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Daniel Kahneman and Richard Thaler: behavioural finance and why rules protect us from ourselves

Investment ideas Ideas explained with data from the portfolios of six investors

A psychologist and an economist who showed that people are not the calm calculators the old theory assumed. Loss aversion, mental accounts, nudges, and why a bot with fixed rules does not panic.

Who they were

Daniel Kahneman (1934–2024) was an Israeli-American psychologist who spent most of his career at the Hebrew University, Princeton and elsewhere. For decades he worked with Amos Tversky (1937–1996), and their joint papers on how people judge and decide under uncertainty founded a new field. In 2002 Kahneman received the Nobel Prize in economics, although he was a psychologist; Tversky had died in 1996, and the prize is not given after death.

Richard Thaler (born 1945) is an economist at the University of Chicago who took these findings into economics and finance. He received the Nobel Prize in 2017. Kahneman wrote the bestseller Thinking, Fast and Slow (2011); Thaler wrote Misbehaving (2015) and, with the lawyer Cass Sunstein, Nudge (2008).

The core ideas

In 1979 Kahneman and Tversky published prospect theory in the journal Econometrica. Its main points are simple. People judge an outcome as a gain or a loss relative to a reference point, usually what they have now or paid, and not as a final level of wealth. A loss hurts more than an equal gain pleases; the commonly cited estimate is about twice as much, and this is called loss aversion. And people react to small probabilities in a distorted way: they overpay for lottery tickets and for insurance against remote events.

Their wider programme, often called heuristics and biases, showed that the mind uses shortcuts that usually work and sometimes fail in a predictable direction: anchoring on a number you have just seen, overconfidence in your own forecasts, judging how likely something is by how easily you can recall an example. Kahneman described a fast, intuitive way of thinking and a slow, effortful one, and argued that the fast one decides much more than we admit.

How it works in practice

Thaler added concepts that fit the behaviour of money. The endowment effect: people value what they own more than the same thing before they own it, as in the well-known mug experiments, where owners asked for much more than buyers would pay. Mental accounting: people sort money into labelled pockets, such as a bonus, savings or house money, and treat the same euro differently depending on the label. Myopic loss aversion, with Shlomo Benartzi: the more often you look at a portfolio, the more often you see a loss, and the less risk you are willing to carry.

Researchers who followed this line found a familiar pattern in the accounts of private investors. They tend to sell winners too early and keep losers too long, because selling a loser makes the loss real. They trade more than is good for them, and the most active households earned less after costs. They buy after prices have risen and sell after they have fallen. Thaler's practical answer was the nudge: change the setting, not the person. Automatic enrolment in a pension plan, and the Save More Tomorrow plan, in which employees commit part of their future raises to saving, raised savings without asking anyone to become more disciplined.

What six current portfolios show

A filing cannot show feelings, but it shows behaviour. Berkshire Hathaway opens or closes on average 1.0% of its book per quarter, and Duquesne 23.8%: one holds almost everything for years, the other changes a quarter of the book every three months. Neither style is proved right by the table, but both are rules, and the best-known investors are the ones who found a rule that suits their temperament and kept it through bad years.

The concentrated books are the hardest test of loss aversion. Pabrai holds 4 stocks, the largest 43.3% of his book; holding that through a drop of a third requires a stomach that few people have. Buffett has often said, in his own words, that an investor should be fearful when others are greedy and greedy when others are fearful, which is a plain description of how to behave against the biases described here. Saying it is easy; doing it is the rare part.

Six real portfolios side by side, from the latest filing of each investor (Form 13F, US-listed stocks only). Effective positions is the number of equal-sized positions that would be as concentrated as the real portfolio: one divided by the sum of the squared shares. Opened or closed is the average over the four quarters of half the combined share of positions that were opened or closed.

Where it is challenged

There are several criticisms. Some famous results have been hard to reproduce, and Kahneman himself later said that he had put too much trust in small studies on priming in his book. Some researchers argue that loss aversion is weaker, or depends more on the situation, than the first papers suggested. Supporters of efficient markets reply that biases do not automatically produce profit: a mistake of the crowd can be real and still not give a clear way to earn from it after costs.

Nudges have their own critics, who point out that the effects are often smaller in large trials than in the first studies and that someone has to decide in which direction to nudge. And knowing about a bias does not make you immune to it: Kahneman said in interviews that after decades of study he remained as prone to these errors as anyone.

What to take from it

Know your reference point and ask whether you would buy what you hold today, if you did not own it. Decide the rules before the storm, when you are calm: how much to hold, when to rebalance, when to sell. Look at the portfolio less often. Automate what you can, because a default that you do not need to remember beats a decision that you must find the courage to make. Write down why you bought, so that later you cannot rewrite your own story.

This is an explanation of research, not a recommendation to buy or sell anything.

Where it meets our bots

This is the main reason our bots exist as rules. Each one trades by a rule that was fixed in advance: it does not feel fear on a bad day, does not wait for a loser to come back out of pride, and does not chase what rose last week. Every trade is public and every account is virtual, so the history is a record of the rule and not of a mood. The reverse is also true: a bot has no judgement, and a rule can be wrong for a long time.

The honest lesson is for the human reader. Look at the worst fall on a bot's page and ask whether you would have kept to the rule through it. A rule that you abandon at the bottom gives you the losses and none of the recovery. The guides below explain each rule and its weak points.

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