Behavioural finance

Based on Wikipedia: Behavioural finance

The Day the Market Lost Its Mind

On October 19, 1987, the Dow Jones Industrial Average plummeted more than twenty percent in a single day. Smaller stocks fared even worse. There was no war, no assassination, no catastrophic news event to explain it. The world looked essentially the same on October 20 as it had on October 18. Yet billions of dollars in value had simply evaporated.

This wasn't supposed to happen.

For decades, economists had built elegant mathematical models on a simple foundation: people are rational. Given the same information, rational actors should reach similar conclusions about what a stock is worth. Prices might fluctuate, but they shouldn't swing wildly without cause. Markets, in this view, are efficient machines that incorporate all available information into prices almost instantaneously.

The crash of 1987 wasn't an isolated embarrassment for this theory. It was a preview of things to come. Within a few years, Japan's Nikkei index would balloon to 40,000, with companies trading at price-to-earnings ratios in the triple digits. Nippon Telephone and Telegraph alone achieved a market value exceeding the entire stock market of West Germany. Then the Nikkei collapsed, losing nearly half its value in less than a year.

The 1990s brought the dot-com bubble, where companies with no revenue and no clear path to profitability commanded valuations in the billions. When that bubble burst around 2000, even large, profitable technology companies lost eighty percent of their value.

Something was clearly wrong with the theory. Enter behavioural finance—a field built on a heretical premise: maybe people aren't so rational after all.

The Rational Investor and Other Myths

Traditional finance rests on two pillars. The first is Modern Portfolio Theory, developed by Harry Markowitz in the 1950s. This framework treats investing as an optimization problem. Every asset has an expected return, a level of risk (measured by how much its price bounces around), and a relationship to other assets. A smart investor, according to this theory, constructs a portfolio that maximizes returns for any given level of risk.

The second pillar is the Efficient Market Hypothesis, often abbreviated as EMH. This idea, most associated with economist Eugene Fama, holds that market prices reflect all publicly available information. If a company announces strong earnings, the stock price adjusts immediately. There are no bargains to be found because everyone has access to the same information and processes it correctly. The logical conclusion? Don't bother trying to beat the market. Just buy a diversified index fund and hold it.

These theories are mathematically elegant. They won Nobel Prizes. They shaped how pension funds invest trillions of dollars. They have one small problem: they require humans to behave like calculators.

Real humans do not behave like calculators.

A Field 150 Years in the Making

The insight that psychology matters in markets isn't new. Charles Mackay's "Extraordinary Popular Delusions and the Madness of Crowds," published in 1841, catalogued financial manias from the Dutch tulip bubble of the 1630s to the South Sea Company collapse of 1720. His central observation was that groups of people can behave in ways that no individual member would consider sensible.

Gustave Le Bon's "The Crowd: A Study of the Popular Mind," published in 1895, went deeper into the psychology of group behaviour. When people gather into crowds, Le Bon argued, they lose their individual rationality. They become suggestible, emotional, and prone to extremes. A mob can commit acts that would horrify each of its members acting alone.

George Charles Selden applied these ideas directly to Wall Street in his 1912 book "Psychology of the Stock Market." He observed that fear and greed drive trading decisions far more than cool analysis of balance sheets. Investors chase rising stocks not because they've carefully calculated intrinsic value, but because they fear missing out on gains. They panic-sell during downturns not because fundamental values have changed, but because the emotional pain of watching prices fall becomes unbearable.

These early works were prescient but largely ignored by academic economists. The mathematical models were so useful, so tractable, that the profession had little appetite for the messy complications of human psychology.

The Biases That Move Markets

Modern behavioural finance emerged in the 1970s and 1980s, pioneered by psychologists Daniel Kahneman and Amos Tversky. Their research documented dozens of systematic ways that human judgment deviates from rationality. These aren't random errors that cancel out across many people. They're predictable patterns, hardwired into how we think.

Consider overconfidence. Most investors believe they can beat the market average. This is mathematically impossible—for every winner, there must be a loser. Yet studies consistently show that active traders earn lower returns than passive investors, largely because their excessive confidence leads them to trade too frequently, racking up costs and taxes.

Or consider loss aversion. Kahneman and Tversky demonstrated that the pain of losing money is roughly twice as intense as the pleasure of gaining the same amount. This asymmetry explains why investors hold onto losing stocks far too long (hoping to avoid the pain of crystallizing a loss) while selling winners too quickly (locking in the pleasure of a gain).

There's anchoring—our tendency to fixate on arbitrary reference points. If you bought a stock at fifty dollars, you're likely to view that price as meaningful even when market conditions have changed entirely. You might refuse to sell at forty dollars, waiting to "get back to even," even when the rational move is to cut your losses.

Herding behaviour explains how bubbles form. When prices rise, people buy not because they've analyzed the fundamentals but because others are buying. This creates a feedback loop where rising prices attract more buyers, pushing prices higher still. The process continues until it doesn't—at which point the same herding instinct drives a collapse.

The Counterarguments

Critics of behavioural finance raise several objections worth taking seriously.

The first is that individual irrationality doesn't necessarily produce market-wide irrationality. If some investors are too optimistic and others too pessimistic, their errors might cancel out, leaving prices approximately correct. Markets aggregate millions of opinions; surely the wisdom of crowds emerges.

There's something to this. Random individual mistakes probably do cancel out. But behavioural biases aren't random. They're systematic. When markets rise, most people become more optimistic, not less. When prices fall, pessimism becomes widespread. These correlated errors don't cancel—they amplify.

A second criticism is that even if biases exist, sophisticated arbitrageurs should exploit them, driving prices back to rational levels. If stocks become overvalued, smart money should sell them short, profiting when prices fall and correcting the mispricing in the process.

In theory, yes. In practice, arbitrage has limits. Short-selling is expensive and risky. If you short an overvalued stock and it becomes even more overvalued before correcting, you might go bankrupt before you're proven right. As John Maynard Keynes reportedly said, "Markets can remain irrational longer than you can remain solvent." The dot-com bubble lasted years, destroying many arbitrageurs who correctly identified the madness but couldn't survive the interim.

A third objection is that behavioural finance is better at explaining anomalies after the fact than predicting them in advance. Traditional finance makes precise, testable predictions. Behavioural finance sometimes seems to offer only the observation that "people are weird." This is a fair criticism, and one that behavioural researchers have worked to address.

Putting Numbers on Irrationality

Quantitative behavioural finance attempts to make the field more rigorous by building mathematical models of how biases affect prices. Richard Thaler, who won the 2017 Nobel Prize in Economics, developed models showing how markets process new information in three distinct phases.

First comes underreaction. When significant news arrives, prices typically don't adjust enough immediately. Investors seem to anchor to old prices, adjusting their views too slowly. This creates momentum—stocks that have risen tend to keep rising for a while, and stocks that have fallen tend to keep falling.

Then comes adjustment, as prices gradually move toward where they should be.

Finally, overreaction. Prices often overshoot, moving too far in the direction of the news. This explains a curious pattern in market data: stocks that have performed very well over the past few years tend to underperform over the next few years, and vice versa. The market eventually corrects its excess enthusiasm or pessimism.

Other researchers have built agent-based models—computer simulations where thousands of artificial traders interact according to behavioural rules. These simulations can generate bubbles and crashes that look remarkably like real market events, something that's very difficult to produce with models assuming pure rationality.

The Experimental Evidence

Vernon Smith, who won the 2002 Nobel Prize in Economics, developed a powerful method for testing market theories: experimental markets where real people trade with real money.

In one famous series of experiments, participants traded an asset that paid a fixed dividend for fifteen periods and then became worthless. The math was simple enough that anyone could calculate the asset's declining fundamental value. Yet prices consistently soared above this fundamental value, creating bubbles that eventually crashed.

These weren't uninformed investors. The fundamental value was transparent. The time horizon was clear. There was no complex information to process. Yet bubbles formed anyway. Something about human psychology, when combined with the dynamics of trading, seems to produce speculative excess almost inevitably.

Gunduz Caginalp and collaborators took this further, developing differential equations to model how prices evolve based on the amount of cash in the system, the price trend, and investors' assessment of fundamental value. One key prediction: doubling the amount of cash available to traders while keeping the number of shares constant should roughly double the size of bubbles. Experiments confirmed this prediction.

The Noise Problem

One challenge in studying behavioural effects in real markets is separating psychology from everything else that moves prices. Fischer Black, famous for the Black-Scholes options pricing model, coined the term "noise" to describe all the random factors that affect prices—wars, weather, policy changes, technological breakthroughs, earnings surprises, and countless other events.

Noise makes it difficult to isolate behavioural patterns. Many statistical studies have found little evidence of predictable price movements, seemingly confirming market efficiency. But this might just mean the noise is overwhelming the signal, not that behavioural effects don't exist.

Researchers have found clever ways around this problem. Closed-end funds are particularly useful. Unlike regular mutual funds, closed-end funds issue a fixed number of shares that trade on exchanges like stocks. The fund's net asset value—what you would get if you liquidated all holdings—is calculated and published regularly. Yet the price at which shares trade often differs substantially from this value.

By studying these deviations, researchers can examine behavioural effects while controlling for fundamental value. One study found that when a fund's price deviates significantly from its net asset value, it tends to snap back toward that value the next day. More intriguingly, large deviations in one direction are often preceded by deviations in the opposite direction—as if traders are positioning themselves in anticipation of moves that haven't happened yet.

What This Means for You

Behavioural finance offers no magic formula for beating the market. Indeed, one of its core insights is that trying to beat the market is itself often a behavioural error—overconfidence leading to excessive trading.

But awareness of biases can help you avoid costly mistakes. Recognizing that you're likely to hold losers too long and sell winners too quickly might help you make better decisions about your portfolio. Understanding that your confidence in your stock picks is almost certainly inflated might encourage more humility and diversification.

The broader lesson is about the nature of markets themselves. They're not the perfectly efficient machines that textbooks once described, nor are they complete chaos. They're human institutions, subject to human psychology, capable of both remarkable information processing and spectacular folly.

The crashes and bubbles of the past weren't aberrations. They were natural products of how human minds interact in markets. There will be more bubbles, and more crashes, and each time, people will be surprised, because one of our most persistent biases is believing that this time is different.

It never is.