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.

Research Analyst Disclaimer

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.

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.

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 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.

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

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.

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.

Research Analyst Disclaimer

F1 teams had arrived for pre-season testing, ready to show the world their brand-new cars.

And before the season had even begun, one team caught everyone’s attention.

Racing Point, formerly known as Force India.

Their new car looked strikingly similar to the Mercedes that had won the championship the previous year.

The nose looked like a Mercedes. The sidepods looked like a Mercedes. Even the overall aerodynamic concept and design philosophy appeared strikingly similar.

People started calling it “The Pink Mercedes”.

Instead of designing their own car from scratch, Racing Point had used high-resolution photographs and 3D cameras to reverse-engineer and replicate the external aerodynamic surfaces of Mercedes’ winning 2019 car, the W10.

This sparked a major controversy.

The team was fined €400,000 and lost 15 championship points for using Mercedes-inspired rear brake duct designs. However, Racing Point was still allowed to use the parts for the rest of the season.

F1 teams don’t have complete freedom when designing their cars. They all have to follow the same rulebook.

For example, every team must use a similar 1.6-litre turbocharged V6 engine. There are also limits on how much fuel a car can use during a race.

Even with the tyres, teams must follow rules on tyre pressure and temperature set by the FIA and tyre supplier Pirelli.

The wings are regulated too. The FIA specifies how large they can be and how many aerodynamic elements they are allowed to have.

As a result, teams are often working within the same set of constraints.

That’s one reason the cars end up looking surprisingly similar.

The real challenge lies in how teams, despite working within the same constraints, find ways to win championships.

Engineers are always looking for small advantages. A slightly better aerodynamic design, a more efficient cooling system, or a smarter suspension setup can make a difference of a few tenths of a second per lap.

That might not sound like much, but over an entire season, those tiny gains can be the difference between winning races and barely scoring points.

A surprisingly similar dynamic exists in mutual funds.

Just as F1 teams can’t build anything they want, fund managers can’t invest anywhere they want.

Take large-cap mutual funds, for example. SEBI requires large-cap funds to invest at least 80% of their assets in large-cap stocks, which are defined as the top 100 companies by full market capitalisation.

The reason is simple. When people invest in a large-cap fund, they expect their money to go into large, well-established companies. They don’t expect the fund manager to take big risks by investing heavily in smaller companies.

These rules help keep large-cap funds focused on what they’re meant to do and make sure investors get what they signed up for.

But there’s another side to it. Since all large-cap fund managers have to invest mostly in the same group of large companies, they’re often choosing from a pool of roughly 100 stocks.

This makes many large-cap fund portfolios end up looking quite similar. Overlap becomes likely. But that raises an interesting question.

If most large-cap funds own many of the same stocks, do they also deliver similar returns?

Or can funds that look almost identical on paper still produce very different results?

We decided to find out.

The Study

To see how these constraints play out over time, we analysed the performance of large-cap mutual funds over a 10-year period from June 2016 to May 2026. First, we looked at the equity portfolio of every active large-cap fund as of June 2016 and compared each fund against all others to find the pairs with the highest initial similarity.

The idea was to find the fund pairs that looked most similar to each other at the starting point.

For every pair, we checked:

how many stocks Fund A owned, how many stocks Fund B owned, how many stocks were common between both funds, and what percentage of the combined stock list overlapped.

For example, if Fund A held 35 stocks and Fund B also held 35 stocks, and 27 stocks were common between them, then the two funds were clearly holding many of the same companies.

To measure the overlap, we look at something called the Jaccard overlap.

It is a simple stock-level overlap measure.

It does not check whether the funds own the stocks in the same weight. It only checks whether both funds owned the same stock.

Once we found the most overlapping fund pairs from June 2016, we ran a monthly SIP of Rs 10,000 in each of those funds.

The SIP started in June 2016 and continued till May 2026.

It was invested on the first available NAV date of every month. That gave us 120 monthly SIP installments.

The total invested in each fund was Rs 12 lakh.

Final values were calculated as of 10 June 2026.

We also ran the same Rs 10,000 monthly SIP in the UTI Nifty 50 Index Fund to check whether these active funds, despite their overlap, did better or worse than a simple index fund.

The most similar fund pairs in June 2016

When we compared the portfolios, one clear cluster appeared.

JM Large Cap Fund appeared again and again among the most overlapping pairs.

JM and PGIM had the highest overlap. They shared 27 stocks out of a combined 43 unique stocks. That means 62.79% of their combined stock list was common.

Franklin and JM were almost as similar. They shared 30 stocks out of 48.

Bandhan and JM also had more than 60% overlap.

At this stage, an investor could reasonably ask: If two funds share more than half their stocks, why hold both?

That sounds logical.

But the next ten years show why the answer is not so simple.

How similar were they to the index?

The overlap was not only between active funds. We also compared these active funds with the UTI Nifty 50 Index Fund’s June 2016 portfolio.

We compared the June 2016 portfolios of all six active funds against the UTI Nifty 50 Index Fund’s June 2016 portfolio. The index fund held 50 stocks (the full Nifty 50 constituents at that time).

JM Large Cap held 35 stocks in June 2016. Every single one of those stocks was also present in the UTI Nifty 50 Index Fund.

Not most of them. All of them.

PGIM and Bandhan weren’t very different either. Nearly 88% of their holdings were already part of the index. Even ICICI Pru, which was the least index-like fund in this group, had 32 of its 45 stocks in the Nifty 50.

This tells us something important.

These active large-cap funds were not trying to be different by owning completely different companies. A large part of the active decision was in sizing.

How much should be allocated to each stock? Which index stocks are overweight? Which are underweight? And which few non-index stocks should be added?

That’s where active management was happening.

Results

What happened to a Rs 10,000 monthly SIP?

At first glance, the results look fairly similar.

Every fund turned Rs 12 lakh into somewhere between Rs 20.6 lakh and Rs 24.6 lakh. In other words, they all behaved like large-cap equity funds. None massively outperformed, and none dramatically underperformed.

They all moved broadly with the market.

But they weren’t really identical.

ICICI Pru delivered the highest final corpus at Rs 24.59 lakh, beating the UTI Nifty 50 Index Fund by Rs 3.29 lakh. At the other end, PGIM finished with Rs 20.57 lakh, trailing the index by about Rs 74,000.

That means two funds that started with highly overlapping portfolios ended up creating a gap of roughly Rs 4.03 lakh on the same Rs 12 lakh investment.

So while these funds often owned many of the same stocks, the outcomes were still meaningfully different.

Pair-by-pair, what changed?

Now let us look at the exact fund pairs.

Some fund pairs stayed remarkably close despite a decade of investing. Bandhan and Tata, which had 50% portfolio overlap in June 2016, ended up just Rs 54,901 apart. Franklin and PGIM, also with 50% overlap, were separated by only Rs 65,153. JM and Tata finished less than Rs 81,000 apart.

For a ten-year SIP, those are relatively small differences.

But not every pair followed the same pattern.

Bandhan and PGIM started with 50% overlap, yet Bandhan finished ahead by Rs 3.08 lakh. ICICI Pru and JM had nearly 57% overlap, but ICICI Pru ended ahead by Rs 2.30 lakh.

In other words, portfolio overlap did pull many funds into the same broad return range. Funds that owned many of the same stocks generally ended up in the same ballpark.

But overlap did not lock their returns together.

Small differences in stock weights, portfolio changes over time, and investment decisions along the way were enough to create meaningful gaps in outcomes.

Why did similar funds produce different outcomes?

The answer is simple.

Owning the same stocks is only part of the story. What matters just as much is how much of each stock a fund owns, when it buys or sells, and how the portfolio changes over time.

Two funds can start with very similar portfolios and still take different paths over the next ten years. Those small differences compound.

That’s why JM Large Cap, despite having all 35 of its stocks inside the Nifty 50, still beat the index by Rs 98,366.

And that’s why PGIM, even with nearly 89% of its holdings overlapping with the index, ended up trailing it.

The stock lists were similar. The decisions behind those stock lists were not.

Did the index fund do better or worse?

The UTI Nifty 50 Index Fund turned a Rs 10,000 monthly SIP from June 2016 to May 2026 into Rs 21,30,971 by 10 June 2026.

How did the active funds compare?

Four of the six active funds finished ahead of the index, while two finished below it.

Franklin India Large Cap came closest, ending just Rs 9,134 below the index. Essentially the same outcome. PGIM trailed the index by Rs 74,287. At the other end, ICICI Pru outperformed the index by Rs 3,28,505, the largest margin among the six funds.

What’s particularly interesting is JM Large Cap. Even though all 35 of its stocks were already present in the Nifty 50, it still generated Rs 98,366 more than the index over ten years.

That reinforces the same point that active management is not just about owning different stocks.

ICICI Pru reinforces the same idea. It had the lowest overlap with the index among the six funds yet delivered the highest outperformance.

So the difference wasn’t simply the stocks they owned. It was what fund managers did with them.

The Real Takeaway

The idea from this study is that overlap is neither good nor bad.

Large-cap funds are naturally forced into the same investment universe.

As a result, portfolios often start with many of the same ingredients. But the final outcome depends on how those ingredients are used.

And that’s where active management comes in.

When people debate active versus passive investing, the conversation often focuses on the final result: did the fund beat the index or not?

What gets overlooked is everything that goes into trying to achieve that result.

How much of a stock should the fund own? Which sectors deserve a larger allocation? When should positions be increased, reduced, or exited altogether? Which opportunities outside the index are worth taking?

These decisions may seem small in isolation. But over years, they compound.

Our study showed exactly that. Funds that started with highly similar portfolios still ended up delivering meaningfully different outcomes. Some beat the index. Some didn’t.

The stock universe was largely the same.

The decisions were not.

And in mutual fund investing, those decisions are often where the difference is made.

Note:

• Overlap only looks at stock names, not allocation. Two funds may own many of the same stocks, but they can still perform differently depending on how much money they invest in each stock.

• The results are based on one specific 10-year period. This period included both the COVID-19 market crash and a strong market recovery. Results could be different in another time period.

• The SIP assumes perfect investing discipline. We assumed ₹10,000 was invested every month on the first available NAV date without fail. Actual investor behaviour may differ.

• This study looks at only six funds. These were chosen because they had the highest portfolio overlap in June 2016 and enough historical data. They do not represent all large-cap funds.

• Taxes, exit loads, and other costs are not included. These can affect the returns investors actually receive.

• Portfolio overlap was measured only once, in June 2016. Fund portfolios changed over time, so the level of overlap between them also changed during the following ten years.

Research Analyst Disclaimer

Platforms like Zomato and Swiggy are popular because they make the entire ordering process convenient. They offer a choice of menus to choose from all in a single place, and customers can pay digitally and track their order in real-time, turning a multi-step chore into a three-click experience.

They handle the entire logistical headache without having to contact anyone.

But these platforms are not the only way to order food. Many restaurants also accept direct orders. A lot of the time, such orders can turn out to be cheaper for the customer.

The meal itself remains exactly the same.

The food is prepared by the same chefs, in the same kitchen, using the same ingredients. It is packaged in the same container and delivered to the same customer. Nothing about the product has changed.

The only difference is the channel through which the order was placed.

So why does the price differ?

The answer is simple. Food delivery platforms provide a service. They help customers discover restaurants, process payments, and manage logistics. In return, they charge fees for that convenience.

The customer is not paying for a different meal.

They are paying for a different route to get the same meal.

Direct and Regular mutual funds operate on a surprisingly similar principle.

Understanding Direct vs Regular Plans

Direct and Regular Plans are not different mutual funds. They are simply two different ways of investing in the same mutual fund.

When an investor chooses a Regular Plan, the investment is made through an intermediary such as a distributor, broker, bank, or financial advisor. In return for distribution and advisory services, the mutual fund pays a commission to the intermediary.

This cost is built into the fund’s expense ratio and is ultimately borne by the investor.

When an investor chooses a Direct Plan, they invest directly with the mutual fund company or through a platform that does not charge distribution commissions. The research and investment decisions are made entirely by the investor.

As a result, Direct Plans have lower expense ratios than their regular counterparts.

Because there is no middleman commission built into a Direct Plan, its cost is lower. But just like ordering food, the underlying product is identical.

Both plans track the exact same stocks, managed by the exact same fund manager, inside the exact same portfolio.

This raises an obvious question: do direct and Regular Plans differ in terms of returns?

The Experiment

We wanted to understand whether the choice between a direct and Regular Plan actually makes a meaningful difference to investor returns.

To test this, we selected a set of mutual funds across different categories and compared the performance of their direct and Regular Plans over a 10-year period. Each month an SIP of Rs 10,000 was made into the fund through both plans.

At the end of 10 years, the amount invested in each plan was Rs 12 lakh.

We were trying to answer two simple questions:

1. Is there a meaningful difference in returns between Direct and Regular Plans?

2. If there is, how does that difference develop over time?

To make the comparison broad-based, we selected funds from multiple categories:

• UTI Nifty Index Fund

• Nippon India Large Cap Fund

• SBI Small Cap Fund

• HDFC Mid-Cap Opportunities Fund

• Aditya Birla Sun Life Bond Fund

For each fund, we compared the direct and regular versions of the same scheme. Since the underlying portfolio and fund manager remain identical, any difference in performance would come from the difference in costs between the two plans.

By looking across equity, index, and debt funds, we wanted to see whether the gap was consistent over time and over categories.

Results

Across all the categories, Direct Plans performed better than the Regular Plan counterparts.

This result is not particularly surprising. Since Direct Plans have lower expense ratios, a smaller portion of the portfolio is deducted as fees each year. While the difference may appear small initially, it compounds over time and gradually widens the gap in returns.

The gap in the returns varied significantly across categories.

For the debt and index funds, the difference was relatively modest, with the Direct Plan generating roughly Rs 15,000 more on a Rs 10 lakh portfolio over 10 years.

However, in actively managed equity funds, the gap was much larger, ranging from Rs 1.5 lakh to Rs 2 lakh over 10 years.

This difference can be seen by looking at the CAGR too.

The differences between Direct and Regular Plans may appear small on an annual basis often around 0.1% to 1.3% points but when compounded over a decade, they can translate into meaningful differences in final portfolio value.

How does this difference accumulate?

The graph below shows how the difference between the Direct and Regular plans forms over time. The gap does not grow in a straight line.

In the initial years, the difference is barely noticeable. For most funds, the gap is only a few hundred or a few thousand Rupees. This is insignificant, compared to the amount invested.

However, as time passes, the gap begins to widen at an accelerating pace. This happens because the money saved through lower expenses in a Direct Plan remains invested and earns returns of its own. Those additional returns then generate further returns, creating a compounding effect.

This is especially visible in the equity funds. What starts as a relatively small difference in the early years gradually rises into a significant gap by the end of the decade.

The debt and index funds follow the same pattern, although the graphs are less dramatic because their return profiles and expense differences are lower.

The key takeaway is that costs compound just like returns do. A seemingly small annual expense difference may not feel important in year one, but over long investment horizons it can create a meaningful difference in final wealth.

Why do investors invest through Regular Plans?

Given that Direct Plans generated higher returns across every category we tested, one might assume that everyone in India is flocking to them. However, the reality of the Indian mutual fund industry is more nuanced.

India has a population of over 1.4 billion, but according to data from AMFI, there are currently only about 5 crore unique mutual fund investors.

That means only about 3.5% to 4% of the Indian population currently invests in mutual funds.

Despite the rapid growth of the industry, mutual fund penetration in India remains relatively low compared to developed markets. For many people, investing is still seen as a complex and intimidating process.

This helps explain why Regular Plans continue to dominate the industry, accounting for roughly 60% of total mutual fund assets under management.

For many first-time investors, selecting funds, understanding risk, completing KYC requirements, and making decisions can be overwhelming or simply inaccessible.

This is where distributors, advisors, and banks play an important role.

They help investors get started, recommend funds, handle paperwork, and provide guidance during periods of market uncertainty.

It is also worth remembering that Direct Plans are a relatively recent addition to the Indian mutual fund landscape. They were introduced by SEBI only in 2013.

For decades before that, distributors and advisors were the primary channel through which investors accessed mutual funds, playing a key role in expanding the industry’s reach across the country.

Conclusion

Ultimately, choosing between a Direct and Regular mutual fund comes back to the food delivery analogy.

Regular Plans charge a higher expense ratio because part of that cost goes towards distributors, advisors, and the services they provide. For many investors, especially those who are new to investing or prefer professional guidance, that additional cost may be well worth paying.

Direct Plans, on the other hand, are designed for investors who are comfortable making their own investment decisions and managing their portfolios independently.

As our 10-year experiment shows, this small difference in expenses can compound into a gap in wealth over the long term.

However, costs are only one part of the equation. Staying invested through market cycles proves to be more important than saving a fraction of a percent in fees.

Ultimately, it comes down to the investor’s experience, confidence, and ability to manage investments independently.

The right choice is not necessarily the one with the lowest cost, but the one that works best for the investor and helps them stay invested for the long term.

Limitations of the Experiment

• Past performance is not a guarantee of future returns: The results are based on the last 10 years. The gap between Direct and Regular Plans may be different in the future.

• Expense ratios can change: Fund houses regularly revise their expense ratios. As a result, the difference between Direct and Regular Plans may widen or narrow over time.

• Investor behaviour matters: This analysis assumes both investors stay invested for the entire period. In reality, an advisor may help an investor avoid panic-selling or stay disciplined during market downturns.

• Switching plans may have costs: Investors moving from a Regular Plan to a Direct Plan could face taxes or exit loads, which are not included in this analysis.

• Limited history: Direct Plans were introduced only in 2013, so it is not possible to compare their performance across older market cycles.

Research Analyst Disclaimer

That teenager was Jack Sweeney.

While studying at the University of Central Florida, Sweeney built a Twitter bot called @ElonJet.

The account wasn’t spreading rumours, nor was it hacking into anything.

Instead, it relied on a technology called ADS-B.

ADS-B (Automatic Dependent Surveillance-Broadcast) is publicly available data that broadcasts the aeroplane’s location, altitude, speed, and direction. It is primarily used by air traffic controllers and nearby aircraft for safety.

Sweeney decided to use that public data to track Elon Musk’s private jet.

The bot monitored Musk’s aircraft and automatically posted updates whenever the jet took off or landed. As a result, whenever Musk’s jet moved, the internet knew.

The account quickly became popular. Journalists, investors, and Tesla enthusiasts started following it closely.

Why?

Because the jet’s movements often seemed to reveal important business developments before any official announcement.

If the aircraft repeatedly appeared near a Tesla facility, people wondered whether the company was planning a new expansion. If it landed in Washington, speculation grew about meetings with regulators or government officials. And when it showed up near another company’s headquarters, rumours of partnerships, acquisitions, or corporate negotiations quickly followed.

People weren’t analysing the business itself.

They were analysing the movements of the person running the business.

The assumption was if important people are moving around, they probably know something.

Investors often apply the same logic to shareholding patterns.

Every listed company is required to disclose a shareholding pattern every quarter. This report shows who owns the company’s shares and how those ownership stakes have changed over the previous three months.

Investors closely examine these filings to see whether promoters, mutual funds, foreign institutions, or retail investors are increasing or reducing their stakes.

The belief is simple: changes in ownership might reveal something before the company’s results, announcements, or stock price do.

Which raises an interesting question.

Does the shareholding pattern actually tell us anything useful about where a stock goes next?

We decided to find out.

The Experiment

We started with the Nifty 50 constituents as they existed in November 2013.

From this list, we collected quarterly shareholding data from December 2013 to March 2026.

For every company and every quarter, we recorded three ownership categories:

• Promoter and promoter group ownership

• Institutional ownership

• Non-institutional ownership

Next, we collected daily stock price data for the same companies and measured what happened to stock prices after ownership changed.

To avoid reacting to tiny fluctuations, only changes larger than 0.25 percentage points were treated as meaningful.

For each observation, we then measured stock returns over the following year.

Study 1: Do promoter ownership changes matter?

For every quarter, we calculated how much promoter ownership changed compared to the previous quarter. Each observation was placed into one of three groups.

Promoter ownership increased, promoter ownership decreased, or no meaningful change (less than 0.25 percentage points in either direction).

We then counted how many times the stock was higher or lower one year later in each group.

If promoter ownership is an important signal, stocks where promoters increased their stake should have gone up more often than stocks where promoters reduced it.

Study 2: Do institutional ownership changes matter?

We repeated the exact same process for institutional ownership, which in our dataset combines FII and DII into one number.

For every quarter, each observation was placed into one of three groups.

Institutional ownership increased, decreased, or did not change meaningfully.

We then counted outcomes one year later for each group.

The question was simple. If institutions are smart money, stocks they were accumulating should have gone up more often than stocks they were exiting.

Study 3: Do non-institutional ownership changes matter?

We repeated the same process for non-institutional ownership. This category covers everyone who is not a promoter and not an institution (retail investors, HNIs, trusts, and family offices).

Again, three groups. Increased, decreased, no meaningful change. Outcomes counted one year later.

This let us test the contrarian belief that rising retail participation is a warning sign.

Study 4: Do ownership streaks matter more than single-quarter changes?

The first three studies looked at one quarter in isolation.

But a single quarter of buying or selling can happen for many reasons and may mean nothing.

So we looked for streaks, consecutive quarters where the same ownership group kept moving in the same direction.

For each streak length, we counted how many times the stock was higher or lower one year later.

The question was whether persistence made any signal stronger than a one-off change.

Results

Study 1:

Promoter holding is one of the most closely watched numbers in a company’s shareholding pattern.

The common belief is that if promoters are buying more shares, they must be confident about the company’s future. If they are selling shares, something must be wrong.

But the data doesn’t support such a straightforward conclusion.

In 100 quarters where promoters increased their stake, the stock was higher a year later in 53 cases and lower in 47. That is a 53%.

In 147 quarters where promoters reduced their stake, the stock was higher a year later in 77 cases and lower in 70. That is also a 52% hit rate.

A coin flip is 50%.

The 1,680 quarters, where promoter ownership barely moved, produced a 62% hit rate.

The two groups were nearly identical, with promoter buying leading to positive one-year outcomes 53% of the time versus 52% for promoter selling.

The difference is just one percentage point.

If promoter ownership changes contained a strong signal about future stock performance, we would expect a much wider gap between these two groups. Promoter buying should consistently be associated with better outcomes than promoter selling.

That is not what the data shows.

Instead, stocks performed remarkably similarly after both promoter buying and promoter selling.

This does not mean promoter ownership is unimportant. Promoters remain the largest owners of many Indian companies and their actions can matter enormously in specific situations.

But across this dataset, small quarter-to-quarter changes in promoter ownership did not appear to provide a reliable clue about where a stock would be one year later.

The shareholding pattern tells us that ownership changed.

It does not tell us whether that change happened because of conviction, compliance requirements, family arrangements, dilution, liquidity needs, or something else entirely.

And that may be why the signal appears so weak.

The broader lesson is not that investors should ignore promoter ownership. It is that promoter ownership, by itself, may not be enough.

Study 2:

When institutions bought or sold

If promoter holding is the signal retail investors watch most, institutional holding is the one they trust.

The reasoning makes sense. Why wouldn’t they?

Institutions have research teams, analyst coverage, management access, and decades of market experience.

This 2nd study tests whether that trust was justified.

Across our dataset, institutional ownership moved up or down in 1,087 out of 1,927 observations. That’s far more often than promoter ownership, which suggests institutions were constantly buying, selling, and tweaking their positions.

We then looked at what happened to stock prices over the following year.

In 590 quarters where institutions increased their stake, the stock was higher a year later in 57% of observations. In 497 quarters where institutions reduced their stake, the stock was higher a year later in 60% of observations. The 840 quarters where institutional ownership barely changed saw stocks end higher one year later in 64% of observations.

One possible explanation is that institutional activity is often more complicated than it appears.

Institutions do not buy and sell stocks for just one reason. New money comes into funds. Investors withdraw money. Portfolios get rebalanced. Sector allocations change. Risk limits get adjusted.

The shareholding pattern tells us that institutions changed their position. It doesn’t tell us why.

Stocks performed broadly similarly after both institutional buying and institutional selling. The gap between the two was much smaller than most investors would probably expect.

Knowing whether institutions were buying or selling did not tell us much about where a stock would be one year later.

Study 3:

When retail and public ownership moved

The last group we looked at was non-institutional ownership. This includes retail investors, HNIs, family offices, trusts, and other non-institutional holders.

Opinions on this group are often completely opposite. Some investors see rising public ownership as a positive sign. More people are discovering the company and becoming shareholders. Others see it as a warning sign, arguing that institutions may be using retail demand as an opportunity to exit.

The data gave us a chance to test both ideas.

When non-institutional ownership increased, stocks were higher a year later 61% of the time (309 out of 510 cases).

When non-institutional ownership decreased, stocks were higher 58% of the time (286 out of 491 cases).

When ownership barely changed, stocks were higher 63% of the time (580 out of 926 cases).

61%. 58%. 63%.

Three numbers that are almost identical to each other.

Rising public ownership or falling public ownership or even ownership staying largely unchanged did not tell us much.

No matter what was happening in the shareholding pattern, stocks still ended up higher roughly six times out of ten.

Study 4:

When the trend kept going

The first three studies looked at ownership changes over just one quarter. But ownership often changes gradually. Institutions may keep selling for several quarters, or promoters may slowly increase or reduce their stake over time.

So instead of looking at one-quarter changes, we looked at ownership trends that continued for multiple quarters.

The idea is that if a change lasts for several quarters, it may be more meaningful than a one-time move. We then checked how often these stocks were higher one year later.

What stands out is how similar the results are. Whether institutions sold for one quarter or several quarters in a row, the chance of the stock being higher a year later stayed close to 60%.

If sustained institutional selling was a strong negative signal, you would expect longer selling streaks to lead to worse outcomes. But the data does not show that.

We saw a similar pattern with non-institutional ownership.

At first, this seems more promising because the hit rate falls as the ownership streak gets longer. But the decline is small and inconsistent. As the streak length increases, the number of observations also becomes smaller, making the results less reliable.

Promoter ownership showed a similar story. There were too few long streaks to draw strong conclusions, but longer trends did not appear to be any more useful than shorter ones.

This was one of the biggest surprises of the study. If ownership trends contained a strong signal, it should have shown up here. Instead, the results stayed remarkably similar across all groups. Whether ownership changed for one quarter or four, and whether it involved institutions, promoters, or non-institutional investors, the chances of the stock being higher a year later remained broadly the same.

Note:

• We only used quarterly shareholding data, so we can see promoter ownership only four times a year. Any buying or selling within the quarter is not captured.

• The analysis looks only at one-year stock performance. Results could be different over shorter or longer time periods.

• Changes below 0.25% were excluded to remove noise. Some genuine promoter activity may therefore be missing from the dataset.

• All promoter ownership changes were treated equally. A 0.3% increase was counted the same as a 12% increase.

• Shareholding data shows what changed, not why it changed. Promoter selling can happen for many reasons, but all selling was grouped together in the analysis.

• The study covers only 43 large-cap Nifty 50 companies. The results may not apply to mid-cap and small-cap companies.

Research Analyst Disclaimer

This raises a simple question:

If banks are such a dominant force in the index, how much of the Nifty’s long-term returns actually come from them?

And if you removed banking stocks from the equation, how would it affect the returns?

The Experiment

We decided to keep the experiment simple.

First, we took the current list of Nifty 50 companies and looked at how they performed over the last 10 years. We assumed an investor put Rs 1 lakh equally across all the stocks and stayed invested throughout the period.

For this experiment we took the current stocks in the Nifty 50, not the index. We did not use the actual Nifty 50 weightages for this experiment. Every stock was given equal weight in the portfolio so that we could purely measure the impact of removing an entire sector.

Note: Because we are looking at 10 year data, we had to exclude 5 current Nifty 50 companies (like Jio Financial and SBI Life) because they were not listed in 2016. So, our investor divided their Rs 1 Lakh equally across the 45 stocks that were actually available to buy).

This became our baseline portfolio.

Next, we removed all banking stocks from this portfolio and repeated the exact same exercise with the remaining companies. In total, 8 banking stocks were excluded.

To make the comparison more interesting, we also ran the same experiment after putting the banks back in, but removing IT stocks from the portfolio.

Results

Here is what the data showed:

For the baseline Nifty 50 portfolio, the investor puts Rs 2,222.22 into each of the 45 stocks. Over the 10 years, the portfolio grew nearly 6x.

When the banking and finance stocks were removed, the investor put Rs 2,702.7 into each of the 37 remaining stocks.

The baseline portfolio did beat the portfolio without banking stocks, but not significantly. The difference came down to roughly Rs 14,000. It proves that even without the financial stocks, the rest of the market easily carried the load.

Finally, Rs 2,500 was invested equally in the 40 non-IT stocks.

Here is where the significant difference arises. Without the IT giants, the portfolio didn’t drop. It went up. It made Rs 6,16,334. That is almost Rs 35,000 more than the baseline portfolio, generated simply by avoiding the IT sector.

Conclusion

Why did this happen?

The Nifty 50’s sectoral weightage naturally draws attention to banking and IT.

Because they occupy such a massive portion of the index, there is a common perception that these two sectors solely dictate the broader market’s performance.

However, this experiment highlights the underlying diversity of the market.

The numbers show that over the last 10 years, excluding the financials reduced the returns, but not by a great level. This is because sectors like auto, metals, and FMCG contributed enough growth to keep the overall returns nearly identical.

Furthermore, despite their large market capitalization and market prominence, the data shows that large-cap IT stocks actually had a moderating effect on this specific portfolio’s overall CAGR. It reduced the returns for this portfolio.

This drag is largely due to the fact that while IT had a bull run in 2020-2021, the sector went through periods of sluggish growth and corrections in the subsequent years.

This does not mean banking or IT stocks did not give good returns at all.

The experiment ultimately shows that wealth creation within the Nifty 50 has been more diversified than the headlines make it seem. Even after removing major sectors like banking or IT, the remaining companies were still able to generate substantial long-term returns.

Limitations of the experiment

• Survivourship Bias: The study only included companies that are part of the index today. Some weaker companies that were in the Nifty 50 in 2016 but later got removed were not included.

• Equal-weight portfolio: Every stock was given the same weight, unlike the actual Nifty 50 where larger companies have a bigger impact on returns.

• Specific 10-year period: The results are based only on the 2016–2026 period. Different time periods could lead to different outcomes.

• No rebalancing: The portfolio was treated as a one-time investment with no changes made over the 10 years.

Research Analyst Disclaimer

One might fly higher to catch a tailwind. Another might route slightly west to avoid turbulence. One pilot may be asked to slow down because Delhi airport is crowded. Another might be given a clearance to land.

The flight times may vary slightly. But none of this is visible to the passenger.

And then both planes land, within minutes of each other, at the same terminal.

The paths were different. The destination was the same.

ETFs and index funds behave similarly.

Both are trying to do the exact same thing: track the Nifty 50.

But the path they take is slightly different.

An ETF trades on the stock exchange in real time, which means its price keeps changing every second the market is open. When someone buys an ETF, they are buying it at the price the market is offering at that moment.

An index fund works differently. You place an order anytime before the cut-off time, usually around 3 PM, and after the market closes, the fund calculates the exact value of all the stocks it holds. Every investor who placed an order that day receives units at that same end-of-day price.

Even though both products are trying to track the same index, the way they function underneath is slightly different.

An ETF behaves more like a stock because it trades continuously on the exchange, while an index fund behaves more like a traditional mutual fund where transactions happen directly with the fund house.

But despite these differences, both are trying to mirror the performance of the Nifty 50

We decided to find out if the path taken actually makes a difference.

The Experiment

To make the comparison as fair as possible, we selected two passive investing setups where both the ETF and the index fund came from the same AMC.

The study covers September 2015 to May 2026, a period of approximately ten years and four months.

The pairs were:

• SBI Nifty ETF vs SBI Nifty Index Fund

• UTI Nifty ETF vs UTI Nifty Index Fund

This was important because we did not want differences in fund houses, fund management styles, or benchmark methodology affecting the results.

Then we simulated two investors.

Investor A: The ETF Investor

Every month, Investor A buys ETF units directly from the stock exchange at market price.

The closing market price on the SIP date was used as the purchase price for all ETF transactions.

Brokerage and STT costs on ETF purchases have not been factored into this study. Including these would widen the gap slightly further in favour of the index fund.

Investor B: The Index Fund Investor

Investor B invests in the matching index fund and receives units at NAV.

Both investors:

• invested Rs 10,000 every month

• followed a SIP on the 3rd of every month

• stayed invested through the full study period

The Results

The gap turned out to be much smaller than most people would expect.

Both ETFs and index funds tracked the same Nifty index. And after ten years of monthly investing, the final outcomes were almost identical.

But not perfectly identical.

Similar to the two flights that took different routes and landed within minutes of each other.

SBI: ETF vs Index Fund

Both investors started investing Rs 10,000 every month. One bought the SBI Nifty ETF from the stock exchange at market price. The other invested in the SBI Nifty Index Fund at NAV.

Over the full study period:

The index fund finished ahead by roughly Rs 21,000.

Not a huge difference.

But still enough to show that even when both products track the same index, the investor outcome was not perfectly identical.

UTI: ETF vs Index Fund

Then we repeated the exact same experiment using UTI products.

And once again, the results were extremely close.

The index fund again finished slightly ahead.

This time by around Rs 15,000.

Which is interesting. Because it suggests the SBI result was not a one-off. In a different AMC, with a different set of SIP execution dates, the same broad pattern appeared again.

Why did the index funds slightly outperform?

The study does not suggest ETFs are bad investments.

In fact, the more interesting takeaway is probably the opposite.

Despite operating through slightly different systems for many years, both the ETF and index fund portfolios ended up remarkably close to each other. That itself says a lot about how efficient passive investing has become in India.

But the study did reveal something subtle.

Two products tracking the exact same index are still not perfectly identical in real-world investing.

Because investment returns are influenced not only by what you own, but also how you access it.

An index fund investor deals directly with the fund house. At the end of the day, the AMC calculates the exact value of all the underlying stocks and gives every investor units at that same NAV.

An ETF investor, meanwhile, interacts with the stock market itself. The ETF trades live during market hours, and its price depends on ongoing buying and selling activity in the market.

That introduces very small inefficiencies along the way.

Sometimes the ETF may trade slightly above its actual value. Sometimes slightly below. Sometimes there may not be enough trading activity at the exact moment an SIP order gets executed. Sometimes the buying price and selling price in the market may not perfectly match.

Individually, these differences are tiny and almost invisible on any single day.

But SIP investing works through repetition.

When something repeats every month for ten years, even tiny differences slowly start compounding.

Enough to create small differences in the final portfolio value.

It is also worth noting that the specific funds chosen may have had slightly different tracking efficiencies relative to the Nifty 50 during this period.

This study does not claim that index funds as a category always outperform ETFs. It only tells that in these two specific fund pairs, over this specific period, the outcomes differed slightly.

So who should choose ETFs?

The study did not find ETFs to be meaningfully worse. The gap was small, but it did reveal where friction tends to accumulate, which helps clarify who each product naturally suits.

Since ETFs trade like stocks, they offer a level of flexibility that traditional index funds cannot.

An ETF investor can buy during market hours, place limit orders at specific prices, react instantly during sharp market falls, or move money quickly between different asset classes.

For investors who actively manage allocations or prefer more control over execution, these features can genuinely be useful.

Index funds, however, may suit a very different type of investor.

They work especially well for people who primarily invest through SIPs, prefer simplicity, and do not want to think about market prices, trading windows, liquidity, or execution timing every month.

You simply invest the money and receive units at the day’s NAV directly from the fund house. The process is automatic, predictable and operationally simple.

The study suggests that operational simplicity can matter alongside expense ratios.

Because long-term investing is not only about finding the theoretically perfect product.

It is also about finding the system that works most smoothly for the investor using it month after month for many years.

The Real Takeaway

Both investors still ended up in almost the same place after ten years.

For long-term passive investing, the structure matters far less than simply staying invested consistently for many years.

The ETF investor and the index fund investor both captured the same thing in the end. That is long-term growth of the Indian equity market.

They just travelled through slightly different systems to get there.

Note:

• This study uses closing ETF closing market prices for SIP purchases. In real life, investors may buy at slightly different prices during the trading day.

• Brokerage, STT, and demat-related costs on ETF transactions were not included. Depending on the platform used, these costs could slightly reduce ETF returns further.

• The study compares only two AMC pairs (SBI and UTI) because they had the longest available historical data. Results may differ for other ETFs, index funds, or different time periods.

• Some part of the return difference may also come from how efficiently each individual fund tracked the Nifty 50, not just from the ETF vs index fund structure itself.

• The study period runs from September 2015 to May 2026, which includes major market phases like the COVID crash and recovery. Results during other market periods may differ.

Research Analyst Disclaimer

Investors compare past returns, check ratings, read recommendations, and usually avoid funds that have recently performed poorly.

That instinct feels reasonable.

After all, if a fund has not given good returns in recent times, why would anyone want to put money into it?

But what if an investor ended up investing in a poorly performing fund?

Would that decision seriously damage long-term wealth?

We decided to put this to the test.

Instead of picking winners, we built a strategy that does the opposite. Every year, it invests in the lowest-performing large-cap mutual fund from the previous year.

In a way, this represents the unluckiest investor. Someone who consistently ends up in the weakest fund on the list, year after year.

What happens to their wealth over time?

The Experiment

We established a clear set of rules.

The universe was limited to Large Cap mutual funds that had existed for at least 10 years.

On 1st January of every year, the investor would:

• Identify the lowest performing large cap fund from the previous calendar year

• Invest Rs 1 lakh into that fund

• Repeat the same process every year for the next 10 years

For example, after identifying the lowest performing fund of 2015, the investor will invest in it in 2016. In 2017, the investor will invest in the lowest performing fund of 2016, and so on…

Also, throughout the 10 years, no selling will take place. If the lowest performing fund changed the next year, the earlier investment would remain untouched. A fresh Rs 1 lakh would simply go into the newly identified lowest performer.

This avoided taxes, exit loads, and unnecessary churn.

We ran the experiment starting from Jan 2016 to Dec 2025, a period of 10 calendar years.

Finally, as a benchmark, we compared this strategy against an index fund investor who put Rs 1 lakh into the UTI Nifty Index Fund on the same days.

Lowest Performing Funds Each Year

One thing becomes obvious immediately. No single fund stays at the bottom forever.

Some names appear multiple times, but the rankings constantly change depending on market cycles, portfolio decisions, etc.

It is also interesting to note that the lowest returns vary drastically depending on how the broader market performed that year. During strong bull market years like 2021 and 2023, even the lowest-performing large-cap funds still delivered double-digit returns (~17%).

Results

The result was surprising.

The strategy of investing in the previous year’s lowest performing large cap fund actually ended up outperforming the index fund over the 10-year period.

It was not significant.

But enough to challenge the assumption that the lowest performer automatically means that it is a bad investment.

Why Did This Happen?

In mutual funds, short-term underperformance does not always mean the fund is weak.

A fund can underperform simply because it did not own the stocks that were driving the market during that period. It may have held different sectors, avoided overheated stocks, or maintained cash while others stayed fully invested.

As the market experiences changes, the performance of the fund undergoes changes too.

This experiment also shows something important about large cap funds. Even the lowest performing fund in the category is mostly invested in large, established companies. It may lag behind other funds, but it is not taking extreme bets or investing in weak businesses.

Because of this, poor short-term performance does not automatically translate into long-term damage.

Also, remember that we have not considered any costs in this experiment. And while the fund’s expense ratio is already factored into the returns we calculated, other charges (if applicable) could slightly alter the exact final numbers.

Conclusion

Ultimately, this experiment reveals a highly comforting truth for everyday investors: a period of underperformance in your portfolio will not permanently destroy your wealth.

Even if you happen to invest in a fund that goes through a rough patch, it does not mean your returns are doomed. Because of market cycles (and in this case, the safety of established large-cap companies), today’s underperformers may often bounce back in the future.

The key to the strategy’s success was simple: the investor stayed in the market.

The anxiety we often feel about picking the ‘perfect’ mutual fund is largely misplaced.

We saw here that even if you possessed the worst luck imaginable, where you blindly picked the absolute bottom-ranked large-cap fund every single year for a decade, the compounding power of the broader Indian stock market still more than doubled your money. It even outperformed the index fund by a small margin.

To be clear, the takeaway isn’t that you should actively seek out poorly performing funds.

You should always do your due diligence before investing. It is just that an underperforming fund may not necessarily be a wealth destroyer, as long as you stay invested.

At the end of the day, consistently investing and staying in the market matters far more than perfectly timing or picking the absolute best fund.

Limitations of the Experiment

• The study is limited to large cap mutual funds, which are relatively stable and diversified. Results may not hold in small cap or sector funds where volatility is much higher.

• It uses a simplified selection rule of picking the previous year’s worst performing fund, without analysing why it underperformed or whether conditions were temporary or structural.

• The strategy assumes no selling at all, which removes real-world behaviour like profit booking, rebalancing, or exiting underperforming funds.

• It ignores taxes, exit loads, and transaction costs, which would reduce actual investor returns in practice.

• It is based on historical hindsight data, meaning the selection is easier in hindsight than it would be in real-time decision making.

• The holding period assumes long-term patience every time, which may not match how most investors actually behave during underperformance periods.

• It does not account for fund closures, mergers, or category changes, which can affect real-world portfolio continuity.

• The experiment uses a Rs 1 lakh lump sum at the start of every year, which makes timing very sensitive. In reality, SIPs spread investments over the year, reducing timing impact.

Overall, the strategy used here is a controlled thought experiment, not a practical investing strategy.

Research Analyst Disclaimer

They tell you everything.

They are mandated by SEBI to disclose their full portfolios.

This means, investment in every stock, every sector and the exact percentage of allocation is published on their website, available to anyone for free.

This transparency also brings up an interesting thought that has likely crossed the minds of anyone who has spent time reading these disclosures.

If I can see exactly what the fund manager bought, why do I need the fund?

The manager’s entire portfolio is available. Updated every 30 days.

You could, in theory, replicate it yourself. You could buy the same stocks and use the same proportions. Invest directly, without middlemen or any fees.

Would it work? Would it give better returns than the fund? We decided to test it.

We chose the ICICI Prudential Focused Equity Fund and tracked it through January 2020 to December 2025.

We chose a focused fund for two main reasons. Many mutual funds hold 60, 70, or sometimes even 100 stocks. Replicating those portfolios is practically unmanageable for an individual investor.

Two, the fund stays almost fully invested in equities, typically around 98-99%. That makes the comparison much closer to directly investing in stocks.

With almost no cash on the side, what you see is essentially the same set of companies the fund is invested in.

We considered two investors. Both of them invested Rs 1,00,000 every month. Both invested on the same day.

Investor A put their money in the mutual fund.

Investor B decided to copy the fund. So every month, whatever the fund disclosed, they adjusted their portfolio accordingly.

What you are always reading is last month’s decision

Before looking at any returns, there is one structural aspect of this strategy that needs to be understood.

Across all 72 months in this study, the ICICI Prudential Focused Equity Fund published data in 5 to 8 days after the month end.

For example, if we take a look at the data from 2024, the fund’s portfolio as of 31 Jan was published on 8 Feb. This is a 7-day reporting lag after month-end.

In reality the lag could be even bigger. If the fund manager started buying a stock on, say, 5 Jan, an individual investor would only get to know about that on 8 Feb. This specific decision becomes visible after a 34-day gap.

This creates a clear mismatch between the two investors.

So Investor B, as the copycat, is reacting to data which has been disclosed with a delay. Investor A is investing in real time based on the fund manager’s decisions as they happen.

So the two investors are not actually buying the same stocks, in the same composition, at the same time.

Results

Both investors appeared to be doing the same thing on the surface. But over time, their outcomes began to diverge.

At the end of 6 years, the mutual fund investor ended up with Rs 1.44 crore.

The copycat investor ended up with Rs 1.32 crore.

Both made good money. Both nearly doubled what they started with. But there was still a gap of Rs 11.7 lakh between them.

How did this happen?

In 2020, almost nothing changed in the portfolio. Hardly any stocks were bought or sold. When the COVID crash came, both investors faced similar losses. And when the market recovered, both went up. During this period, the delay did not have much impact.

The same pattern repeated in 2023. The drivers of returns were already part of the portfolio, so the copycat simply held on and participated in the move.

In both years, the copycat finished with slightly better returns.

In 2021, the fund manager made a lot of moves. By the time the copycat entered or exited, prices had already moved. Those delays accumulated quickly and erased its earlier lead within a short period. A similar pattern played out in 2024, where a larger reshuffling widened the gap again, and it never fully recovered.

Basically, in years where the fund didn’t change much, the copycat stayed close. Sometimes they even did slightly better. But in years where the fund was more active, buying new stocks or selling old ones, the copycat started falling behind.

When the portfolio is stable, the copycat stays close. When activity increases, the lag starts to matter. This is because the fund manager acts first. The copycat reacts later, which often means entering at slightly higher prices and exiting at slightly lower ones. Each difference is small, but they add up over time.

Fees and Charges

For the study, everything was calculated as gross returns. This means no brokerage fees, no government taxes, no execution costs.

But copying a fund means buying and selling constantly.

Every month, Investor B deployed fresh Rs 1 lakh by splitting it across every stock the fund currently held, between 20 and 30 stocks depending on the month. Each stock was a separate buy order. On top of that, every time the fund removed a stock from its portfolio, Investor B had to sell it too.

Over six years, that added up to 1,981 buy orders and 92 forced sell transactions, 2,073 trades in total. Investor A made 72. One per month.

Every single one of those 2,073 trades attracted brokerage and Securities Transaction Tax. On any individual trade, these costs feel small. But across 2,073 transactions over six years, they accumulate into a real and unavoidable drag on returns.

Then there is the tax on gains. Each of those 92 forced sells was a taxable event. Most of those stocks were held for less than a year, because the fund added them and removed them within months. Any profit made on a position held under 12 months is taxed at 20% as Short Term Capital Gains. So every time the fund rotated out of a stock and Investor B followed, money went to the government.

Investor A did not face any of this. Mutual funds buy and sell internally without triggering a tax event for the investor. You only pay tax when you personally sell your units. And if you held them for more than a year, the applicable rate is 12.5%, not 20%.

So in reality, the difference of Rs 11.7 lakh turns out to be superficial. The real gap, after brokerage, STT, and short-term capital gains tax, is likely between Rs 14 lakh and Rs 15.4 lakh on the total Rs 72 lakh invested.

The Real Takeaway

The copycat strategy is not broken in theory. It just does not work in practice. Because by the time you saw it, acted on it, paid for it, and got taxed on it, the advantage had already gone.

This is what the data actually says. Investing in the fund directly was simpler, cheaper, and more effective. One transaction a month.

Copying the fund looked clever but was not. Same stocks, more effort, worse outcome. The fee you were trying to save turned out to be the smallest cost in the entire process.

The real cost was the delay. Not the 0.59% expense ratio, but the gap between when the fund manager acted and when you found out. That lag changed everything. By the time you reacted, the opportunity had already moved.

So the conclusion is simple.

If you believe in the fund manager’s process, invest in the fund. Trying to replicate their decisions from a monthly disclosure is not a shortcut. It is a slower and more expensive way to get to a worse result.

A mutual fund does not just give you a list of stocks. It gives you timing, execution, tax efficiency, and judgement.

All of that comes bundled into a single transaction every month.

Research Analyst Disclaimer

The marine giant could survive for more than 200 years, which is quite an age.

But can you guess who comes next to Bowhead whales?

It is us, humans.

With modern civilisation and advancing healthcare, a lot of humans today are conveniently crossing the timeline of 80 years.

And going by the numbers, 80 years or 29000+ days is not a small duration.

With such a long lifespan, it’s natural that we only have vivid memories of a handful of days, rather than recalling every day we live.

The last day of school, the moment you got embarrassed in front of your crush, the joy after getting your first salary, or the moment when she, or he said ‘yes’, etc.

It’s just a few days that turn out to be critical in a person’s life, days which decide where the life will lead itself from here on.

But what would you say if we tell that this idea fits well in the stock markets too?

So the stock markets trade for nearly 250 days each year, roughly 2,500 days in a decade, and ~5,000 in 20 years.

Everyday, it moves a little up or a little down.

What do you think would happen if we removed just 2 days in the last 20 years?

So we attempted a small simulation.

We made monthly SIP investments of Rs 10,000 in a Nifty 50 Index Fund for 20 years, from January 2006 to December 2025.

The only change was, we didn’t participate or stay invested on 2 specific days, when the Nifty rose the most.

This means we sold off our entire accrued investments one day before the omitted days, sat on cash during omission days, and bought again the very next day at the then prices.

By doing so, we witnessed a change that was worth noting.

Just by missing the 2 best days in 20 years, there was a significant drop in the total returns.

Missing out on those 2 days could have cost almost Rs 10 Lakh.

This reflects too big of a difference coming up in too short periods.

These numbers brought out the adventure lover in us, and we tried again, but this time skipping the best 5 days in 20 years.

Here is what we found.

The lost returns doubled down to almost Rs 19 Lakh when we skipped for 5 days, from 2 days.

Now there is something more that grabbed our attention and we would like you to know.

The big gain days were never in isolation; almost every such day came only after a big fall day.

9 out of 10 days of the largest gains in the last 20 years(Jan 2006 - Dec 2025), either occurred the very next day after a big fall, or within just a couple of days of it.

Showing that the most crucial days we talked about earlier actually lie around the worst of the days.

Which is surprising to know, as bear markets or a big fall happens to be a major trigger to exit for most of us.

And here it shows that the tendency may potentially do more harm than good, since one might actually miss some of the best days just looming around.

It therefore makes sense to hold onto your investments rather than withdrawing, if panic was the only reason to make that withdrawal.

What do you think?

Feel free to share your thoughts in the comments!


Note:

• SBI Nifty 50 Index Fund Regular - Growth has been used for index fund SIP return calculations.

• The outcome of this simulation could have been different if a different mutual fund or a different timeline could have been chosen

• Images used are AI generated

Research Analyst Disclaimer

The possibility that an investment won’t perform as expected, or worse, that it loses value is difficult to ignore.

No one wants to make the wrong decision. No one wants to watch a single sector or company decline sharply and drag their entire portfolio down with it.

This is where diversification comes in.

To manage risk, money is spread across different assets and categories such as stocks, debt, commodities, etc.

The idea is simple: different assets are expected to behave differently, different sectors behave differently, and even similar assets can behave differently.

When one struggles, another may hold steady or perform better. Together, they are meant to reduce the impact of any single poor outcome.

To reduce risk, diversification is important.

But as portfolios become more diversified, a different question starts to emerge.

Is there such a thing as too much diversification? Can it make your portfolio redundant?

Is there a point where it stops adding safety and only increases the number of moving parts in a portfolio?

To explore this, we tried to understand how diversification affected a portfolio by looking at 8 mutual funds over a 10-year period.

The Experiment

We looked at the historical NAV data of the following mutual funds (all Growth, Direct) for the last 10 years. The selection was intentionally diversified across fund types and AMCs to avoid concentration within a single category or style.

1. UTI Nifty 50 Index Fund(G)-Direct Plan

2. ICICI Pru Large Cap Fund(G)-Direct Plan

3. Parag Parikh Flexi Cap Fund(G)-Direct Plan

4. HDFC Mid Cap Fund (G) Direct Plan

5. Mirae Asset ELSS Tax Saver Fund(G)-Direct Plan

6. Nippon India Small Cap Fund(B)-Direct Plan

7. SBI Magnum Gilt Fund- Direct Growth

8. Kotak Gold Fund-Direct Growth

The goal was to see how the same amount of Rs 1 lakh would behave over the period from 2016 to 2026 when managed under different portfolios.

The experiment was structured in three parts: returns from each mutual fund individually, a fully diversified portfolio where the Rs 1 lakh was split equally across all 8 funds and a comparison with alternative outcomes.

Results

• Individual Returns

Looking at each fund in isolation, the differences in outcomes are significant.

Over the 10-year period, returns ranged from around Rs 2.1 lakh at the lower end to over Rs 7.1 lakh at the higher end for the same investment.

Unsurprisingly, equity-heavy funds, particularly the small- and mid-cap funds, gave the highest returns, while the debt fund gave the lowest.

Granted, their goals are different, one is for rapid compounding, which comes with higher risk, and the other is for stability and safety.

Which is why holding just one fund, or only one type of fund, may not be the most efficient way to invest.

• Diversified Portfolio

Now, we look at a portfolio with all 8 mutual funds. The Rs 1 lakh is divided equally between the funds, Rs 12,500 in each fund.

Remember, diversification can be done in different ways. This is our interpretation and to make matters simple.

Here is how the portfolio looks at the end of 10 years.

Amount invested: Rs 1 lakh (Rs 12,500 per fund) lumpsum
Total Portfolio Value: Rs 4.78 lakh
Profit: Rs 3.78 lakh

Compared to the individual funds, the diversified portfolio sat exactly in the middle in terms of returns.

It delivered better performance than gold and debt funds and also outperformed the index fund and large-cap fund in isolation.

At the same time, it lagged behind the other equity funds.

In this portfolio too, the small-cap, mid-cap, ELSS fund and the flexi-cap fund pulled the most weight by having the most share in profits.

The other 4 funds, while giving not as many returns, kept the portfolio safe and stable.

In simple terms, diversification smoothed the extremes. It avoided the weakest outcomes but also diluted exposure and consequently risk to the strongest ones.

• Alternate Options

The next question we asked is what happens when the structure of diversification is changed.

The idea was simple: if too many funds can dilute returns, then a smaller set should consistently perform better than a fully diversified portfolio

The question explored was whether a smaller combination of funds (fewer than 8) could have delivered different outcomes compared to a fully diversified portfolio.

To test this, multiple combinations were created by gradually reducing the number of funds while keeping allocations equal.

(Note: These variations are not meant to suggest better or worse approaches. They are simply different ways of observing the same idea from different angles).

What was observed was that the outcomes did not follow a linear pattern.

Here are some examples:

In some combinations, removing lower-returning or defensive assets such as debt or index exposure slightly improved returns. In others, the same removal led to weaker performance.

Even the all-equity fund of 3 conservative funds could not beat the diversified portfolio.

Other than the portfolios with both small-cap and mid-cap funds and higher weightage (which increases risk), no other single configuration consistently outperformed the fully diversified portfolio. Their returns were higher, but that also means they were more susceptible to volatility.

Overlapping funds (funds with similar composition) did not seem to add value. Whether this is a structural truth or specific to the funds chosen in this dataset is something that would need further testing.

This leads to one clear observation: The outcome of diversification is not determined by the number of funds alone, but by the composition of those funds.

Conclusion

It ultimately comes down to one simple fact. There is no single right way to invest.

Risk appetite differs from investor to investor, and even for the same investor, it can change over time depending on goals, market conditions, and experience.

What this experiment highlights is a set of patterns rather than fixed rules.

Reducing diversification does not automatically improve returns.

In fact, certain combinations (such as when large-cap, index, and ELSS funds were grouped together) showed that maintaining similar risk exposure (all equity) while reducing the number of funds did not necessarily lead to better outcomes.

At the same time, increasing diversification does not automatically reduce returns either.

However, diversification without structure or awareness of overlap can dilute performance without meaningfully improving stability.

The bottom line is diversification is not a bad strategy. It keeps the investor safe. But depending on how it is constructed, it may not always be the most effective way to push returns beyond a certain threshold if that’s what the aim is.

Limitations of the Experiment

• Fixed time period: The analysis is based on a single 10-year window (2016–2026). Different market cycles could produce different outcomes.

• Limited fund universe: Only 8 mutual funds were selected. Results may vary with a different set of funds within the same categories.

• Equal allocation assumption: Each fund was given the same weight. In reality, investors often adjust allocations based on risk appetite, performance, or market conditions.

• No cost adjustments: Taxes, expense ratios over time, and transaction costs were not factored into the calculations, which can impact real-world returns.

• Return-focused analysis: The study focuses primarily on returns. Risk-adjusted measures like volatility, drawdowns, or Sharpe ratios were not explicitly evaluated.

• Simplified portfolio behaviour: The experiment does not account for investor actions such as rebalancing, entry/exit timing, or behavioural decisions.

Research Analyst Disclaimer

We studied 20 years of Nifty 50 data and found 47 times when the market crossed a level it had never reached before.

That study focused on the moment at the top.

This one is about what happens after.

Right now, the Nifty is going through exactly that phase. It hit an all-time high of 26,328 on 2 January 2026. Then it started falling. First slowly, then faster, with a few strong recoveries in between that did not last.

From 26,328 (all-time high), it reached a low of 22,331 on 30 March, a fall of 15.2%.

Since then, the market has started moving up again. As of 9 April, it is up 6.5% from the low, but still below the January peak.

Which brings us to the real question.

When the market starts recovering like this, can you tell it is real? Is it different from the earlier bounces that failed? How long do recoveries usually take?

And more importantly, do we even have an answer to this?

To understand this, we studied every meaningful fall that came after an all-time high over the last 20 years.

The experiment

We looked at 47 times when the market hit an all-time high.

Out of these, 18 turned out to be real peaks, meaning the market fell at least 5% within the next three months.

But even among these 18, not all falls really mattered.

In 10 cases, the market recovered almost immediately, within a day or two. It dipped slightly and then went on to make a new high. These were not real declines, just small pauses during a strong bull run.

That leaves us with 8 cases where the fall was actually meaningful. In these, the market stayed down for longer, days, weeks, or even months, before finding a bottom and recovering.

We focused on these 8 real declines. For each one, we tracked three key things.

First, we looked at how the fall actually played out. How many days the market went up, how many days it went down, and how often it seemed like it was recovering before falling again.

Second, we checked what would have happened if you tried to sell during the fall, at 5%, 10%, or 15% below the all-time high.

Third, and most importantly, we measured how long it took for the market to hit the bottom and how long it took to recover and reach a new all-time high after that.

Every red triangle on this chart shows a point where the Nifty hit an all-time high and then fell meaningfully. Every green, purple, or teal dot shows where that fall finally ended, the point where the market found its bottom before recovering.

Looking at the chart, it all seems simple. The market peaks, falls, finds a bottom, and then recovers. It looks clear and easy to understand in hindsight.

But it never feels this clear when you are actually living through it.

Results

A fall doesn’t look like a fall while you’re inside it

This is the clearest pattern in the data, and it explains why market corrections feel so confusing.

Look at the chart. Every line starts at the peak and moves lower over time. But none of them fall in a straight line. During most real falls, the market doesn’t move down in a straight line. It moves in waves. It falls, then rises, then falls again. And these up moves are not rare. They happen often.

Those rises are the problem. They look like recoveries. They feel like the fall might be over. But most of the time, they are not.

In longer declines, nearly half of all trading days are positive. In the current fall, about 49% of the days between the January peak and the March bottom were green. The market was going up almost as often as it was going down, even while trending lower overall.

In three of the eight falls, there were no green days at all. The market fell quickly and in a straight line (January 2007, November 2007, and November 2013). Six to seven trading days, all red, and it was over.

The other five were different. And the current fall has been the most extreme example.

This is what changes the experience. It feels like uncertainty and not a certain fall or rise.

Within this back and forth, some days stand out. Strong up days where the market rises 1% or more. These feel like turning points. They look like the start of a recovery. But many turn out to be not.

We call these false bounces. Short rallies that are followed by further declines.

We are not trying to say whether the current rise is real or not. The point is simpler.

A market fall is not easy to recognise while it is happening. In hindsight, the bottom looks obvious. But in real time, the market keeps moving up and down, with several convincing rallies along the way.

Because of this, the actual bottom never stands out clearly.

And when the real recovery finally begins, it looks exactly like those earlier false bounces. There is no clear signal that tells you, this is the one.

So by the time it feels real, the market has usually already moved higher.

A market fall is designed in a way that makes it hard to act confidently. It keeps giving you signals that look real but don’t hold. So if you wait for a clear, reliable sign that the fall is over, you will almost always be late.If your strategy depends on recognising that exact turning point, it’s likely to fail, not because you’re wrong, but because the market never makes that moment obvious.

Selling partway through almost always made things worse

If not at the all-time high, most investors sell after the fall has started. They watch it drop 5%, decide it’s real, and get out. Or they wait for 10%.

We tested both scenarios across all 18 confirmed peaks over 20 years.

We looked at all 18 confirmed peaks, not just the 8 deeper falls. Because at the moment you are making the decision, you don’t know which kind of fall this will turn into. It could recover quickly, or it could keep falling. So the right way to test this is to include every case where a decline had already begun.

The result is the same either way.

If you sold at -5% down (across all 18 confirmed peaks) the market continued falling in 16 of 18 cases. The average additional decline from your sell price was 24.7%.

If you sold at -10% down, the market fell further every single time. All 14 cases where the Nifty reached,10% below the ATH saw further selling from that level. Average additional fall: 23.5%.

November 2013 is the one case where selling at –5% worked — the market recovered almost immediately. It happened once in eight real falls.

But the deeper problem isn’t even that the market keeps falling after you sell. It’s what happens next. Once you’ve exited, the problem changes. It’s no longer about avoiding losses, it’s about deciding when to come back in.

And this is where the confusion from the fall carries forward.

Because the recovery begins in the same way the false bounces did, with a few strong up days that don’t yet feel trustworthy.

So the decision that feels like it will reduce uncertainty ends up creating a new one. Instead of asking “should I sell?”, you’re now asking “is this the real recovery?” And because the market has already shown you multiple failed recoveries during the fall, you hesitate. By the time it feels safe again, a part of the recovery has already happened.

When the bottom comes, it doesn’t wait

This is the finding that matters most right now.

In every single completed fall in our 20-year dataset, all seven of them, the five trading days after the bottom were positive.

The average return in the 5 trading days after the bottom was +5.1%. The average in the 21 trading days after the bottom was +7.1%, with every single one positive.

The current fall bottomed on 30 March 2026. In the five trading days after that bottom, the Nifty rose 7.5%. That is consistent with every previous post-ATH fall bottom in our dataset.

Look at how the recoveries moved from the bottom. They were not slow. In four of the seven completed falls, the market was back at its all-time high level within just 10 trading days of the bottom. Two of the fastest, January 2007 and November 2007, recovered in just 2 trading days from the bottom.

What this shows is that the market does not pause at the bottom. It does not move sideways or give time for confirmation. It turns and starts moving up almost immediately. There is no clear transition point where the fall ends and the recovery begins. The shift happens while uncertainty is still high.

When we extend this further and look at how long it takes to recover fully back to a new all-time high, the results are even more surprising.

Recovery is usually faster than the fall

If you look at how long markets take to fall versus how long they take to recover, a clear pattern emerges.

Days falling vs days recovering, side by side for each episode

In five of seven completed falls, the time it took to recover from the bottom back to the all-time high was shorter than the time it took to fall from the all-time high to the bottom.

The market tends to fall slowly and recover quickly.

January 2007 fell in 5 trading days but recovered in just 2. September 2014 fell over 24 days and recovered in 10. April 2019 fell over 15 days and recovered in 5. Even the deeper June 2019 correction, which took 73 days to fall, recovered in 45 days.

There is a clear difference in how markets fall and how they recover. The fall usually takes time and feels uncertain. It moves slowly, with mixed signals and no clear direction. But when the recovery begins, it is often faster and more decisive.

If you wait for clear signs that the recovery has started, like steady gains or better news, you will likely be late. By the time it feels obvious, the market has already moved higher.

Markets do not move in a balanced way. They fall slowly and in a confusing manner, but they tend to recover faster, which makes the recovery easier to miss.

Conclusion

This study set out to answer a simple question: when a market fall starts reversing, can you actually tell?

The data suggests that in most cases, you cannot.

The bottom is not something you recognise at the moment. It is something you recognise after the market has already moved away from it.

Which is the hardest part of all this. Not the falling. Not the uncertainty while it’s happening.

The hardest part is that it may already be over and you won’t know until it’s too late to act on it.

The broader lesson is not that markets always recover quickly or that every fall is temporary. It is more specific.

Timing the market means getting two decisions right. When to exit, and when to come back in. Doing both consistently is very difficult.

Even studies have shown that investors who got their timing wrong still did better than those who stayed out waiting for the perfect moment.

The cost of waiting for certainty is usually higher than the cost of staying invested.

Over time, what has worked more consistently is staying in the market, not trying to time it.

Limitations of the experiment

• The study is based on 20 years of Nifty 50 data (2 January 2006 to 9 April 2026), with only 8 meaningful post all-time-high falls.

• The findings show patterns in past data, not guarantees.

• The results depend on definitions. What counts as a confirmed peak, meaningful fall, false bounce, or recovery, is based on chosen thresholds. Changing these would change the conclusions.

• The analysis uses only Nifty 50 closing prices. It does not capture intraday moves, volumes, sector differences, or stock-level behavior.

• The study is based on hindsight. Bottoms and recoveries are identified after they happen, not in real time.

• It does not fully reflect real investor behavior. It excludes taxes, transaction costs, emotions, and different entry or exit strategies.

• The current fall (Jan 2026) is still ongoing, so its final outcome is unknown.

• All falls occurred within a broader bull market. Results may differ in a prolonged bear market.

• The analysis focuses only on the Nifty 50. Other indices or asset classes may behave differently.

Research Analyst Disclaimer

Russia invaded Ukraine, and suddenly Europe was at war again. Markets reacted immediately. Stocks fell, investors got nervous, and money started moving into assets that were considered safer.

Gold was one of them.

Whenever uncertainty rises (wars, inflation, economic slowdowns), investors usually move towards gold. Not because gold generates income, but because it is seen as something that holds value when everything else becomes uncertain.

And that’s exactly what happened.

Gold prices started rising as investors looked for stability. Over the next few years, uncertainty didn’t really go away. Inflation remained high in many countries, interest rates moved sharply, global growth slowed, and geopolitical tensions kept showing up in different parts of the world.

Through all of this, gold largely held its ground and often moved higher. This reinforced the old belief in markets that in times of crisis, gold performs well.

In fact, since the start of the Russia-Ukraine war, gold prices have risen more than 100%.

But something interesting happened recently.

Late February this year, tensions between the US and Iran escalated, and markets once again started worrying about a potential conflict in the Middle East. This is a region that is extremely important for global oil supply and trade routes.

Normally, in a situation like this, gold should have risen. But it turns out, gold fell around 10%.

And that raises an interesting question.

If gold is supposed to be the safe asset, why did it fall during a geopolitical crisis?

If gold is considered as the safe asset, why have the prices been falling?

We decided to take a look into it.

The Study

Now, gold prices change with the supply and demand in the market. But what affects this supply and demand? How do prices change in relation to these external factors?

We analysed a dataset spanning almost 20 years of daily market movements, from 2006 to 2026 (nearly 5,000 trading days).

We tracked the relationship between three specific forces that are connected to each other: Gold prices (in USD), the Broad US Dollar Index, and 10-Year US Treasury Yields.

Unlike a simple exchange rate, the broad US Dollar index measures the dollar’s strength against a massive basket of global currencies from the Euro to the Rupee. This index is useful for this study because gold is priced in dollars worldwide and the two usually share an inverse relationship.

Gold as an asset pays zero interest. The US Treasury Bonds, however, pay a guaranteed return. When those returns (yields) rise, the cost of choosing Gold over Bonds increases, which may affect the price of gold.

Results

• The Gold-Dollar Movement

When we looked at the statistical correlation between the dollar and gold in the dataset, the value was -0.40. In simple terms, this means that gold and dollars have a moderate inverse relationship. Gold and the dollar move in the opposite direction, but even that relation is not perfect and not very strong.

In statistics, if you square that correlation (-0.40 x -0.40), you get a value known as R-Squared (0.16) This value means that the Dollar’s movement only explains about 16% of why Gold moves.

Moving away from statistics, we looked at a simpler metric: how often do they actually move in opposite directions?

Over the last 20 years, gold and the dollar moved against each other 65.9% of the time.

It basically means that for every 10 days the dollar rises, gold will likely fall about 6 to 7 times.

• Volatility in Gold

We wanted to see how the 10-Year Treasury Yield environment impacts gold price’s stability. To do this, we compared two distinct scenarios in our 20-year dataset: a Low Yield environment and a High Yield environment.

We specifically looked at periods where yields were below 2% versus periods where they rose above 4% to see if Gold’s behaviour changed.

- The Low-Yield Test (Yields < 2%)

During periods where 10-year yields remained below the 2% mark, Gold showed a higher degree of price stability. The average daily price change was 0.03%.

In this environment, the opportunity cost of holding Gold is relatively low. Meaning, since traditional fixed-income assets provide minimal returns, there is less incentive for capital to move out of gold.

- The High-Yield Test (Yields > 4%)

When we looked at periods where yields were above 4%, the dataset showed a shift in Gold’s price movement. The average daily price change was 0.10%, which was over 3 times larger compared to the low-yield periods we tested.

This comparison might help explain why gold has been struggling recently.

After the US-Iran tensions started, bond yields began rising again. When bond yields rise, bonds start offering better returns, and investors have more incentive to move money into bonds instead of gold. This shift in capital can make gold prices more volatile and sometimes even push them down.

But there is one important thing to remember here.

High yields do not automatically mean gold prices will fall.

In fact, over the last few years, yields have been high, and gold has still risen.

This is because gold today is not driven only by interest rates. There are also structural factors influencing gold prices like central banks around the world buying gold, countries trying to reduce dependence on the US dollar, and geopolitical uncertainty pushing countries to hold more gold reserves.

So gold today is influenced by interest rates, the dollar, central bank buying, geopolitics, and global financial stability.

Historical Context: Significant Periods in the Dataset

• The 2008 Financial Crisis:

During the early stages of the 2008 crisis, especially around the fall of Lehman Brothers, markets behaved in a surprising way.

Gold did not immediately rise. In fact, it fell for a bit (though it held its ground better than the stock market).

The reason was simple. Investors were not looking for safety. They were looking for cash. The US Dollar Index rose as investors sold assets across the board to hold liquid dollars.

It can be said that during extreme fear (in the initial stages), the US dollar becomes the preferred safe haven over gold. Gold cannot be used for immediate needs, but cash can be.

• The 2011 Debt Crisis

In 2011, the US faced a credit rating downgrade, and Europe was dealing with a sovereign debt crisis.

Gold responded strongly and rose to its highest level at that time.

The key factor was interest rates. US yields were falling toward 1.5%. With such low returns, there was an incentive to hold gold. Gold saw steady gains with low volatility.

• The 2020 Covid 19 Pandemic

When COVID first hit global markets in March 2020, something very similar to 2008 happened initially.

Gold actually fell in the first few days of the market crash. The dollar rose sharply as investors again rushed to hold cash.

But then something changed.

Central banks around the world cut interest rates aggressively, and US Treasury yields fell to less than 1%. With yields almost at zero, bonds were giving almost no return.

At that point, holding gold became attractive again.

So after the initial fall, gold rose significantly during the pandemic period.

• The 2022 Russia-Ukraine War

The conflict following the Russian invasion of Ukraine followed the usual pattern.

Gold rose significantly as uncertainty increased.

Although the dollar remained strong, interest rates were still recovering from pandemic lows. Yields were typically in the 2.5% to 3.5% range and had not yet crossed the 4% level that becomes a major threshold.

Gold was able to rise despite dollar strength because interest rates were not high enough to pull investors away.

Gold Movements after a Crisis

One critical finding in this study was the performance of gold in the immediate 48-hour window of a crisis versus the next 30 days.

In the initial stage of a major shock (such as 2008, 2020), gold often experienced a price decline. This is due to institutional selling to meet margin calls and the immediate global demand for US Dollar liquidity.

Historically, gold only begins its ‘safe-haven’ climb once the initial panic for cash has stabilised.

The 2026 Reality

The US-Iran conflict coincided with the strengthening of the Dollar (a rise of 2.60% of the broad US dollar Index) which made gold expensive globally. High yields of 4.35% as of March 2026 means bonds are more attractive.

All of this could mean that gold is still in the liquidity phase. Moving money into bonds may seem more attractive than keeping in gold.

Conclusion

To say gold is not a safe asset anymore would not be fair. But the data from the last 20 years indicates that gold does not automatically rise during every conflict.

Its behaviour depends largely on macro conditions rather than just uncertainty itself.

At the same time, interest rates play a critical role in shaping gold’s attractiveness. When yields are low, gold faces little competition. But when yields rise meaningfully (even taking into account inflation) the opportunity cost of holding gold increases, making it less appealing relative to fixed-income assets.

The interaction with the US dollar further adds to this dynamic. In periods where the dollar strengthens alongside geopolitical tension, it can offset or even outweigh the traditional safe-haven demand for gold.

Gold’s performance is not driven by fear alone. It is shaped by the trade-off between liquidity, returns, and global capital flows.

Gold still remains a store of value, but not in isolation.

Limitations of the experiment

• The study is based on historical data and identifies correlations between different factors, not causation.

• It focuses mainly on the US Dollar and Treasury yields, while other factors like inflation, central bank actions, and investor sentiment are not fully captured.

• The analysis is based on historical data. Market behaviour can change across different cycles, so past patterns may not always predict future outcomes.

• Yield thresholds (such as 2% and 4%) are chosen benchmarks; different thresholds could lead to different results.

• The findings should be viewed as indicative trends rather than fixed rules. Short-term market movements can deviate from observed patterns due to unexpected events or shocks.

• Gold prices can vary across countries due to currency differences. This study uses USD-based prices and does not account for INR or other local currency effects.

Research Analyst Disclaimer

Recently, the Nifty 50 did what it has done many times over the last 20 years. It reached a point it had never been before.

All-time highs.

It crossed a new peak, moving past levels that, not too long ago, felt impossible. And like always, the reactions around it were very familiar.

All-time highs always divide investors like you and me. Some people start getting anxious and think this might be a good time to book profits. The market has been positive for a long time, portfolios are up, and it starts to feel like things are getting a bit too hot.

At that point, the thought is maybe it’s better to sell now and come back later when markets fall.

But the truth is, you can’t really tell when the market will fall.

And then one morning, the index hits another all-time high, and something clicks. You feel like you’ve seen enough. You sell everything, planning to re-enter when markets are lower.

But there are also other investors who see this very differently. To them, all-time highs aren’t a warning sign. They’re just part of how markets work. Markets grow over time, and if markets keep growing, they will keep making new highs. So instead of trying to exit, they just stay invested.

So in the end, you usually end up with two kinds of investors.

Those who sell at highs, and those who stay invested.

And what makes selling at highs so convincing is that it doesn’t feel like a mistake when you do it. It feels logical. It feels like you are being disciplined and protecting your profits.

But the real question is whether this actually works.

Can you really time the market by selling when it feels like markets are at a peak?

So instead of debating this theoretically, we decided to test it using actual data.

We took 20 years of Nifty 50 daily closing data, from January 2006 to March 2026, covering 5,017 trading days, and asked a very simple question:

Every time the Nifty hit an all-time high, what actually happened next?

The Experiment

First, we had to define what “feels like a peak.”

Because in real life, nobody knows the exact top. So we needed a clear rule.

We chose all-time highs.

An all-time high is when the Nifty reaches a level it has never reached before. This is usually the moment when many investors feel uncomfortable. Prices are high, markets have been going up for a while, and it starts to feel like “this can’t go much higher.”

So we treated every all-time high as a moment when a “peak seller” might decide to exit.

Then we looked at data from January 2006 to March 2026.

Every time the Nifty hit a new all-time high, we marked that day. But markets often keep hitting new highs continuously during a rally. So, to avoid counting too many similar points, we only considered highs that were at least 30 trading days apart.

After applying this filter, we got 47 such moments over 20 years.

We then asked two questions:

1. Was it actually a peak?
We defined a “real peak” as a situation where the Nifty fell at least 5% within the next 3 months. If it did not fall that much, then selling at that point would not have helped.

2. What happened after that day?
We tracked how the Nifty performed in the short term, looking at returns after 1, 5, 10, and 30 days.

Results:

Every dot on this chart shows a time when the market was at its highest level ever.

The red triangles show times when the market actually fell after that. The green circles show times when the market kept going up, so selling would have been a mistake.

There are almost twice as many green circles as red triangles.

Out of 47 times the market hit a new high, only 18 times it fell within the next three months.
The other 29 times, more than half, it just kept going higher.

So if your rule was to sell every time Nifty hits a new high, you would usually be wrong.

You would sell, then watch the market go up, and end up buying back at a higher price. You would also lose money on fees and taxes and go through unnecessary stress for a worse result than simply staying invested.

Unpredictability

If you zoom in on what happens right after an all-time high, one thing becomes clear. There is no clear pattern.

Across all 47 all-time highs, the Nifty went up the next day 23 times and went down 24 times. The average return was +0.13%, and the median was essentially zero. That is as close to random as it gets. An all-time high, by itself, does not tell you anything about what the market will do tomorrow.

Even if you extend the window slightly, the picture doesn’t change much. After 5 days and 10 days, the number of positive and negative outcomes is still fairly balanced. By 30 days, the market is positive more often (30 up vs 17 down), but that reflects the general upward drift of equities over time, not any specific signal from being at an all-time high.

But this average hides something important.

When you split these 47 moments into two groups (the ones that actually turned out to be peaks, and the ones that didn’t), the difference becomes obvious.

In the 18 cases where the market did fall meaningfully after, the data is consistently negative. The next day itself leans downward, and over 5, 10, and 30 days, returns remain negative on average. By 30 days, only 5 out of 18 cases were positive, and the average return was –1.47%. When it really was a peak, the market tended to drift lower afterwards.

In the remaining 29 cases, the exact opposite happens. These are the times when the market did not fall. It kept moving higher. The next day is more often positive, and that trend continues over longer periods. By 30 days, 25 out of 29 cases were positive, with an average return of +3.04%. When it wasn’t a real peak, the market usually continued rising, and quite strongly, too.

So in hindsight, the distinction is clean. Some all-time highs are genuine turning points, and others are just pauses before the next leg up.

But in real time, there is no difference.

On that day, all 47 moments looked the same, the market was at a new high after a strong run, and it felt like maybe things had gone too far.

But there was no clear sign to tell what would happen next, you could not know if this would be one of the 18 times the market falls or one of the 29 times it keeps going up.

That is why the next day looks like a coin toss, because in that moment it really is.

What you should conclude from this?

Even when you correctly identify a peak, it doesn’t play out the way you expect.

In most cases, the market does not fall immediately after the peak. It either goes higher or moves sideways first. In your data, this happened in 15 out of 18 cases.

Only in a small number of cases (3 out of 18) does the market fall quickly and make your decision look right immediately.

Even at real peaks, the drop took time

Among the 18 confirmed peaks, the fall didn’t arrive immediately. On average it took 27 trading days before the Nifty dropped even 5% from the peak.

What was actually happening during those 27 days of waiting

This is the most misunderstood part of the study.

When we say it took around 27 days on average for the fall to arrive, the natural assumption is that the market must have been drifting lower during that time. Slowly falling, day after day, validating the decision to sell.

That is not what happened.

To understand this properly, we looked at something very specific: what was the highest level the market reached after the all-time high before the eventual 5% fall arrived?

And that changes the picture completely.

In 11 out of 18 confirmed peaks, the market did not fall after the peak. It went higher first. Not sideways, not flat, but clearly higher.

This means an investor who sold at the all-time high would have watched the market move above their exit price, sometimes for days, sometimes for weeks. They were eventually right about the direction, but in real time it would not have felt that way.

Take July 2018. The Nifty hits an all-time high at 11,134 and you sell. Over the next 48 days, the market rose another 5.43%, reaching around 11,740. Only after that did it fall enough to qualify as a correction. For those 48 days, the market is trading above your selling price, with no clear sign that a fall is coming, only the feeling that the market keeps going up.

The same pattern shows up again in August 2024. The Nifty peaks at 25,018 and you exit. Over the next 55 days, the market climbs another 4.79%, touching nearly 26,250. Only then does it fall past the 5% mark. Again, the call was right, but only after a long stretch where the market moved against it.

There is another version of this.

In 4 out of 18 cases, the market did not go higher, but it did not fall either. It simply moved sideways for weeks before eventually dropping. From the outside, this is even harder to deal with, because there is no movement, no confirmation, and no clarity on whether to stay out or get back in.

And then there are the clean cases.

Only 3 out of 18 peaks behaved the way most people expect, where the market falls quickly and directly after the peak, like in November 2007, November 2010, and November 2013. These are the moments that make selling feel right, but they are rare.

So what this actually means is simple. Even when the peak is identified correctly, the experience does not feel right.

Most of the time, after selling, the market either moves higher or goes nowhere for weeks. Very rarely does it immediately confirm the decision, and that is where the real difficulty comes from.

Because in real time, there is no clarity on which situation is playing out. It is not possible to know whether this is one of the few cases where the market will fall immediately, or one of the many where it will rise further before falling, or just drift sideways.

So the reaction follows a pattern. You sell, the market goes higher, doubt sets in, and you buy back in, often at a higher price. And only after that does the fall come.

That is why timing peaks is so difficult in practice. Not because the direction is always wrong, but because the path makes it very hard to stick with the decision.

Note:
This study only looks at all-time highs that are at least 30 days apart. This helps avoid counting the same rally again and again. But it also means the study is only looking at specific situations, not every time the market feels expensive. For example, if someone sells on a random day during a bull run, that situation isn’t included here.

So the results only apply to all-time highs, not every selling decision.

Next, the study defines a “peak” as a 5% fall within three months. But this is just a choice. If 3% was used, there would be more peaks. If 10% was used, there would be fewer. So the exact numbers depend on this rule. Still, the main idea remains: most of the time, markets keep going up even after hitting new highs.

Research Analyst Disclaimer