The variable no formula includes

So far in this series, every risk has been about the market or the instrument — leverage, correlation, sequence, the arithmetic of losses. This one is about the other party to every transaction you’ll ever make: you.

Behavioural finance is the study of the systematic ways people depart from the cool calculation the formulas assume. “Systematic” is the important word. These aren’t random errors that average out. They’re predictable biases that push most people in the same wrong direction at the same moment — which is exactly why they’re expensive.

Five of them, each attached to a number from earlier in this blog. The point of the numbers is recognition: it’s much easier to catch a bias when you can name the situation it lives in.

1. Loss aversion

What it is. Losses hurt roughly twice as much as equivalent gains feel good — the finding behind Kahneman and Tversky’s prospect theory (1979); their 1992 follow-up put the ratio at about 2.25. A ₹10,000 loss and a ₹10,000 gain are not emotional opposites; the loss is heavier.

Where it lives. In the underwater chart. Over twenty years and 4,915 trading days, UTI Nifty 50 Index Fund spent only 10% of trading days at or near an all-time high. It sat more than 10% below a previous peak on 33.7% of days, and more than 20% below on 13.6%. The median day was 5.9% below the high.

Read that again: a good, boring, diversified index fund spends most of its life below a number it once reached. Owning it means looking at a shortfall against the peak far more often than not. If each of those glances weighs twice what the gains do, holding through twenty years is emotionally punishing even when it’s financially fine — and the drawdown post showed that “financially fine” included a six-year wait after 2008.

The tell. Checking the portfolio more often when it’s falling. Loss aversion makes each check hurt; frequent checking makes the hurt frequent.

2. Recency

What it is. Weighting what just happened far more than what usually happens. The last year feels like the truth about the asset; the last twenty feel like history.

Where it lives. In the rolling returns. Three-year returns on the same fund ranged from −4.82% to 31.95% a year depending on start date; the median barely moved around 11.82%. Someone who started in March 2020 saw three years near the top of that range and quite reasonably concluded equities return 30% a year. Someone who started in 2017 saw the bottom of it and concluded the opposite. Both were extrapolating three years into a forecast. Both were wrong in the same way.

The most expensive version is the money that arrives after a great run and leaves after a bad one — retail flows into equity funds have tended to be strongest after strong markets and weakest after falls. Recency converts the market’s past into the investor’s future at exactly the wrong moments.

The tell. “This time it’s different” and “it’s been going up for years” are both recency. So is judging a fund on its one-year return.

3. Anchoring to your buy price

What it is. Treating the price you paid as the reference point for every future decision, when the market has no idea what you paid.

Where it lives. In the phrase “I’ll sell when it gets back to my price.” The arithmetic of losses showed why the anchor is so sticky: a stock down 40% needs +67% to reach it. Waiting for that isn’t a strategy, it’s a hope with a number attached — and while you wait, the question you should be asking (“would I buy this today at this price?”) goes unasked.

The anchor also works in reverse. A stock that has doubled feels “expensive” relative to your cost, so it gets sold, even if nothing about the business says so. Your purchase price is a fact about your history. It is not information about the asset.

The tell. Any sentence that includes “my price,” “back to even,” or “I’m up X% so I’ll book it.”

4. The disposition effect

What it is. Selling winners too early and holding losers too long. It’s loss aversion and anchoring combined: booking a gain feels good and makes the gain “real,” while selling a loser makes the loss “real,” so people do the first and avoid the second.

Where it lives. In the F&O data. The F&O post noted that among individual traders who lost money two years running, about 90% lost again the following year. On its own that can’t separate skill from luck, and much of it is structural — costs, counterparties, leverage. But SEBI’s behaviour study (August 2026) adds a telling detail: among traders who had both profitable and losing quarters, about 79% lost more in an average losing quarter than they made in an average winning one. That pattern — small wins, large losses — is consistent with the disposition effect: wins taken quickly, losses held in the hope of recovery. You can be right more often than wrong and still lose.

For long-term investors the cost is quieter but real: a portfolio pruned of its winners and stocked with its losers, by design.

The tell. Your realised gains are many and small; your unrealised losses are few and large.

5. Herding

What it is. Doing what everyone else is doing because everyone else is doing it. Evolutionarily sensible — the herd is usually right about where the water is. Financially expensive — the herd is, by construction, the crowd that has already bought.

Where it lives. In participation numbers. The F&O studies describe the number of individual traders in the derivatives segment rising sharply through a bull market and falling sharply after — new entrants arriving when the activity was most visible, most discussed and most recently profitable, and leaving after the losses. (The FY26 fall also followed SEBI’s derivatives curbs, phased in from November 2024 — larger minimum contract sizes, weekly expiries limited to one index per exchange — so not all of it is the herd changing its mind; SEBI’s own study says it can’t establish cause.) The same pattern shows up in every IPO boom, every small-cap rally, every “everyone’s buying” moment.

Herding is also what makes correlations rise in a crash — the last post but one noted that risky assets fall together precisely when you need them not to. That’s not a property of the assets. It’s everyone selling at once.

The tell. You can’t remember your own reason for the position, but you can remember who else has it.

🧒 Explain it like I'm 10 (optional — skip if this is already clear)

Imagine you and your friends trading cricket cards.

  • You hate losing a card twice as much as you like getting one, so you hold onto bad cards forever. That’s loss aversion.
  • Whichever player scored a century last week, everyone wants his card this week. That’s recency.
  • You won’t trade a card for less than you paid, even though nobody else cares what you paid. That’s anchoring.
  • You trade away your best cards quickly for a small gain and keep the duds hoping they’ll come good. That’s the disposition effect.
  • When the whole class suddenly wants one player’s card, you want it too — and pay the most for it. That’s herding.

None of these makes you a bad trader. They make you a normal person. The trick is knowing which one you’re doing.

The other side: what the data says about staying put

Being fair to human nature, the same dataset has a counterweight. The SIP post found that a ₹10,000 monthly SIP over twenty years — straight through 2008 — was worth less than the amount invested in only 12 of 239 months, and never for longer than 8 months at a stretch. And of the fund’s 20 financial years, 15 were up and 5 were down; the worst (FY09, −36.3%) was followed immediately by +71.8%.

So the biases above don’t need to be defeated. They need to be made irrelevant to the decision — which is what a fixed monthly amount, a written asset allocation, and a rule for when you’ll look do. Not because rules are smarter than you, but because they were written by the version of you that wasn’t looking at a red number.

Common mistakes

  • Believing you’re the exception. Everybody does. That belief is itself a documented bias (overconfidence), and it’s what keeps the other five in business.
  • Trying to out-think the biases in the moment. They work because they feel like clear thinking at the time. The defence is a rule made in advance, not willpower made on the day.
  • Confusing activity with control. Checking, trading and rebalancing more often feels like managing risk. Mostly it gives loss aversion and recency more opportunities.
  • Learning the wrong lesson from a good outcome. A trade that worked through luck teaches the same bias as one that worked through skill, and the market doesn’t label which was which.
  • Reading this post and changing nothing. Research on “debiasing” (the classic review is Fischhoff, 1982) found that simply warning people about a bias does little to reduce it. Changing the process — the automation, the rules, the checking frequency — is what moves the outcome.

Takeaway: Loss aversion, recency, anchoring, the disposition effect and herding each have a number on this blog, and each number marks a moment you’ll recognise. A diversified index fund spent 33.7% of twenty years more than 10% below its previous high, which is when the biases bite. You don’t beat them in the moment; you write the rule before it.