Let’s start with what happened in India’s F&O markets.

Over the last decade, retail traders got access to tools that didn’t exist before — real-time data, sophisticated charting platforms, options analytics, and backtesting engines so simple you don’t even need to know how to code. The number of people who could do sophisticated research exploded virtually overnight. Millions of new participants started trading in the F&O market, armed with better tools than most professional traders had just ten years ago.

And yet — over 90% of retail F&O traders still lose money.

Better tools didn’t change the outcome. They just changed the scale.

Now Everyone Is Saying AI Will Transform Investing

They’re not wrong. It will.

AI will make stock research faster and cheaper than ever before. An individual investor sitting at home will be able to analyse a balance sheet in seconds, run backtests in minutes, identify patterns across thousands of stocks simultaneously, and access the kind of research that was previously locked behind institutional walls or expensive subscriptions.

Retail investors will be able to cover more stocks. Their research capability will genuinely improve. On those points — absolutely agreed.

But here’s where the narrative goes wrong.

Better research capability does not automatically translate into better portfolio performance. But it might just translate to over-confidence in them.

The F&O Lesson, Applied to AI

When backtesting tools became free and easy, more people ran backtests. More people found “strategies.” More people traded with confidence.

And more people lost money.

Because the tools solved the information problem. They didn’t solve the behaviour problem.

AI will do the same thing at a larger scale. It will democratise research. It will lower the cost of analysis to near zero. And then — when the market falls 10% in a month, or rallies 20% in a quarter — investors will still panic, still chase, still freeze. The same emotions that have driven markets for centuries will drive them still.

What AI Cannot Fix

Markets have never really been about information. They’ve always been about reactions to information.

When everyone has the same data, when everyone runs the same AI-powered screening tools, when the analytical edge disappears — what’s left? Behaviour. Process. Discipline.

AI will speed up research. It will not remove the impatience you feel during a drawdown. It will not quiet the overconfidence that builds during a bull run. It will not stop you from checking your portfolio fourteen times on a bad day.

The investors who win in an AI-powered world will be the same kind of people who win today: those with a rules-based process they can follow without flinching. The ones who don’t need AI to tell them what to do, because they already know — and more importantly, they do it.

The Outcomes May Look Familiar

When the dust settles on this AI revolution in investing, here’s what I expect we’ll still see:

• The disciplined few will continue to outperform

• The majority will still chase performance and buy near tops

• Bear markets will still feel catastrophic to those without a process

• Only about 25% of fund managers will beat the index — because the other 75% will have the same AI tools, and still make the same behavioural errors

• Fear and greed will continue to cycle, just faster

Alpha doesn’t come from information anymore. In many ways, it never really did. Alpha comes from doing the boring, uncomfortable, countercultural thing when everyone around you is doing the opposite. AI doesn’t help with that. If anything, when everyone’s analysis converges, doing the right thing gets lonelier.

What This Means For You

The AI investing tools coming your way are genuinely useful. Use them. Better research is better than worse research.

But don’t mistake the ability to research more stocks for the ability to manage a portfolio better. The F&O traders who lost money weren’t losing because they lacked data. They were losing because they lacked process — and worse, they used the tools to justify trades they’d already emotionally committed to.

The most important question isn’t “should I use AI to research stocks?” It’s “should I be picking stocks at all?”

For most people, the honest answer is: probably not. Not because they’re not smart enough — but because the edge in investing has never been intelligence. It’s been temperament. And that’s not something any tool, however powerful, can give you.

- Vivek

P.S. If you’d like to discuss financial planning, retirement, early financial independence, asset allocation, or long-term investing, feel free to schedule a conversation using the calendar link below. These discussions are educational in nature and do not include product-specific investment recommendations.

https://calendar.app.google/AYsnryPRfT67q9Xf6

Almost every single year, the market experienced a meaningful correction.

Some were small:

• 5% to 10%

Some were painful:

• 15% to 20%

And a few were brutal:

• 2008 (-55%)

• 2020 (-35%)

Looking at this data, a very obvious question comes to mind:

If markets correct almost every year, why not simply wait for the dip before investing?

Why do a regular SIP every month?

Why invest when markets are near all-time highs?

Wouldn’t it be smarter to keep some cash aside and deploy it only when markets fall 5%, 10%, or even 15%?

It sounds logical. In fact, many investors try exactly that.

Instead of investing the full SIP amount every month, they wait for corrections, hoping to buy more units at lower prices.

So I decided to test this idea.

Using 20 years of market data, I compared a regular SIP against multiple “Buy the Dip” strategies to answer one simple question:

Can waiting for market corrections actually beat a plain old SIP?

The results were surprising.

The Logic Behind Waiting

Imagine you have ₹20,000 to invest every month.

A regular SIP investor puts the entire amount into the market every month.

A “Buy the Dip” investor does something different.

He invests only half.

The remaining money sits in cash waiting for an opportunity.

Whenever the market falls:

• 5% from its recent peak

• 10% from its recent peak

• or 15% from its recent peak

He deploys the accumulated cash. Sounds smart.

After all, buying at lower prices should lead to better returns. Right?

The Experiment

I tested four strategies using 20 years of market data.

Strategy 1: Regular SIP

Invest ₹20,000 every month.

No prediction. No timing. No adjustments.

Just discipline.

Strategy 2: Smart SIP – Buy at 5% Corrections

Invest ₹10,000 every month.

Keep ₹10,000 aside.

Deploy accumulated cash whenever markets fall 5%.

Strategy 3: Smart SIP – Buy at 10% Corrections

Same approach.

Cash gets deployed only after a 10% correction.

Strategy 4: Smart SIP – Buy at 15% Corrections

Wait for deeper falls before investing accumulated cash.

The Results

Final Portfolio Value for 20K SIP for 20 yrs

Not only did the Buy-the-Dip strategy fail to beat the regular SIP......the longer you waited for bigger corrections, the worse the results became.

The 15% correction strategy delivered the lowest wealth.

But Why?

This seems counterintuitive.

You are buying at lower prices.

Shouldn’t that help?

The problem is that while you wait for the dip, your money sits idle.

And idle money doesn’t compound.

Markets rise more often than they fall.

A typical pattern might look like this:

+5%, +5%, +5%, +5%, Then a correction of 5%.

The investor waiting for the dip feels smart.

But he forgets that the market first moved up more than 20% before falling 5%.

The correction happened from a much higher level.

Meanwhile, the regular SIP investor was participating in the entire journey.

The hidden cost of waiting is often larger than the benefit of buying slightly cheaper.

A More Important Test

One criticism of any backtest is that it depends heavily on the starting date.

Maybe this result occurred because of the specific 20-year period chosen.

So I ran a second test.

Instead of looking at one 20-year period, I broke the data into multiple rolling 10-year periods.

For example:

• 2006–2015

• 2007–2016

• 2008–2017

• and so on

This creates multiple independent observations.

The question becomes:

How often does Buy-the-Dip actually beat a regular SIP?

The answer surprised me.

Not once.

Across all the rolling 10-year periods tested, the regular SIP beat every version of the Buy-the-Dip strategy.

Not once did the “smart” SIP prove smarter.

So Should You Ever Buy The Dip?

Yes.

But probably not in the way most investors think about it.

Many investors stop or reduce their investments while waiting for a market correction. The assumption is simple: if markets are likely to fall at some point, why invest today?

Our analysis suggests that’s usually a mistake.

The problem isn’t buying during a dip.

The problem is waiting for a dip while keeping money on the sidelines.

A much better approach is to continue your regular SIP and, if you have surplus cash available, invest additional amounts when markets experience meaningful corrections.

That makes intuitive sense.

When markets fall, every rupee buys more units. If you have cash available during these periods, you can take advantage of lower prices without sacrificing the benefits of staying invested throughout the journey.

The distinction is subtle but important:

❌ Wait for the dip before investing

✅ Stay invested and invest more during the dip

One approach relies on market timing.

The other relies on discipline and opportunism.

And over the long run, the second approach tends to work far better.

The Takeaway

The market rewards participation more than prediction.

The biggest mistake investors make is assuming that corrections are easy to identify in advance.

They aren’t.

Most corrections occur after markets have already risen significantly.

And while you wait for them, compounding quietly slips away.

The data suggests a simple rule:

Don’t wait for the dip.

Invest through the dip.

And if you have extra cash available when markets fall, deploy more.

Because the long-term winner isn’t the investor with perfect timing.

It’s the investor who remains consistently invested while everyone else is waiting.

You probably don’t remember it, because you were too young. But it happened early. Your marks in school. Someone else’s marks in school. A report card held up next to another report card, as if that’s what you were - a number on a page, measured against another number on a page.

And for a while, you played along because what else do you do when you’re eight?

Then you grew up a little, and the metric shifted.

It wasn’t marks anymore. It was which college you got into. Which stream. Which city. The comparison became more sophisticated, more socially acceptable to pretend it wasn’t happening, but it was the same game. You were still being measured against someone else’s life as the reference point.

And then you graduated, got a job, started earning. And now the metric was salary. CTC. Designation. How many years to the first promotion.

At no point did anyone stop and ask: compared to what you actually want, how are you doing?

It was always: compared to someone else, how are you doing?

We’ve been trained, since childhood, to evaluate ourselves using external, relative metrics. Not internal ones. Not fixed ones. Relative ones. Ones that depend entirely on who happens to be standing next to you.

So it shouldn’t surprise anyone that this follows us into investing.

When you start, the questions are grounded. Where does this money go? What am I trying to do with it? A home. A child’s education. Some version of financial independence that doesn’t require you to keep working until you physically can’t.

But at some point, gradually, without announcement, the questions change shape.

How am I doing compared to everyone else?

Old habit but a different arena.

Charlie Munger had this observation about envy that I keep coming back to. He said it’s the only one of the seven deadly sins that people don’t even enjoy while committing it. Greed at least feels good in the moment. Envy just hums in the background, this constant low-grade feeling of not enough, even when everything is technically fine.

And it shows up not as envy, usually. It shows up as restlessness. As the sudden urge to change a strategy that was working. As risk you didn’t need to take. As decisions that have nothing to do with your goals and everything to do with not wanting to feel left behind.

Here’s a thought experiment that makes this uncomfortably real.

Situation A: You’re exactly on track...... the home, the kids’ education, the retirement you’d actually enjoy. But the people around you seem to be doing better.

Situation B: You’re slightly behind some benchmark. But you’re doing better than most people you know.

Be honest about which one feels better.

A lot of people, more than would admit it, feel more at peace in the second situation. Not because it’s objectively better. Just because it looks better relative to others.

That’s what decades of relative comparison does to you. It rewires what “doing well” even means.

The problem with relative benchmarks is they never hold still.

There’s always someone doing better. Always a higher number. Someone whose bet worked out in a way yours didn’t. So you’re chasing a finish line that keeps moving — which is exhausting, and also pointless, because the finish line was never really yours to begin with.

The boring alternative, and I say boring because it doesn’t make for exciting dinner conversation, is to measure yourself against your own past.

Am I closer to what I set out to do than I was a year ago?

That’s it. Not compared to your neighbour. Not compared to someone from college who apparently made a killing in some stock. Not compared to a benchmark that has no relationship to your actual life, income, responsibilities, or timeline. Just: are you moving in the direction you intended?

We’ve spent so long using other people as the yardstick that this feels almost disorienting at first. Because the marks comparison, the college comparison, the salary comparison — none of that ever asked you what you actually wanted. It just asked how you ranked.

This finally asks something different.

One small, practical thing that helps: separating money by purpose.

Portfolios that try to do everything at once, short-term needs, medium-term plans, long-term goals, all sitting together — tend to create unnecessary anxiety. When money you need in two years behaves like money you won’t touch for twenty, every market dip feels like a crisis. You start reacting. Which is usually expensive.

Separating things by purpose, this is for X, that is for Y, quietly removes a lot of the noise. You stop reacting to everything because not everything is relevant to everything. The clarity isn’t just financial. It’s psychological.

A portfolio that does exactly what it was supposed to do, even if nobody at the party is impressed, is a successful portfolio.

A life that’s on track for what you actually care about, even if it doesn’t rank well against whoever happens to be the reference point this week, is a good life.

We’ve been measured against other people since we were children. In school, in college, in careers. It’s a deeply grooved habit.

But the metric was always theirs. Not yours.

The only yardstick worth building, probably for the first time, is your own.

After all, every year we see lists of “top performing funds” across categories. Funds that delivered the highest returns over the last one year, three years, or five years naturally attract attention. And somewhere in the back of our minds, there is a very intuitive assumption:

If a fund has done well recently, maybe it will continue doing well going forward.

I wanted to test this idea in a structured way.

So I ran a simple backtest across three categories of equity mutual funds — large cap, mid cap, and small cap funds.

The idea was straightforward.

What happens if we simply invest in the best performing mutual funds from the recent past? Can a strategy built around “past winners” consistently outperform the market indices?

To test this, I created two different approaches.

In the first approach, I selected the top three performing funds based on their returns over the previous one year. Divided the available capital in 3 equal parts and invested in the 3 selected funds. These funds were then held for the next one year.

For example, investments made in 2018 were based on the best performing funds of 2017. At the end of 2018, the portfolio was refreshed again using the top performers of 2018 for investment during 2019, and so on.

So both the selection period and holding period were one year.

In the second approach, I took a longer-term view.

Instead of looking at just one year of past returns, I selected funds based on their previous three-year performance and then held them for the next three years.

Here again, both the selection period and holding period were aligned at three years.

The backtest started with an initial capital of ₹25 lakh.

To make the comparison realistic, I also accounted for taxes during portfolio changes. In the mutual fund strategies, long-term capital gains tax was applied every time the portfolio was rebalanced. For the benchmark indices, taxes were assumed to be paid only at the end of 2025.

I used the current LTCG tax structure of 12.5%, with the ₹1.25 lakh annual exemption considered wherever applicable.

The objective of this exercise was not to discover a perfect investing strategy.

It was to examine a very common investor behaviour:

If we simply keep investing in yesterday’s winning mutual funds, do we actually end up beating the market over time?

I tested both the approaches for Large, Mid and Small cap funds

Let’s first look at which large cap funds emerged as the top performers each year, and how a portfolio built using those funds would have performed.

Large Cap Funds

Let’s first look at which large cap funds emerged as the top performers each year, and how a portfolio built using those funds would have performed.

Now let’s compare the yearly returns of this strategy against the Nifty 100 Index.

Starting with ₹25 lakh:The strategy outperformed the benchmark in 6 out of 8 years and generated an average excess return of 2.9% annually.

However, one thing stood out clearly.

A large part of the outperformance came from a single year — 2023.

I also checked whether top-performing funds continued to remain among the better-performing funds in the following year.

The Persistence rate I got was 50%. Which means there are 50% chances of a top quartile fund to remain top quartile next year as well.

If performance persistence were completely random, the expected number would have been closer to 25%.

So past winners did show some persistence, though the sample size is still small.

3-Year Rebalancing Approach

Now let’s look at what happened when the portfolio was rebalanced every 3 years instead of every year.

And here is the comparison against the Nifty 100:

The 3-year approach performed better for two likely reasons:

• Lower tax drag due to less frequent rebalancing

• Strong-performing funds often remained strong for multiple years

Key Takeaway

The results suggest that past winning large cap funds do show some degree of persistence.

But the outperformance was not smooth, and a meaningful part of the excess return came from a handful of strong years.

Past performance may contain information. But it is still far from a guarantee.

Mid Cap Funds

The results for mid cap funds were far less clear-cut.

Unlike large caps, where the yearly rebalancing approach showed a noticeable edge, mid cap results were much closer to the benchmark.

The yearly rebalancing strategy marginally outperformed the index, while the 3-year approach slightly underperformed.

1-Year Rebalancing: Year-wise Comparison

3-Year Rebalancing: Year-wise Comparison

One interesting difference emerged here.

In large caps, the longer holding-period approach performed better. In mid caps, that advantage disappeared.

This suggests that leadership in mid cap funds may be less stable and more cyclical. Holding past winners for too long can sometimes leave investors stuck with yesterday’s outperformers while market leadership shifts elsewhere.

The broader takeaway:

Past winning mid cap funds did not show the same level of persistence seen in large caps. And the timing of rebalancing mattered much more.

Small Cap Funds

Small cap funds produced the strongest overall returns in the study.

Both approaches outperformed the benchmark index over the full period, though the path was far more volatile than large caps or mid caps.

The yearly rebalancing approach delivered the highest final value and the best CAGR among all categories tested.

1-Year Rebalancing: Year-wise Comparison

The strategy beat the index in 6 out of 8 years.

3-Year Rebalancing Approach

The longer holding-period approach also outperformed the benchmark, though the advantage was smaller.

What the table reveals quickly

Final closing remarks

So if someone really wants to experiment with a momentum-style approach, the results here suggest that large-cap funds might be the only place where it shows some consistency. But when we look at mid caps and small caps, the strategy just doesn’t generate the kind of reliable alpha I would be looking for.

And there’s another important caveat here — the time period we analysed is still relatively limited. Markets go through different cycles, and results over a longer timeframe could look very different.

So overall, while this analysis was interesting from a research perspective, it still doesn’t give me enough confidence to actually try this strategy in my own portfolio.

Person A grows their income at 5% a year. Person B grows theirs at 15%. Both save 15% of their income and spend the rest.

Who reaches financial independence first?

If you said Person B — you’re wrong. And so was I, until I actually ran the numbers.

The answer that breaks your brain

In many scenarios, Person A gets there faster.

Not because they earn more. Not because they invest better. But because their lifestyle grows slower - their expenses are not growing as much.

That one insight flips most people’s entire understanding of FIRE.

Why higher income can actually delay retirement

Most of us think FIRE is an investment problem. Get better returns, retire faster.

It’s not. It’s a lifestyle problem.

Here’s the math that trips people up:

Your FIRE number = Annual Expenses × 25

(This comes from the famous 4% rule, in which we divide Annual Expenses by 4% to get the 25 X multiplier. Whether 25X is sufficient for a person retiring early, say at 45, is a topic for another day)

That means your target isn’t fixed. It moves with your spending. If your income grows and your savings rate stays the same, your expenses grow too, and suddenly your finish line is sprinting away from you.

You’re not falling behind because you’re not earning enough. You’re falling behind because the goalpost keeps shifting.

Savings rate is the real lever

Before anything else, look at this:

If you’d like to explore the calculations yourself and test different assumptions around savings rates, income growth, and lifestyle inflation, I’ve attached the Excel calculator at the end of the article. Download it, make your own copy, and play around with the numbers — the insights can be quite surprising once you see how small behavioural changes impact the journey to financial independence.

The data below is the same one, represented in a tabular format

Assumptions : for a 40 yr old, generating 12% returns from his portfolio, starting corpus of 50 lacs. The excel file is attached at the end of newsletter.

If your savings rate is 30% you reach your FIRE number in 35 yrs vs 6 yrs if your savings rate is 75%.

That’s a 29-year difference from the savings rate alone.

Not returns. Not income. How much you keep.

The income growth trap

Now here’s the part that genuinely surprised me.

Take someone with a fixed 30% savings rate. Watch what happens as their income grows faster:

More income growth. More years to retirement.

Why? Because a constant savings rate means expenses scale with income. And since FIRE is anchored to expenses, a bigger salary just means a bigger target — one that’s harder to hit.

This is why you meet people earning extraordinarily well who never feel financially free. The portfolio grows but the lifestyle grows just as fast. Sometimes faster.

The fix is simple — but not easy

The goal isn’t to earn less. It’s to let your savings grow faster than your lifestyle.

And there’s one rule that does exactly that.

The 50/50 Rule

Every time your income goes up — split the extra right down the middle.

50% improves your life today. 50% builds your freedom tomorrow.

You don’t need extreme frugality or complicated spreadsheets to accelerate your journey to financial independence. In many cases, the single most important decision is what you do with every salary increment, whether it fully upgrades your lifestyle, or whether a meaningful part of it quietly goes toward building future freedom.

Think about what that actually means in practice. You still get to enjoy your raise. You still upgrade your life. You just don’t hand all of it over to lifestyle inflation.

And the math that follows is hard to ignore.

Same person. Same 30% starting savings rate. Same income growth.

• Keep a fixed savings rate → FIRE in ~40 years

• Apply the 50/50 rule to every raise → FIRE in ~25 years

Fifteen years earlier.

Not from chasing better returns. Not from a lucky windfall. Not from cutting out your morning coffee.

Just from one simple rule about where your next raise goes.

The real equation

Every ₹50K per month of permanent lifestyle spending you add requires roughly ₹1.25 crore more in your retirement corpus. Most people never think about a new subscription, a bigger flat, or a restaurant habit in those terms.

Financial Independence isn’t about being a miser. It’s about being deliberate.

Spend on what genuinely improves your life. Just don’t let every raise automatically turn into a permanent upgrade you’ll need to fund for the next 30 years.

The gap between how fast your income grows and how fast your lifestyle grows, that gap is where wealth actually gets built.

https://docs.google.com/spreadsheets/d/109In71iUl6az3MOjG4NUR_o6toInHwTHISdGTESi1qU/edit?usp=sharing

This is for educational purposes only and should not be taken as investment advice. Please consult a financial advisor before making investment decisions.

Almost all the money is in cricket.

You can be the 100th best cricketer in the country and still earn more
than the number one player in archery, table tennis, or wrestling.

That doesn’t say anything about skill.
It says everything about where the attention and money are.

The game you choose dominates the outcome.

Investing works the same way.

We spend an enormous amount of time debating:

• which stock

• which fund

• which manager

Very little time is spent asking a far more important question:

Where should my money be allocated in the first place?

For decades, research has shown that asset allocation is the biggest driver of how portfolios behave, but the often-quoted “90% of returns come from asset allocation” is a simplification. The classic studies by Gary P. Brinson and later by Roger G. Ibbotson actually found that over 90% of the variation in portfolio returns over time is explained by asset allocation—not that 90% of the returns themselves come from it.

In simple terms, your mix of equity, debt, and other assets largely determines how your portfolio moves through market cycles and the range of outcomes you experience, while fund selection and timing play a more secondary role in shaping the final result.

Part of the reason is simple and uncomfortable.

It is very hard to know which asset class will do well next.

Equity, debt, gold, domestic markets, global markets, each takes turns outperforming and disappointing.

Anyone who claims certainty here is either guessing or selling confidence.

Asset allocation is not about prediction rather it’s about accepting uncertainty and diversifying your bets.

Let’s take a simple example.

In one recent year, emerging market equities delivered returns in the range of ~25%,
while Indian equities delivered closer to ~8%.

Now imagine two investors.

The first is a very good fund picker.
He focuses only on Indian markets and manages to generate 5% alpha.

So he earns around 13%.

The second investor thinks differently.
He allocates part of his money to emerging markets as well.
He doesn’t pick brilliantly—maybe he just uses an index fund.

Even with no alpha, his overall outcome can be better.

Not because he’s smarter.
But because he chose to play more than one game.

This is what asset allocation really does.

It smoothens portfolio volatility.
And that matters far more than most people realise.

Lower volatility doesn’t just feel better.
It makes it easier to stay invested.

And staying invested is where long-term returns actually come from.

One practical lens I personally use is crowd behaviour, and it works beautifully over the long run. In every asset class.

When everyone is running towards something, returns are usually already priced in.

When everyone is running away from something, future returns tend to improve.

But this needs nuance. Sometimes people run away for the right reasons.
A company with governance issues can go to zero—and deserves to.

That’s very different from investors running away from an entire asset class.

If equities, or any other asset class for that matter, haven’t delivered returns for 3 or 5 years and everyone is disinterested, bored, or frustrated, that’s usually when future returns quietly improve.

One another framework that I follow is that of ICICI’s equity valuation model. The ICICI Equity Valuation Model is a rule-based framework used to adjust equity exposure based on market valuations. It looks at indicators like P/E, P/B, and earnings yield versus bond yields to judge whether equities are expensive or cheap.

When valuations are high, it reduces equity and shifts towards debt; when valuations are attractive, it increases equity. The goal is not short-term timing, but disciplined allocation across market cycles.

The index has moved from Neutral zone to “Invest in Equities” (light green zone) after the recent correction. ICICI Pru Balanced Advantage Fund increased equity to ~62% (5-year high)

These kind of bets take patience because they can underperform longer than you expect. This is where retail investors have an edge. A fund manager doesn’t have the liberty to be that much flexible. A retail investor does.

At least you know one thing:

The Nifty 50 is not going to zero. And if it does… well, in that scenario, the index will be the least of your concerns 🙂

If I’m honest, I ignored asset allocation for years.

My thinking was always about: Which stock should I buy?
Which fund looks better?

Asset allocation felt abstract. Almost boring.

Only later did I realise that fund selection decides margins, but asset allocation decides direction.

You can be excellent at picking funds in the wrong bucket and still end up disappointed.

Asset allocation doesn’t guarantee success but it quietly improves the odds, and your behaviour, over time.

A few months ago, he told me he had invested in it and had managed to double his money in about a year. It sounded like a great outcome, but then he added something almost casually. The amount he had invested was very small. In that moment, the entire story changed. The return was impressive, but the impact on his overall portfolio was negligible.

This is something most of us have experienced at some point. We come across a good idea, act on it in a limited way, and even if it works out well, it doesn’t really move the needle.

Over the years, I’ve found myself drifting into this pattern more than once, and that pushed me to develop two simple rules of my own. They’ve helped me stay focused and, more importantly, keep my portfolio simple and manageable.

The two rules I follow are simple:

• I invest only when I’m willing to allocate at least 5% of my portfolio to the idea.

• I invest only when I have the conviction to hold it for at least three years.

These are not sophisticated rules, but they act as strong filters. They force me to be honest about my conviction and naturally lead to a portfolio with fewer positions, ones I can actually track, understand, and stick with over time.

There is a deeper idea behind this. When we say an investment looks interesting, it is often a low-bar statement. Almost everything looks interesting in some way.

But saying that you are willing to allocate meaningful capital is a very different level of commitment. That shift in thinking naturally reduces the number of ideas you act on, and that is where clarity begins to emerge.

This way of thinking connects closely with something Howard Marks once reflected on. In a conversation with his son, he realized that across several decades of investing, the number of truly high-conviction calls he had made was perhaps four or five.

These were not frequent, recurring opportunities, but rare moments when the odds were clearly in his favor and required decisive action. It is an uncomfortable realization, because most investors behave as if such opportunities present themselves regularly.

Charlie Munger has expressed a similar idea in his own way. As he put it, “The wise ones bet heavily when the world offers them that opportunity.” The implication is simple but difficult to implement. The problem is rarely finding ideas. It is having the conviction to act decisively when it truly matters.

Even large institutions reflect this philosophy. Berkshire Hathaway has, at various points, held well over a hundred billion dollars in cash, not out of indecision, but out of discipline. That kind of patience isn’t passive. It’s deliberate. It comes from the recognition that truly great opportunities are rare.

And when they do appear, they deserve to be backed meaningfully. Until then, the willingness to wait, to do nothing, is what preserves the ability to bet big when it actually matters.

As individual investors, we actually have a structural advantage here. We are not required to stay fully invested at all times. We are not judged on short-term performance every quarter.

We do not have to explain periods of underperformance to clients or investors. This freedom allows us to wait, to be selective, and to act only when conviction is high.

However, having this freedom does not automatically mean we use it well. In practice, the constant flow of information, opinions, and new ideas makes it very difficult to sit still. Over time, portfolios tend to get filled with small positions, each one justified at the time of investment, but collectively creating clutter and dilution of focus.

The real edge in investing does not come from constantly finding new ideas. It comes from being able to ignore most of them, act decisively on a few, and then hold on with patience.

This is harder than it sounds, especially in an environment where there is always something happening and always something to react to.

That is why the simple rules of allocating meaningfully and committing for the long term are useful. They act as guardrails against overactivity and help ensure that when you do invest, it is with intent and conviction.

Over time, this approach leads to a portfolio that is not only simpler but also more aligned with your thinking.

In the end, outcomes are shaped not by how many ideas you act on, but by how you behave around a few decisions that truly matter.

Three categories have seen particularly strong interest:

• Aggressive Hybrid Funds

• Multi Asset Allocation Funds

• Dynamic Asset Allocation Funds

But here’s the problem:
Most investors don’t fully understand how these funds differ - especially in terms of risk, return behavior, and role in a portfolio.

Let’s break this down clearly.

1. Aggressive Hybrid Funds

Aggressive hybrid funds are equity-heavy funds that typically maintain 65–80% allocation to equities, with the rest in debt. Their goal is to provide growth with some stability, but the returns are still largely driven by equity markets.

• Equity taxation, since they maintain over 65% equity exposure

• Volatility remains moderately high and market-linked

• Asset allocation is mostly fixed, with limited active shifts

They are generally suited for first-time investors or those with a moderate risk appetite, and are often used as part of a core portfolio.

👉 In simple terms:
Equity funds with a small cushion — but without any active downside protection.

2. Multi Asset Allocation Funds

Multi asset allocation funds invest across equity, debt, and gold, with each asset class having a minimum allocation of 10%. The idea is to build a diversified portfolio within a single fund, reducing dependence on any one asset class.

• Gold acts as a natural hedge during market stress

• Allocation tends to be relatively stable, not aggressively timed

• Performance is driven by diversification rather than market calls

They are well suited for long-term portfolios, especially for investors who prefer a “set it and forget it” approach and don’t want to actively rebalance across asset classes.

👉 In simple terms:
A diversification play — designed to reduce shocks, not chase returns.

3. Dynamic Asset Allocation Funds

Also known as Balanced Advantage Funds

Dynamic asset allocation funds actively shift between equity and debt based on valuations or quantitative models. The goal is not to maximise returns, but to manage risk by adjusting exposure as markets become expensive or attractive.

• Equity allocation can vary widely (20%–80%+) depending on market conditions

• Designed to deliver a smoother return experience across cycles

• Allocation changes are rule-based or model-driven

They are well suited for conservative investors, lumpsum deployment, and those who are sensitive to market volatility.

👉 In simple terms:
Risk-managed funds — focused on protecting on the downside rather than chasing upside.

Summary of the 3 fund categories

Now lets take a look at how these 3 categories have performed in last 10 years.

Key Insights

Multi Asset is the clear winner (risk-adjusted)

👉 Highest risk-adjusted return = 1.86%

• Same return as Agg Hybrid (~12%)

• But ~40% lower volatility

Interpretation:

You are getting equity-like returns with debt-like volatility

BAF and Multi Asset have the lowest volatility

Both categories deliver a much smoother return experience compared to Agg Hybrid and pure equity

Multi Asset, however, stands out further

👉 Similar volatility as BAF
👉 But meaningfully higher returns

Interpretation:

You are getting BAF-like stability with better return potential

Aggressive Hybrid = inefficient risk-return tradeoff

• Return: ~12% (same as Multi Asset)

• Risk: 10.26 (very high)

• Risk-adjusted: worst (1.18%)

Interpretation:

You are taking equity-level risk without equity-level returns

Large Cap = highest return, but inefficient

• Return: 14% (highest)

• Risk: 11 (highest)

• Risk-adjusted: 1.27% (mediocre)

Interpretation:

High return comes with high volatility → not optimal for many clients

Identifying the Top Funds — A Structured Approach

To move beyond category-level insights, the next step was to identify which funds within these categories actually stand out.

Most investors pick funds based on past returns.

The problem is that returns alone don’t tell you how those returns were generated.

For this, I built a simple but robust 3-factor framework, focusing on what truly matters:

1️⃣ Alpha Strength

Does the fund generate meaningful excess returns over its benchmark?

We look at a combination of:

• Average 3-year rolling alpha (captures recent and cycle performance)

• Full-period alpha (captures long-term capability)

This avoids over-relying on short-term spikes, which can often be misleading.

2️⃣ Consistency

How often does the fund outperform across cycles?

We measure this using rolling 3-year periods and check how frequently the fund delivers positive alpha. A higher score indicates more reliable performance.

Consistency, however, does not capture magnitude — a fund can beat the benchmark often but by small margins. That’s why it needs to be evaluated alongside Alpha Strength.

3️⃣ Risk Efficiency

Is the return worth the risk taken?

We use a simple measure:

• Risk Efficiency = CAGR / Standard Deviation

This helps identify funds that generate returns more efficiently, without excessive volatility. While it uses volatility (not drawdown), it still provides a strong directional view of how disciplined the fund is in managing risk.

How the Selection Works

• Funds are ranked within their category on each of the three factors

• A weighted average of these ranks is used to arrive at a consolidated score:

  (0.45 × Alpha Rank) + (0.30 × Consistency Rank) + (0.25 × Risk Efficiency Rank)

• Finally, to account for reliability:

  • Funds with shorter track records are penalized using a multiplier

  • This ensures newer funds are not ignored, but are assigned lower confidence

  Universe Selection

  To maintain a reasonable level of robustness:

  • Only funds with a minimum 5-year track record were considered

  • Funds with longer histories are naturally favored through the track record adjustment

  Why this approach

  This framework moves beyond just picking the highest return funds. It focuses on funds that combine performance, consistency, and disciplined risk-taking — traits that are more likely to sustain over time.

  These are not strict rankings. The idea is to identify a shortlist of good funds, not a single winner. The top 5 in each category are all strong options, and the final choice should depend on your specific needs.

  

Disclaimer:
This analysis is meant purely for educational purposes and does not constitute investment advice or a recommendation to buy or sell any fund. Please consult your financial advisor before making any investment decisions.

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A few months ago, I came across something I haven’t been able to forget.

A quarter of mutual fund equity investors in India have an average holding period of six months or less.

Six months — for money that is meant to grow over decades.

We have better platforms now. More data. Lower costs. Almost zero friction.

And yet, we’re holding investments for less time than ever before.

At first, I didn’t understand this.

Then I thought about how investing actually works today.

You’re in the middle of something. A thought crosses your mind. You open an app. A few taps later, you’ve bought a fund — or sold one. It takes less time than making a cup of tea.

The ease that was supposed to make us better investors has quietly made us more reactive ones.

Who I am (and why this matters to me)

I’ve spent over two decades in markets — but not in a straight line.

I’m a graduate of IIT Kharagpur, where I studied Electrical Engineering. I started out as an engineer, working on Oracle’s ERP suite. It was a great job — the kind you’re supposed to keep.

But I left within a year because I couldn’t see myself doing it for life.

Around that time, markets were booming. Everything I bought went up. I thought I understood investing.

I didn’t. I was just lucky.

That false confidence pushed me into trading — where I spent the next decade.

I started as a crude oil trader, eventually leading an energy trading desk, working alongside some of the best trading talent in the country. It was intense, fast, and deeply engaging.

Trading teaches you sharp lessons:

• Risk matters more than reward

• Discipline matters more than intelligence

• Behaviour matters more than both

But here’s the irony.

While I was a good trader, I was a poor investor.

I traded actively. I earned well. But I didn’t invest seriously. Somewhere along the way, I had convinced myself that financial planning was only for people who needed it.

That belief changed when I started working directly with investors.

Over the years, I’ve interacted with thousands of people and been part of managing portfolios for over 25,000 investors — across individuals, HNIs, and institutions.

And that’s where things became clear.

Most investing doesn’t fail because people choose the wrong fund.

It fails because behaviour breaks — slowly, quietly, repeatedly.

Why this newsletter

I’m starting this newsletter because I want a space where we slow down.

Not to talk about what the market did this week — there’s no shortage of that.

But to think about what actually matters over the long run:

• Goals

• Behaviour

• Discipline

• And the connection between money and the life you’re trying to build

Because the real challenge in investing isn’t what to do.

It’s sticking to it.

What to expect

I’ll be writing twice a month — on the 2nd and 4th Saturday.

Each piece will be:

• Short enough to read in one sitting

• Practical, not theoretical

• Focused on how people actually behave with money

No market predictions.
No “top funds to buy now.”
No noise.

Just ideas that help you make fewer mistakes — and stay invested long enough for things to work.

Why I’m writing this (honestly)

I’m also writing this for myself.

Writing forces clarity.

It makes you question what you believe, and why you believe it. Some of the clearest thinking I’ve done hasn’t come from reading more — but from trying to explain something simply.

If this becomes a space where I can think better, that’s already valuable.

Why you might want to subscribe

If you’ve ever:

• Checked your portfolio more often than you’d like

• Felt the urge to act, even when you know you shouldn’t

• Wondered if you’re doing the “right” thing with your money

This might help.

Not by giving you answers.

But by helping you ask better questions — and stay calmer while doing it.

If these letters help you interfere a little less with your long-term plan, they’ve done their job.

And if they help me become a slightly calmer, clearer thinker along the way — that’s reason enough to keep going.

Good to have you here.

— Vivek

If this resonated, forward it to someone who might need it.
No better way to start a conversation about money than a quiet one.

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