Behavioural finance is a fashionable genre of academic research, and a productive strategy for writing academic papers. But it was not mentioned as a resource by any of the interviewees in Free Capital, and I have never found it much help in my own investing. There are several reasons for this.
Many explanations, few predictions The comprehensive menu of alleged behavioural biases offers an ex-post explanation for almost any decision which hindsight renders sub-optimal. Investors who borrowed money to invest in shares in September 1987 suffer from “over-confidence”; those who failed to do so in March 2009 suffer from “myopic loss aversion.” Investors who respond quickly to new information overweight “availability”; those who respond slowly are “anchored” by prior beliefs. And so on for every other ex-post mistake.
This descriptive charm and versatility is palatable both to academic story-tellers and to casual readers. But to be scientifically or instrumentally useful, a paradigm needs to make some specific predictions. Behavioural finance seems more like Freudian psychology: it can be contorted to explain anything ex-post, and therefore predicts nothing ex-ante.
Poorly defined allegations of “irrationality” A common trope in behavioural finance articles is to document some observed behaviour of investors and assign to this the pejorative label “irrational.” This is an over-used and often unwise epithet, because “irrationality” is usually defined against a narrow normative standard. On closer examination, all that can usually be said is that large groups of people who follow the observed action indiscriminately over long periods of time will lose money compared with large groups of people who don’t. It does not follow that all (or even most) instances of the action are individually irrational.
Data mining and publication bias Any behaviour which can be labelled “irrational” (often dubiously – see above) against some normative standard is newsworthy and publishable. Null results where people behave “rationally” are less exciting, and more likely to be filed in a drawer. The suspicion of data mining for manifestations of “irrational” behaviour is increased by the disparate and often contradictory nature of the claimed biases.
Signal detection misconstrued as probabilistic judgement Many behavioural finance articles assert that investors make invalid probabilistic judgments. In the archetypal example, it is said to be a “conjunction fallacy” that Linda, a 31 year-old with biographical data suggesting liberal social views, is “more likely” to be (a) a bank teller and a feminist rather than (b) just a bank teller.
But the answer (a) which most people give amy not be a probabilistic judgement; it instead may be a socially appropriate response to cues. This type of response is learnt both in educational settings (did you ever see an exam question where you were not expected to "use all the information provided"?), and also in everyday life (the social costs of ignoring cues are usually larger than the social costs of over-responding to them).
The so-called “conjunction fallacy” is generated only because the question is a sort of word-trick: a probability test masquerading as a cue-response test, or a signal detection test. If the normative standard is signal detection or polite response, the so-called 'wrong' answer is correct. And with a slight change in the question wording to focus attention on numerical frequencies rather than cue-responses, most people give the “correct” probabilistic answer (see Gigerenzer).
Or to put this another way: the "wrong" answer may be correct if I interpret the required probability as one conditional on Linda's liberal social views and the giving of the signal (i.e. the fact that you chose to draw my attention to those views, presumably as a cue).
Detecting dishonesty is more useful than simulating truth Intuitions about truth and falsehood are often more usefully directed towards detecting liars, rather than simulating causal relations. For example, apropos the “Linda” example above: when considering reports, it may be a good heuristic to trust the insinuations of people who provide many details - people like Linda's acquaintance - because people who provide many details tend to be truthful witnesses. (This heuristic may invert when considering predictions, where people who provide many details tend to be charlatans.)
More generally, behavioural finance focuses mainly on failures of cognition and calculation, but largely neglects social phenomena such as trust and deception. A visiting Martian might expect the title "behavioural finance" to encompass the study of frauds, stock promotions, and pump & dump operations - all endemic behavioural phenomena in financial markets - but in fact the subject never goes there.
Few normative prescriptions Behavioural finance invites us to gawp at all the foolish mistakes other investors make. This financial freak show is superficially entertaining, but it doesn’t necessarily make us better investors: it doesn’t tell us what to do. To catalogue all your biases and then do nothing much about them is not humility, it is boasting of your modesty.
Some emotions are good emotions In the absence of explicit normative prescriptions, the implicit prescription of behavioural finance appears to be that investors should try to suppress all emotion. This is probably neurologically unfeasible, and in any case undesirable. A better aspiration is to aim to have the right emotions : to hope that your delusions are benign, and your compulsions have utility. A good recent book on this concept is The Emotionally Intelligent Investor.
Update (16 March 2014): How knowing about biases can hurt you.
Update (11 April 2017): John Kay on the weaknesses of behavioural economics.