax change did not flip the winner in this case. The Banking & PSU Fund still came out ahead.

But it clearly reduced the advantage.

That is the real point.

Before 2023, a good 3-year debt fund could beat an FD through both return and tax treatment. After 2023, the same kind of fund has to rely much more on actual performance.

The fund can still win.

But tax is doing much less of the work.

Conclusion

So, what did we actually learn?

The biggest takeaway is that the FD versus debt fund debate was never just about taxes.

Before 2023, the tax rules gave debt mutual funds a meaningful advantage, especially for investors in higher tax brackets. That made it easier for a debt fund to come out ahead of a fixed deposit on an after-tax basis.

The 2023 tax change removed a large part of that advantage.

But that does not automatically mean fixed deposits became the better choice.

In this study, the selected debt fund could still beat the FD in several comparisons. The difference is that the advantage became smaller once the old indexation benefit disappeared.

In other words, the rules of the game changed.

Earlier, a debt fund had two ways to beat an FD. It could earn a higher return, and it could benefit from more favourable tax treatment.

Today, one of those advantages is largely gone. So if a debt fund is going to justify its place against an FD, it has to do so more through actual performance.

That also makes the choice between the two products clearer.

If the priority is certainty, a fixed deposit still does exactly what it always has. The investor knows the interest rate, the maturity date, and the amount they should receive if they hold it till the end.

A debt mutual fund is different. Its return is not guaranteed, but it offers flexibility and the possibility of earning more if the underlying portfolio performs well.

So the question investors should ask today is not just:

“Is a debt fund more tax-efficient than an FD?”

It is this:

“After applying the tax rules that exist today, did the debt fund earn enough to beat the FD over the same period?”

That is the comparison that matters now.

And as this analysis shows, the answer depends on the debt fund category, the holding period, and the return the fund actually delivers.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer

What does this exactly mean?

In a standard Nifty 50 Index Fund, your money is not divided equally among India’s 50 largest companies. Instead, each company receives a different allocation based on its free-float market capitalisation the number of shares available for trading in the market).

Simply put, the larger the company, the larger its weight in the index, and the more of your money gets invested in it.

For example, for every Rs 10,000 invested in a Nifty 50 Index Fund, roughly Rs 1,056 goes into HDFC Bank alone. Another Rs 832 goes into ICICI Bank, while Rs 827 goes into Reliance Industries.

Currently, the biggest 10 companies in the Nifty 50 account for nearly 50% of the entire index!

Source: NSE

This raises an interesting question. What if every company in the Nifty 50 received the same allocation? Would the returns differ?

We decided to test it out.

The Experiment

To test this idea, we compared Nifty 50 Index Funds with Nifty 50 Equal Weight Index Funds from the same fund house.

While the Nifty 50 Index assigns weights based on market capitalization, the Equal Weight Index gives every stock an equal weight of 2%.

The Nifty 50 Equal Weight Index was introduced by the NSE in 2017. Because of that, mutual funds tracking this index are relatively new, with most launched around 2020.

This is why the comparison period is shorter than our previous experiments.

To ensure a fair comparison, we only compared funds within the same AMC. This removes differences arising from fund management practices and allows us to focus purely on the impact of the underlying index methodology.

The fund pairs selected for the analysis were:

• HDFC Nifty 50 Index Fund vs HDFC Nifty 50 Equal Weight Index Fund (Aug 2021 to June 2026)

• DSP Nifty 50 Index Fund vs DSP Nifty 50 Equal Weight Index Fund (Feb 2019 to June 2026)

• ABSL Nifty 50 Index Fund vs ABSL Nifty 50 Equal Weight index Fund (June 2021 to June 2026)

We assumed a simple SIP strategy for comparison: an investor puts Rs 10,000 every month into both the Nifty 50 Index Fund and the Nifty 50 Equal Weight Index Fund. The performance is tracked from the start date of each Equal Weight fund (or the point at which both choices became available to invest in) up to 1 June 2026.

This was done across all three fund house pairs and tracked how the investments grew over time during the respective comparison periods.

Results

Here is what the data showed:

The equal weight index significantly outperformed the standard market-cap index across all three AMCs.

Why did this happen?

Why did this happen?

The answer lies in how both indices behave.

A Nifty 50 Index Fund is driven heavily by a small set of large companies. When these companies perform well, the index rises quickly. But when a few of them underperform, their large weight drags the overall returns.

An Equal Weight Index works differently. Every company contributes equally to performance. This means gains are not concentrated in a few stocks but spread across all 50 companies.

In simple terms, the Equal Weight strategy reduces dependence on a handful of giants and increases exposure to mid-sized companies within the index, many of which go through stronger growth phases over time.

However, this may also come with trade-offs. Equal weight indices may have higher volatility because smaller large-cap stocks are generally more volatile than mega-caps.

Equal weight index also involves more frequent rebalancing, which increases turnover. And importantly, performance is cyclical. This means equal weight does better when the broader market participates, but can underperform when a few large caps drive most of the returns.

Conclusion

The experiment shows that even when the underlying stocks are the same, the way an index is constructed can meaningfully change returns over time.

A Nifty 50 Index Fund is driven by a few large companies, which makes it more concentrated and dependent on their performance. A Nifty 50 Equal Weight Fund, on the other hand, spreads exposure evenly across all 50 stocks, allowing broader participation to play a bigger role in returns.

Neither approach is universally better. Each performs differently depending on market cycles. Sometimes concentration may help, and sometimes diversification within the index works in its favor.

Your investing outcomes are shaped not just by what you own, but also by how those holdings are weighted.

Limitations of the Experiment

• The analysis is based on a specific time period, which may not reflect how the indices perform across full market cycles. Different phases of the market can lead to very different outcomes.

• The period studied was relatively short in equity terms. Long-term performance patterns can change when tested over multiple decades and varying economic conditions.

• Real-world costs such as fund expenses, rebalancing impact, tracking error, and transaction costs are not fully captured in the simplified comparison.

• Past performance in this experiment should not be treated as an indicator of future returns.

The information presented in this post has been compiled and prepared by Groww Invest Tech Pvt Ltd and is intended solely for informational purposes. It is not tailored to any specific investment objectives, financial situations, or needs of any individual investor. The content should not be construed as investment, financial, legal, or tax advice and should not be relied upon as a substitute for professional consultation.

Investing in securities markets involves inherent risks. Investors are advised to carefully review all relevant documents and consider their own risk tolerance before making any investment decisions.

Mutual fund investments are subject to market risks; please read all scheme-related documents carefully. Past performance of financial instruments, schemes, or markets is not indicative of future results.

SEBI Reg Name: Groww Invest Tech Pvt Ltd | SEBI Reg No: INZ000301838 | RA No: INH200008662

Groww Invest Tech Pvt Ltd is registered with the Securities and Exchange Board of India (SEBI) under Registration No: INZ000301838, as a Research Analyst under No: INH200008662, as a Depository Participant under No: IN-DP-417-2019, and with the Association of Mutual Funds in India (AMFI) under ARN-111686.

SEBI registration details: View registered name and registration numbers

Research Analyst Disclaimer