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

A stock is up, or down and that’s what grabs attention.

But alongside every price move, there’s another number quietly changing in the background: trading volume.

Trading volume is the total number of shares being bought and sold on a particular day. It shows how active a stock really is. Whether everyone is piling in, or if the price is moving with just a handful of traders.

The general assumption is that a significant price increase on high volume indicates a strong trend, while a move on low volume is a ‘fake-out’.

So we decided to look at volume and price together. Does one move before the other, or do they just react at the same time? How do they react during significant rises and falls?

The Experiment

We looked at the trading volume and price data of the biggest 3 stocks of the Nifty 50:

• Reliance Industries

• HDFC Bank

• Bharti Airtel

Together, these stocks themselves account for more than 20% of the index weight. If these giants move, their effects can be seen on the stock markets.

We looked at data in a 3 year time frame from 20 March 2023 to 20 March 2026.

Simultaneous Moves

Do volume and price move together?

We looked at the days when the stock price went up or down and found out the number of days when the volume followed the same trend.

There were 746 total trading days in the dataset, but the days where the market was flat have been excluded.

Here is what we found:

Reliance Industries

Here, rises in stock prices are almost equally likely to have rising or falling volume, while price drops more often attract higher trading activity, though not by a lot.

HDFC Bank

HDFC Bank data shows a similar trend to Reliance. Rising prices show no clear volume pattern, but declines generally see more traders stepping in.

Bharti Airtel

Here, rises in stock prices are often (more than 50%) supported by higher volume, while price drops mostly occur on lower volume activity.

The tables show that the chance of price and volume moving in the same direction hovers around 50% for the 3 stocks.

Rising prices are just as likely to occur on falling volume as on rising volume, and the same goes for price declines. This means that volume does not consistently confirm price moves, and there is no clear evidence that one leads the other.

From this dataset, we cannot conclude that price or volume reliably signals the next move.

Top Gains and Losses

We looked at the top gains and losses faced by these stocks during this 3 year period.

How did the trading volume affect or get affected by these gains and losses?

Reliance Industries

Reliance saw the highest gain of 7.02% in Jan 2024.

During that day, the trading volume increased by around 102.3%

But neither the stock price or the volume showed any kind of rising trend over the next few days

Reliance also saw a loss of almost 7.81% in July 2023. But the trading volume did not show any significant change, rising 7.22%.

There was also a fall of 49.76% in a day in Oct 2024 which was due to the issue of bonus shares. During the day, the trading volume rose 16%.

Surprisingly, on the day the trading volume rose the highest (almost 2500% in a day), the change in stock price was only 0.09%, essentially flat.

HDFC Bank

HDFC Bank saw its highest gain in the data set in June 2024 of 4.63%.

The trading volume fell by 8.82%, though not a significant change.

The stock saw its highest fall of 5.32% in March 2026 during which the trading volume rose 630.15%.

The Bank issued bonus shares in Aug 2025, which made the stock fall 50.44% in a day. The trading volume was significantly high, a 93% rise in a day.

Here too, when the trading volume saw the highest rise at 4040.5% the stock price did not change much, falling 0.49%.

Bharti Airtel

Airtel saw its highest gain in a day in July 2024 of 4.50%

The trading volume saw a gain of 123.6%. The price rise did not show the same trend afterwards.

In June 2024 Airtel saw a one day loss of 6.57%. The trading volume saw a rise of 231% that day.

The stock price rose in the days following that.

Here too, in the highest volume change in the data set of 4280% in march 2024, the stock price did not show any major change, rising only 0.24%

What’s the Verdict?

Our three-year study reveals a simple but important truth: volume does not consistently predict price movements.

Across the dataset, the chance of price and volume moving together hovers around 50%. Rising prices can occur on falling volume, and declines can happen without massive trading activity.

Looking at extreme moves reinforces this insight.

Some of the largest gains happened on low or declining volume, showing that price can climb quietly when sellers step back.

Conversely, massive volume spikes (even up to 4,000% in a single day) often resulted in little or no price change, which shows that institutional block trades do not reflect market sentiment.

Price drops, however, did tend to coincide with higher volume, suggesting that panic or urgent selling drives activity more than gradual accumulation.

In short, volume measures liquidity and intensity, not direction.

It can provide context, but it cannot reliably confirm trends or predict the next move.

More in-depth statistical analysis might reveal some insights, but for common investors More in-depth statistical analysis might reveal hidden insights, but for common investors, the lesson is straightforward: quiet moves are not necessarily fake, and loud volume surges are not always meaningful.

Limitations of the Experiment

While our study gives useful insights, it has some boundaries:

Limited Sample: We only analysed the big 3 stocks: Reliance, HDFC, and Airtel. Smaller or mid-cap stocks may behave differently, and volume might matter more for them.

Timeframe Bias: The study covers 2023-2026. Market behaviour can change in different cycles, like bull markets or recessions.

Block Deals: Daily volume doesn’t separate retail trades from institutional block deals. Huge spikes (like 4000%) often reflect pre-arranged trades with no real market impact.

External Factors Excluded: We only looked at price and volume. Global news, earnings, or policy changes can override technical patterns.

Corporate Actions: Some big price drops (like 50%) were due to bonus issues. Without context, raw data could mistakenly flag these as crashes.

Research Analyst Disclaimer

Each investor has their own style of investing. Some look at earnings, some look at growth, and some look at stability.

So we wondered, what if we invested using two very different strategies in the Indian market?

We decided to compare 2 indices: the Nifty Alpha 50 and the Nifty100 Low Volatility 30.

These indices act as proxies for two different sets of stocks, representing high-momentum winners and stable low-volatility performers, respectively.

The Nifty Alpha 50 tracks 50 stocks that delivered the highest alpha over the past year, aiming to capture strong momentum (Alpha measures how much a stock or portfolio outperforms its benchmark. In this case, Nifty 50).

The Nifty100 Low Volatility 30 selects 30 stocks from the Nifty 100 that move the least, prioritising stability and lower risk.

Why compare these 2 indices?

These 2 indices are widely different investing approaches.

The Alpha 50 focuses on recent market winners, aiming for higher returns, while Low Volatility 30 chooses stable stocks that move less, prioritizing safety.

Low Volatility 30 sticks to the top 100 large-cap stocks, offering safety and consistency, whereas Alpha 50 looks at the top 300, including mid-caps, which gives exposure to faster-growing but potentially riskier stocks.

The goal is to see if the higher returns of alpha are worth the extra risk, or if slow and steady actually wins the race.

The Experiment

We compared historical data of the two indices from January 2015 to February 2026, a period of 134 months.

Let’s imagine an investor who put Rs 1 lakh into each index in 2015.

This is how the returns look like.

Over the decade, the Alpha 50 index gave nearly double the Low Volatility returns, with a CAGR of about 17.5% vs 12%.

But returns alone don’t tell the full story.

Logically, we know this because the Alpha 50 index focuses on stocks which are able to grow faster during bull markets.

At the same time, this strategy carries higher risk, meaning the swings (both up and down) are much sharper than a low volatility approach.

Understanding the Risk

To see the real difference, we looked at maximum drawdown, which is the largest drop the indices experienced from peak to trough.

During the 2020 COVID-19 crash,

• Nifty Alpha 50 fell 38.3% in around 32 days.

• Nifty100 Low Volatility 30 fell 31% in the same period.

Recovering from these drops isn’t symmetrical.

Mathematically, after a 31% drop, Low Volatility needed a 45% gain to break even, whereas Alpha 50 needed a 62% gain.

In practice, Alpha 50 took two months longer to recover and reach its previous position.

Daily Volatility and Single-Day Shocks

The difference in daily swings makes the risk more tangible.

On most days, Low Vol 30 moves less than 1%. Alpha 50, however, swings more frequently, creating rapid gains but also sharp drops.

Some notable examples:

• June 4, 2024 (General Election results): Alpha 50 dropped -10.55%, Low Vol 30 fell -1.97%.

• March 23, 2020 (COVID panic): Alpha 50 fell 12.89%, Low Vol 30 fell 10.37%.

• 2024-2026 peak-to-trough: Alpha 50 dropped 17.7%, Low Vol 30 fell 5.4%.

Here is a side-by-side comparison of the daily changes of the 2 indices:

The Verdict: Two Paths, Two Priorities

This 11-year experiment shows that both strategies ended up in the green, but they offer very different experiences for an investor’s portfolio and peace of mind.

There is a clear difference in final wealth. Alpha 50 turned Rs 1 Lakh into Rs 6.08 Lakh, nearly double Low Vol 30’s Rs 3.53 lakh.

But this gap in reward came with something we can call a ‘stress tax’. During volatile periods, an Alpha investor may see 10% of their portfolio value vanish in just a few days — a scenario that occurred with the calmer Low Vol stocks very rarely.

The indices also grew wealth in different ways. Even during the shaky markets, Low Vol offered a smoother path than the Alpha 50, cushioning the portfolio against sharp downturns.

Timing matters too. A Low Volatility investor can generally predict a range for their portfolio at any given time, while an Alpha investor faces “timing risk.” For example, withdrawing in 2025 could have meant selling at an 18% loss, whereas Low Vol investors would have seen much smaller declines.

The best strategy isn’t necessarily the one with the highest returns on paper, but the one an investor can stick with. Alpha 50 offers a high-performance ride for those who can tolerate turbulence. Low Volatility 30 provides steadier, more predictable growth, prioritising capital protection and consistency.

In the world of compounding, momentum can create higher wealth, but stability may make it easier to stay invested.

Research Analyst Disclaimer

The US Federal Reserve had sharply raised interest rates to levels not seen in decades. By 2025, US government bonds were offering yields close to 5%.

That might sound like a small detail. But for global investors managing trillions of dollars, it completely changes the math.

When a risk-free asset in dollars starts offering around 5%, the incentive to take additional risk falls. Investors begin comparing that guaranteed return with the volatility and currency risk present in emerging markets like India.

And that comparison quietly reshapes capital flows.

This is where Foreign Institutional Investors (FIIs) come in.

FIIs represent large global pools of capital: pension funds, sovereign wealth funds, hedge funds, and international asset managers. Unlike domestic investors, they allocate money across multiple countries, constantly shifting capital toward the best risk-adjusted opportunities.

Most importantly, they think in dollar returns, not rupee returns.

So when US yields rise and the dollar strengthens, emerging markets automatically start looking less attractive on a relative basis.

That shift rarely appears directly in headlines about Indian equities. But it shows up clearly in capital flows.

And that is exactly what happened in 2025.

As US yields stayed elevated and the dollar remained strong, FIIs gradually reduced their exposure to Indian equities. Over the course of the year, net selling reached roughly Rs 1.6 lakh crore.

What is interesting is that while FIIs were selling, Domestic Institutional Investors (DIIs) were steadily buying.

Mutual funds continued receiving strong SIP inflows from retail investors. Insurance companies deployed long-term capital. Pension funds and domestic asset managers added liquidity to the system.

The supply created by foreign selling was absorbed by domestic participation.

The result was interesting. Despite heavy foreign selling, markets did not collapse proportionately.

If FIIs sold over Rs 1.5 lakh crore and markets did not crash proportionately, then maybe the relationship is not as simple as we think.

For years, many investors have assumed that FIIs move the Indian market. When they buy, it is seen as positive. When they sell, it is seen as negative.

But is that really true?

Do FIIs actually decide where the market goes?
Or are they reacting to global conditions that also influence prices?
And when they sell, does that automatically cause markets to fall?

We studied the data from 2007 and collected daily FII flows, daily DII flows, and daily Nifty returns. We examined whether FII activity predicted market movement or whether markets moved first and FIIs reacted.

The Experiment

To test whether FIIs really move the market, we went beyond 2025 and studied long-term data.

We collected daily FII flows, DII flows, and Nifty returns starting from 2007. That gave us more than 4,400 trading days across different market cycles

Starting from 12-Nov-2007 to the latest available date

Total observations:

• 4,402 trading days (Daily data)

• 221 months (Monthly aggregated data)

Study 1: Same-Day Relationship

We compared FII buying and selling with the market’s movement on the same day.
At the monthly level, we compared net institutional flows with monthly market returns.

This shows whether institutional activity and market movement tend to align in the same period.

Study 2: Who Moves First

We shifted the dataset by one day and checked whether yesterday’s institutional flows were followed by market movement the next day.

This helps see whether flows tend to lead the market or react to it.

Study 3: When FIIs Sell

We examined periods where FIIs were selling while DIIs were buying, both at daily and monthly levels.

This helps show whether domestic investors are absorbing foreign selling pressure and helping stabilise the market.

Results

STUDY 1

We first looked at whether institutional buying or selling on a given day aligns with how the market closes that same day.

The dataset covers 4,402 trading days.

The table below shows what tended to happen when FIIs or DIIs were buyers or sellers on a given day.

Looking at the daily data, the relationship between institutional flows and market direction is fairly weak.

When FIIs bought, the market went up about 56% of the time. That is slightly better than random, but not strong enough to say FIIs reliably move the market on a given day.

When FIIs sold, the market still went up almost half the time. So daily FII selling does not automatically result in a falling market.

The same pattern appears with DIIs. Their daily buying or selling does not consistently determine whether the market closes higher or lower.

However, once we zoom out to the monthly level, the picture changes significantly.

The table below shows how the market behaved during months when institutions were net buyers or sellers.

At the monthly level, the pattern becomes much clearer.

When FIIs were net buyers for the month, the market ended the month higher about 70% of the time.

When FIIs were net sellers for the month, the market ended lower about 72% of the time.

This suggests that over longer periods, FII behavior tends to move with broader market trends. They typically buy during strong phases and reduce exposure during weaker periods.

DIIs show almost the opposite pattern.

When DIIs were buyers during the month, the market actually ended down more often than up.
When DIIs were sellers, the market ended up in nearly 73% of those months.

This suggests DIIs often behave counter-cyclically. They tend to buy during weaker phases and sell into strength, effectively providing liquidity when foreign investors are moving strongly in one direction.

Putting both views together gives a clearer picture.

At the daily level, institutional flows and market direction are only loosely related. The numbers stay close to 50%, which means day-to-day movements are mostly noise.

At the monthly level, patterns start to appear.

FIIs tend to move with market trends, increasing exposure during strong phases and reducing it during downturns. DIIs often behave in the opposite way, buying during weakness and selling during strength.

But even here, the interpretation should remain careful.

The data does not mean institutions directly control the market. Instead, both flows and market direction appear to be responding to larger macro forces such as liquidity conditions, global risk sentiment, and economic trends.

The key takeaway from Study 1 is simple.

The idea that “FIIs move the market” is too simplistic.

At the daily level, the relationship is mostly noise. At the monthly level, there is stronger alignment, but it reflects trend participation rather than direct control. Markets and institutional flows tend to move together during sustained cycles because both respond to the same broader forces.

STUDY 2

To test whether institutional flows lead the market or react to it, we shifted the dataset by one trading day.

Period analysed: 12 Nov 2007 onwards
Total observations: 4,401 trading days

This allows us to compare yesterday’s activity with what happens today.

The table below shows how yesterday’s institutional activity and market movement relate to what happens the next trading day.

What it tells us

Yesterday’s institutional flows do not predict today’s market move.

If FIIs bought yesterday, the market went up today about half the time.
If they sold yesterday, the market still went up about half the time.

That is essentially random.

But when the direction is reversed, a pattern appears.

After the market rises, FIIs are more likely to buy the next day. After the market falls, they are more likely to sell. This suggests FIIs often follow short-term momentum.

DIIs show a mild tendency to move the other way. They are slightly more likely to sell after market rises and buy after declines, which is consistent with a liquidity-providing role.

However, even this pattern should not be interpreted too strongly.

The highest number in the table is 63%. This means that after the market rises, FIIs buy the next day about 6 out of 10 times. That shows a tendency, but it is far from a reliable rule. In roughly 4 out of 10 cases, the opposite still happens.

So while the numbers hint that institutions may react to recent market movement, the evidence is not strong enough to make a firm conclusion about causality.

The safest interpretation is that daily movements in markets and institutional flows are loosely related, but neither consistently predicts the other. Both are likely responding to the same broader forces, such as liquidity, news, global markets, and investor sentiment.

STUDY 3

When FIIs Sell

The final study looks at a simple but important question.

When FIIs sell, what do DIIs do? Do domestic institutions absorb the selling? And if they do, does that prevent the market from falling?

We examined this at both the daily level and the monthly level.

Daily data

Dataset: 4,402 trading days

The table below shows what happened on days when FIIs were net sellers.

FII Selling Days

What this shows

On the 1,973 days when FIIs sold, DIIs bought on 1,580 days.

That means DIIs stepped in to buy on roughly 80% of FII sell days. This clearly suggests domestic institutions frequently absorb foreign selling.

However, this absorption does not prevent market declines.

The market still fell 988 times, and 807 of those declines happened even when DIIs were buying.

In other words, domestic buying often reduces imbalance, but it does not necessarily stop the market from falling.

Monthly Data

Dataset: 221 months

The table below shows what happened during months when FIIs were net sellers.

FII Negative Months

At the monthly level, the absorption pattern becomes even stronger.

When FIIs were net sellers for the month, DIIs bought in 84 out of 86 months. That means domestic institutions almost always step in when foreign investors sell.

But even then, the market still fell in 62 of those months.

So while DIIs absorb the flows, that absorption does not override broader market direction.

This reveals a clear structural pattern.

DIIs consistently absorb FII selling. This happens both at the daily level and the monthly level, and it becomes even stronger during large foreign outflows.

However, absorption does not guarantee market stability.

Even when domestic investors step in, markets can still decline if broader conditions remain weak. Global risk sentiment, liquidity cycles, interest rates, and macroeconomic expectations continue to play a much larger role in determining trend direction.

The deeper takeaway is simple.

DIIs act primarily as stabilisers. They provide liquidity and help prevent extreme dislocations.

But they do not necessarily reverse trends.

When FIIs sell heavily for extended periods, the broader macro environment tends to dominate. Domestic absorption can cushion volatility, but it does not fully offset sustained global capital movement.

In short, absorption helps stabilise markets, but it does not control market direction.

Research Analyst Disclaimer

Over the next few years, Warren Buffett invested about $1.3 billion into the company. It wasn’t a startup. It wasn’t unknown. It was already a global brand.

So why did he put such serious money into a soft drink business?

Because Buffett wasn’t just looking at sales growth.

He was looking at the economics of the business.

Coca-Cola did not need to constantly pour billions into new factories just to survive. It did not require heavy reinvestment every year to defend its position. The brand, distribution network, and pricing power did most of the work.

For every dollar of capital the company used, it generated strong profits.

Now contrast that with airlines. That is an industry Buffett had criticised for decades. Airlines required massive investment in aircraft, maintenance, and infrastructure. Competition was intense. Pricing power was weak. Even after investing enormous capital, returns were often disappointing.

Both were large companies.

But one turned capital into cash far more efficiently.

And that brings us to a practical question.

Most investors don’t have Buffett’s instinct or decades of experience.

But the principle he used is measurable.

He preferred businesses that earned high returns on capital.

In simple words, companies that could take Rs 100 and reliably turn it into Rs 20 or Rs 25 year after year — without needing more money.

That is what people loosely call “quality.”

But instead of leaving it vague, finance gives us two tools to measure it:

To see how this works, imagine a simple example.

Suppose shareholders have invested Rs 100 into a company.

If the company earns Rs 20 in profit, that means it generated a 20% return on the owners’ money.

That is ROE.

It tells us how effectively management is using shareholders’ capital.

Now consider a slightly different situation.

Many businesses don’t run only on shareholders’ money. They also borrow.

So imagine the company uses Rs 100 of shareholders’ capital and Rs 100 borrowed from a bank. Now it is operating with Rs 200 in total.

If it earns Rs 30 in operating profit, then the relevant question becomes:

How efficiently did it use the entire Rs 200?

Rs 30 on Rs 200 is 15%.

That is ROCE.

ROCE measures how well the business generates operating profit from all the long-term capital it uses — equity plus debt.

Both metrics are trying to answer the same underlying question:

If you give this company Rs 100, how much profit does it produce?

And more importantly, can it keep doing that consistently?

Together, they help investors judge whether a company is merely growing or actually compounding capital efficiently.

To answer that, we needed to define what “high” really means.

Because saying “anything above 15% is good” sounds neat, but it ignores a basic reality: different sectors operate under different economics.

An IT company can scale revenue without building massive factories. An FMCG company benefits from brand power and distribution strength. Naturally, these businesses often earn structurally higher returns on capital.

On the other hand, sectors like telecom, power, oil & gas, and heavy manufacturing require enormous investments in infrastructure. Even well-run companies in these industries may operate at lower return levels simply because of the capital intensity of their business.

So comparing all companies using a single flat benchmark would be misleading.

So we used a sector-adjusted framework.

Step 1: Use the right return metric for each sector.

• ROE was used for Financial Services, IT, and FMCG — sectors where equity efficiency is the key driver.

• ROCE was used for all other sectors, where total capital employed gives a better picture of operating efficiency.

Step 2: Focus on consistency, not one strong year.

Returns can spike temporarily due to cycles, especially in metals, energy, and commodities.

To avoid capturing short-term peaks, we used each company’s 5-year average ROE or ROCE as the primary measure.

Step 3: Compare against a sector benchmark.

For each sector, we defined a reasonable “healthy” return level based on long-term industry norms.

A company qualified only if its 5-year average return exceeded that sector benchmark.

This ensures a fair comparison.

A power company is not expected to generate FMCG-level returns.

But an FMCG company should not qualify as “high quality” with a return that is merely average for its own industry.

The final shortlist: 13 Nifty 50 stocks that cleared the bar

The 13 companies qualified because they met three strict criteria:

• The correct metric was used for each sector — ROE where equity efficiency matters, ROCE where total capital efficiency matters.

• We used the 5-year average ROE or ROCE to capture sustained performance, not a one-year spike.

• Each company’s 5-year average had to exceed a sector-specific benchmark (benchmarks based on standard finance definitions and historically observed return ranges).

Note: IT and FMCG are capital-light and typically show higher structural returns; telecom, power and other capital-heavy sectors typically show lower structural returns. Only companies that were genuinely strong relative to their sector passed the filter.

High ROE/ROCE shortlist

The Study: What the Numbers Actually Say

Let us put this to the test with real money and real time.

The setup: We ran a fixed monthly SIP of Rs 10,000 in these 13 stocks, split equally per stock, starting from December 2014 and continuing every month until December 2025. That is 133 monthly installments.

On the other side, we ran an identical Rs 10,000 monthly SIP in the Aditya Birla SL Nifty 50 Index Fund (Direct, Growth), which is one of the oldest and most widely tracked Nifty 50 index funds over the same period.

Both portfolios started with the same money, on the same dates, for the same duration. The only difference was what that money bought.

Results across 133 months (Dec 2014 – Dec 2025):

The high ROE/ROCE portfolio turned Rs 13.3 lakh into Rs 40.1 lakh. The Nifty 50 index turned the same Rs 13.3 lakh into Rs 29.1 lakh.

That is a difference of Rs 11.06 lakh in final wealth, from the exact same monthly investment, over the exact same period.

In annualised terms, the gap is 5.38% XIRR. That might not sound dramatic. But compounded over 11 years of SIP, those extra percentage points are the difference between creating Rs 15.7 lakh of wealth and creating Rs 26.8 lakh of wealth.

How did individual stocks perform?

Not every stock was a winner. That is an important thing to say.

Here is how each of the 13 stocks performed on XIRR over the full period, ranked highest to lowest:

(Each stock received a Rs 769.23/month SIP for simplicity. The “vs. Index Fund XIRR” column shows each stock’s XIRR minus the index fund’s 13.56% XIRR)

A few things immediately stand out.

The top three, Bharat Electronics, Bajaj Finance, and Bharti Airtel, did the heavy lifting. Bharat Electronics alone generated a final value of Rs 7.85 lakh from a Rs 769 monthly SIP. That is a 34.36% XIRR over 11 years, which is exceptional.

Some well-known “blue chips” actually underperformed the index. TCS, Infosys, Asian Paints, Coal India, and HDFC Bank all delivered lower XIRR than the Nifty 50 index fund. This is a reminder that even “quality” companies are not immune to valuation cycles, sector headwinds, and stagnant stock performance over certain periods, even if their businesses remain fundamentally sound.

Power Grid, ICICI Bank, and Nestle roughly matched the index, with modest positive or negative performance.

The portfolio wins not because every stock won, but because the ones that won, won big enough to carry the overall portfolio ahead of the benchmark.

A note on what this study does and does not tell us

This is a backtested, hindsight study.

We are not predicting that the same 13 stocks will outperform the index over the next 11 years. ROE and ROCE are useful filters, but they are not crystal balls.

What this study does tell us is that a simple, rules-based filter, “pick only companies that are genuinely efficient at turning capital into profit, compared to their own sector’s standards” would have given a long-term, disciplined SIP investor meaningfully better results than a passive index over an 11-year period.

Conclusion

The answer, at least over the considered period, is: you do noticeably better.

Not because you got lucky, and not because you picked obscure small-cap stocks. These are all large, well-known Nifty 50 companies. The only thing different was the filter applied before buying.

By insisting that each company in the portfolio must earn returns that are genuinely high relative to its own sector, using the right profitability metric for that sector, averaged over 5 years to filter out one-time spikes, the portfolio delivered an 18.94% XIRR versus 13.56% for the Nifty 50 index fund.

That gap of 5.38% XIRR translated into Rs 11 lakh more wealth from the same Rs 13.3 lakh invested.

There is a deeper lesson here too. “Buy quality” is advice that gets thrown around constantly in investing circles, often without much substance behind it.

Quality means capital efficiency, measured consistently, compared fairly within each sector.

And when quality is defined that way, and when you simply stay invested, the data suggests it might turn out to be worth the extra step of thinking before buying.

A common phrase implying how greatly the American economy affects the rest of the world.

This is largely true for India as well. After all, the US is one of the biggest buyers of Indian exports.

And it is not just about India. In fact, the US is the world’s biggest importer, a true ‘shopaholic.’

In 2023 alone, the country bought $3 trillion worth of goods from around the globe. To put that in perspective, this figure is greater than Italy’s entire GDP and almost five times larger than India’s total imports that year.

Given its sheer size, any movement in the US economy is bound to be felt by others as well.

But the question is, to what extent?

When it comes to the Indian economy and markets, how strongly are we impacted by developments in the US?

To know this, we tried checking how closely India mirrored the mood of the US markets.

We examined the period beginning 2005 to determine the frequency with which the Nifty 50’s daily closing sentiment mirrored that of the US market on the preceding day.

For example, if the S&P 500 closed in the red on 3rd January 2005, did the Nifty 50 also close in the red on 4th January 2005, or did it move in the opposite direction?

If the answer was “yes” for most trading days, it means that US markets play a significant role in influencing Indian markets; if not, then maybe we are just not as intertwined as initially assumed.

So here is what we found:

(for period between Jan 2005, to 20 Feb 2026)

So in the last 21 years, 59.7% of the time, or almost 2 out of 3 times, the Nifty 50 followed the footsteps the US left a day earlier.

While this looks like a decently strong link, let us not hurry with any conclusions and look further into.

We further checked how the Nifty behaved every time US markets made big moves (<-1% or >1%) per trading session.

Basically, if S&P went up by more than 1%, or fell -1% or lower, did Nifty 50 also rise or fall with a matching intensity the next day?

Here is what we found by looking at the last 21 years of data:

(for period between Jan 2005, to 20 Feb 2026)

Nifty 50 matched the significant market movements (<-1% or >1%) of the S&P 500 in only 33.7% of cases, or approximately one out of three times.

This was lower than the 59.7% directional alignment we found earlier.

It’s interesting to see that while there is a considerable alignment on the overall directional movement of the US & Indian markets, the intensity with which that happens may vary.

We can witness it in some more instances too.

For example, Indian markets did crash alongside the US markets in the 2008 financial crisis, but the Nifty touched the pre-crash peak in a little over 480 days, while S&P 500 took more than 970 days to recover to pre-crash levels.

(recovery period in approx trading days, from trough to pre-fall peak)

Hence, although there are notable overlaps between the Indian and US markets, asserting that the Indian markets are a perfect reflection of the US markets would be an overstatement.

Note:

• The analysis results may differ if a different timeline, country, or indices are considered.

• In the comparison b/w S&P 500 (T) and Nifty 50 (T+1) comparison, for ease of calculation some dates have been omitted due to respective holidays in each market.

Airports closed. Offices shut down. Stock markets fell. People were scared and unsure about what would happen next.

But even during all that chaos, some things did not stop.

Pharmacies stayed open. Grocery stores kept running. Trucks continued delivering food and supplies.

In fact, during times like this, people often buy even more essential items because they want to be prepared. They stock up on medicines, toothpaste, cooking oil, and packaged foods.

This shows something important.

When the economy slows down, people delay big purchases. They postpone buying houses. They avoid upgrading cars. They cancel travel plans. They cut down on extra spending.

But they don’t stop buying basic necessities.

The demand for essential goods might change a little, but it does not fall sharply. And this difference can also be seen in the stock market. Companies that sell essential products usually remain more stable compared to companies that depend on optional or luxury spending.

Over time, investors began grouping such businesses into what are commonly called “defensive sectors”

Defensive sectors are about needs, not wants.

People might skip buying a new phone or delay purchasing a car. But they still need medicines, basic food, hygiene products, and electricity.

Because people continue buying these essentials, companies in these sectors usually do not see a big drop in sales when the economy slows down.

Industries like pharmaceuticals, healthcare services, utilities, and FMCG (fast-moving consumer goods) fall into this category. Whether the economy is doing well or going through a slowdown, people still spend money on these basic items.

In the stock market, these companies are often called “low-beta” stocks.

Beta is simply a measure of how much a stock moves compared to the overall market. If the market goes up or down sharply, low-beta stocks usually move less in comparison.

If a stock has a beta below 1, it usually moves less than the overall market. Defensive stocks often fall into this category.

This means they may not rise as much when the market is going up strongly. But they also usually do not fall as much when the market is going down.

Because of this, many investors think of defensive stocks as a safer choice during weak or uncertain markets. The idea is simple: if markets fall, these stocks should fall less.

To see if this is actually true, we decided to study how defensive sectors behave using real data.

The Data We Studied

We analysed 10 years of daily market data, from October 2015 to December 2025.

That gave us a total of 2,543 trading days.

Out of these:

• The Nifty 50 fell on 1,162 days

• It rose on 1,373 days

We then narrowed our focus only to the down days. Especially the days when Nifty fell more than 1%, 2%, and 3%, along with the worst 10% of all down days.

Specifically:

• Nifty fell 1% or more on 251 days

• Fell 2% or more on 56 days

• Fell 3% or more on 19 days

Then we checked how often each sector actually rose on those red days.

This table shows many times a sector rose (closed positive) on days when the broader market was falling sharply.

Pharma, healthcare and FMCG were positive more often on red days compared to most other sectors.

Banks, financials, realty, and similar sectors almost never turned positive during the fall days. In fact, in the most extreme days (-3% or worse), banks and financial services were never positive.

This suggests that on sharp red days, defensive sectors tend to hold up better than cyclical ones.

That was our first signal. So we decided to go deeper.

Study 2: Looking at Full Market Falls

Daily red days are short-term reactions. But what about full corrections and crashes?

To test that, we studied data from January 2010 to February 2026 (~16 years)

During this period, the Nifty experienced 23 distinct market falls of more than 5%. We labelled each of these as an “event.”

How we defined a “market fall”

We defined a market fall as:

• The Nifty reaches a recent high.

• Then declines,

• And the drop from that high to the lowest point of that decline is at least 5%.

Each event is a clear “high to lowest point” window. Once the market starts recovering meaningfully, the next fall is counted separately.

What We Measured in Each Event

For each of the 23 market-fall windows, we measured 2 things for

• Nifty 50 TRI

• Pharma TRI

• Healthcare TRI

• FMCG TRI

1. High-to-low fall (%)
   How much did each index fall during that same correction?

2. Return from Jan 2010 to the lowest point (%)
   If someone had invested in 2010 and simply held on, where would their portfolio stand at the worst point of that fall?

This helps us understand two things:

• How painful the correction was in that moment

• How much long-term wealth was affected

We grouped the 23 events based on how severe they were:

• 5%-10% falls

• 10%-20% falls

• 20%-50% falls

Then for each group, we listed the exact numbers for Nifty and each sector.

This clearly showed us when Pharma, Healthcare and FMCG behaved defensively and when they did not.

5% to 10% Market Falls

Out of the 23 times the market fell more than 5%, it declined between 5% and 10% on 12 occasions.

You can see the times highlighted in blue are the times the market fell between the range of 5% to 10%.

You can clearly see that, when we examine these 12 periods, the behaviour of defensive sectors is mixed but directionally supportive.

Pharma fell less than the Nifty 7 times.
Healthcare fell less than the Nifty 8 times.
FMCG fell less than the Nifty 9 times.

This means that more often than not, these sectors did provide relative downside cushioning during small corrections. In a few instances, Pharma, Healthcare, or FMCG were even positive while the Nifty was falling, indicating that defensive rotation does occur at times.

But the protection was not consistent.

There were periods, such as early 2018 and mid-2019, when Pharma and Healthcare fell more than the broader market. So defensive sectors are not automatically safer during mild pullbacks.

The clear takeaway is that defensive sectors tend to fall less than the market in small corrections more often than not, but the advantage is modest and not guaranteed.

10% to 20% Market Falls

Out of the 23 instances where the market fell more than 5%, it declined between 10% and 20% on 8 occasions.

During these periods, no sector remained positive. When markets correct by 10% to 20%, almost everything falls. Defensive sectors are not spared.

However, what stands out is the behaviour we were looking for. In most of these cases, the selected defensive sectors fell less than the Nifty 50.

Pharma fell less than the Nifty 6 times.
Healthcare fell less than the Nifty 6 times.
FMCG fell less than the Nifty 5 times.

So in most medium-sized corrections, these sectors did decline by a smaller amount than the broader market. They were not immune, but they generally fell less.

The key takeaway is that as drawdowns are in the 10-20% range, defensive sectors tend to fall less compared to mild corrections. But they still fall. There is no protection from losses. They fall during market stress, just usually by a smaller magnitude.

Now, what does this actually mean for a real investor’s wealth?”

Let’s look at it from a returns perspective.

If someone had invested in January 2010 and simply held through these 10-20% market corrections, how would their total returns look at the trough of each fall?

This table shows what an investor would actually see in their portfolio during those 10-20% market corrections if they had started in January 2010.

In the early years (E01 and E02), the market had not risen much yet. So when Nifty fell around 10%, the total returns were still low or even negative. Defensive sectors did not have a large lead at that point either. The fall felt direct.

But from 2013 onwards, things look very different.

During those corrections,

Nifty gave returns of +113%, +130%, +240%, or +406%
Defensive sectors were often at +157%, +242%, +281%, +500%, even +772%

So even though all sectors fell during the correction window, defensive sectors were usually sitting at higher overall return levels from the starting point. That reduces the effective damage to long-term wealth.

This means that even though defensive sectors also fell during those corrections, their overall gains from the starting point were usually larger. So the investor still remained in more returns compared to Nifty.

The simple takeaway is this:

Defensive sectors do fall when markets fall. But over time, they often build higher total returns before those corrections happen. So when the fall comes, the overall portfolio still remains at a higher level compared to the broader index.

20%-50% Market Falls

Out of the 23 instances where the market fell more than 5%, it declined by more than 20% on 3 occasions.

These were the major market crashes since 2010-2011, 2015-16, and the Covid crash in 2020.

The bar graph makes the pattern much clearer.

Unlike small or medium corrections, defensive behaviour here was consistent across all events. In each of the three crashes:

• Pharma fell less than the Nifty — 3 out of 3 times.

• Healthcare fell less than the Nifty — 3 out of 3 times.

• FMCG fell less than the Nifty — 3 out of 3 times.

In the 2011 crash, when Nifty fell 27%, FMCG actually rose by 7.83%, while Pharma and Healthcare fell much less than the broader index.

During the 2015-16 fall and the 2020 Covid crash, all three sectors declined, but the magnitude of the fall was significantly smaller than that of the Nifty.

This shows that in severe market falls, defensive behaviour becomes strongest. The sectors are not immune; they still fall, but they very clearly fall less than the broader market. The gap in decline is meaningful, often reducing losses by a large margin.

Now let’s look at this from a total returns perspective.

If someone had invested in January 2010 and simply held through these three major crashes (20–50% falls), what would their portfolio look like at the bottom of each fall?

In the 2011 crash, when Nifty had fallen 27% from its peak, the total return from 2010 was –11.27%. In contrast, Pharma was +22.47%, Healthcare was +18.45%, and FMCG was +43.42%.

Even during a deep crash, these sectors were still positive from the starting point.

In the 2015-16 correction, the difference widened further. By then, defensive sectors had already risen much more over the years. So even after a 20% market fall, they were sitting at much higher overall gains compared to the Nifty.

During the Covid crash in 2020, when Nifty fell 38%, the total return from 2010 was still +64.89%. However, Pharma was at +84.21%, Healthcare at +107.00%, and FMCG at +265.48%. Once again, the defensive sectors stood at higher overall return levels despite the panic.

The conclusion here was simple.

In major crashes, defensive sectors not only fall less during the event itself, they also tend to stand at higher overall return levels from the starting point.

For a long-term investor, that means the overall damage to wealth is smaller, and the recovery path begins from a stronger base compared to holding only the broader market.

Conclusion

Based on our study, it is clear that defensive sectors really do what they are supposed to do.

When markets fall hard, they usually fall less. That reduces damage. A 20% fall instead of 40% is not a small difference. It changes recovery time and investor behaviour. The data clearly supports that defensive sectors fall less during crashes.

But markets do not crash most of the time. They spend more time growing. And during strong growth phases, banks, financials, autos and other cyclical sectors often lead. That is where some of the strongest compounding happens. If you stay only on the defensive, your portfolio may feel steadier, but it can also lag when growth accelerates.

So the real learning is not “defensive sector are better.”

It is that they serve a role.

They reduce volatility. They reduce drawdown depth. But they are not designed to maximise upside.

Over long periods, diversification across both defensive and growth sectors has historically produced more balanced outcomes than concentrating entirely in one.

Protection matters. But so does participation.

Some companies feel like they’ve been around forever. So much so, that they almost feel almost too big to fail.

Companies like Reliance, Tata, and HDFC are not just market players. They have also played a major role in the growth of the Indian economy for decades. Even in the stock markets their long term growth has been strong, with CAGRs above 10%.

Equity markets are often volatile, no doubt.

But for an investor, it’s easy to see why money would gravitate toward these giants. They feel like a safer bet.

Think about it: they are large businesses, strong brands, and have a long track record. They are large cap stocks which also means that they won’t face as much volatility as small or mid cap stocks.

And yet, debt funds exist. Millions of investors still choose this predictable, slow lane.

This leads to a simple question:

When companies this big have stayed in the Nifty 50 for 20 years and have given such strong long-term returns, they should be considered safe, right?

So why do investors still put money into debt funds that have given only around 6% CAGR when equity from these same large companies has historically delivered more than 12% CAGR or more?

Why choose slow and predictable returns when the biggest companies seem both familiar and rewarding?

To understand this, we looked deeper.

The Experiment

Currently the top 5 companies in the Nifty 50 are:

Reliance Industries
HDFC Bank
Bharti Airtel
TCS
ICICI Bank

Predicting the future performance of these stocks is impossible. So, we looked at the historical returns using adjusted prices (accounting for bonuses, stock splits and dividends) of the current top 5 Nifty 50 companies over a 15-year period (January 2011 to December 2025).

To see if ‘big’ stocks mean ‘consistent’, we compared them to a debt fund: the ICICI Prudential Gilt Fund.

If an investor bought these stocks in 2011 and let the money grow for 15 years, the returns are significant. Over the long haul, the ‘giants’ did exactly what they were supposed to do — they built massive wealth, giving a steady return of 12-15% CAGR.

On a 15-year chart, equity crushed debt, by quite a significant margin.

But not all investors stay invested for 15 years.

Let us look at smaller timelines too

The picture looks a bit different here. Returns over smaller time horizons have varied massively.

• Reliance looks impressive at first glance, especially over the last year. But its returns over the 3-year and 5-year periods have been muted compared to its 15-year CAGR.

• Airtel is the outlier in this table, having given great, consistent returns over almost all these recent time periods.

• Both banks, HDFC and ICICI, have the lowest 1-year returns on the list, likely due to shifting interest rate cycles and banking sector headwinds. While ICICI has been a star over the 5 and 10-year marks, HDFC seems to have struggled to keep that same pace.

• Despite being a global IT giant, TCS delivered negative returns over 1 and 3 years and barely moved across 5 years. For an investor who put money into TCS in 2021, their wealth grew just 1.83% annually, less than a basic savings account.

On the other hand, for the debt fund, returns have stayed lower than equities but always steady around 6-9% over all time periods.

Equity Returns are never Smooth

Equity market returns don’t grow linearly.

If you look at the history of these current top Nifty 50 companies, every single one of them has gone through a ‘Dead Zone’, a period where the stock underperformed for years.

For our study, we defined this as the longest period where the stock’s CAGR stayed below 6%, roughly what a basic debt fund or fixed deposit would deliver.

Here’s what 15 years of data reveals for the current top Nifty 50 companies:

Reliance Industries

Between roughly 2009 and 2017, the company famously went through what analysts call a ‘Lost Decade.’ The stock stayed trapped in a horizontal range.

This period coincided with massive structural shifts and the incubation of ventures like Jio and Retail, where the company invested heavily.

For our analysis, we focused on data from 2011 onwards. From Jan 2011, to March 2017, Reliance spent 2,258 consecutive days in a Dead Zone, delivering a CAGR of just 3.22%.

These returns were lower than what a simple debt fund or an FD would have given annually.

But once the Dead Zone ended in 2017, Reliance’s returns exploded. The stock, which has stayed in a similar price range for such a long time, doubled in value in less than 2 years and has been growing significantly since then.

HDFC Bank

HDFC Bank has a long-standing reputation of being a safe stock. While not entirely wrong, this too faced a period of horizontal movement after the 2020 pandemic. From around Feb 2021 to mid-2024, a period of almost 1,230 days, the stock gave a mere 1.67% CAGR approximately.

This reflected the market digesting the massive merger with HDFC Ltd and concerns over loan-to-deposit ratios.

Bharti Airtel

From Jan 2011 to May 2019, over 3,000 days, Airtel delivered a CAGR of -0.15%, essentially hovering around the same price for a long time. There were ups and downs in between, but the CAGR remained low.

The reasons were a mix of regulatory fees, and the Jio-led price war and the then ongoing 2g scandal.

After years of stagnation around the same price (Rs 300), Airtel broke out and surged, growing massively in the last six years.

Tata Consultancy Services

From Oct 2014 to Jan 2018, around 1,197 days, TCS delivered a CAGR of just around 2.58%.

IT stocks in general have been facing a lot of volatility ever since the 2020 Covid 19 Pandemic as well as the global shift in the nature of IT services.

ICICI Bank

Between Jan 2011 and May 2017, a period of around 2,325 days ICICI Bank delivered a CAGR of just 5.44%.

This stagnation was largely driven by the Indian banking sector’s NPA crisis and RBI cleanups.

But after 2020, the stock broke out and has given good returns in recent years.

Remember, for the purpose of the study, we have taken into account the CAGR. This means, any occasional rise or fall in the stock price during these ‘dead zones’ have been smoothened over. We have tried to find out the longest period where the CAGR was below our threshold of 6%.

Focusing on these periods might seem like nitpicking, but that’s the point. Even the best stocks can deliver low returns for extended stretches. That doesn’t make the stock ‘bad’, but simply illustrates that equity growth is not linear.

Debt funds on the other hand, do not have this type of volatility. They do not give sky-high returns like a stock would, but they also don’t have periods of uncertainty.

Conclusion

Over the long term, equities clearly outperform debt. But the journey is rarely smooth.

With the giants of the Nifty 50, wealth is often created in short bursts following long stretches of slow growth. Those quiet periods test an investor’s patience and resolve. Even top-performing stocks can lag behind a simple debt fund or FD during these phases, and any urgent need to sell could lock in losses.

Debt funds provide stability and steady compounding, acting as a buffer during these flat phases. They preserve value.

Size can offer a safety net for the business, but not necessarily a shortcut for the investor. So while bigger companies can generate huge wealth, their size alone doesn’t guarantee short-term safety. For those who cannot stomach 2,000 days of low growth, debt funds prove to be a necessary anchor.

Limitations

• Survivorship Bias: This study looks at today’s top 5 Nifty 50 companies. Historical returns may seem higher than if we had analysed the top 5 from 2011.

• Adjusted Prices: Returns account for splits, bonuses, and dividends (Total Shareholder Return). Actual bank balance may vary if dividends weren’t reinvested.

• Tax & Inflation: Returns don’t consider taxes. Real returns would be lower after adjusting for inflation.

• Dead Zone: Defined here as CAGR <6%. Different investors may use other benchmarks.

• Past Performance: Past returns don’t guarantee future results; structural challenges vary across companies and time.

• Market Timing Risk: Returns shown assume the investor stayed invested continuously. Attempting to time the market could lead to very different outcomes.

Research Analyst Disclaimer