CFA Conference: Nicholas C. Barberis

CFABehavioral Finance: Core Principles and Practical Applications

Moderated by Jason Voss (@TheIntuitInvest), CFA, CFA Institute

Nicholas C. Barberis is the Stephen and Camille Schramm Professor of Finance at the Yale School of Management, where he is a researcher in the field of behavioral finance. Prior to his appointment at Yale, he taught for several years at the University of Chicago’s Booth School of Business. Mr. Barberis has won the Paul A. Samuelson Prize for outstanding research and the FAME Research Prize. He received his bachelor’s degree in mathematics from Cambridge University and his PhD in business economics from Harvard University.

  • Behavioral finance argues that a better understanding of human psychology—in particular, that market participants are not always rational—can improve financial decision making
  • This session will present an overview of the current state of the field, with an emphasis on the past decade but including some speculation about the most promising directions for the next decade
  • Behavioral finance is not a minor supplement to the traditional finance paradigm but, rather, an essential framework for understanding many of the most important financial phenomena

My session notes follow. As always, these are contemporaneous notes. I make no guaranty as to their accuracy or completeness.

From the 1950s-1990s, finance was dominated by the rational agent framework, which assumes that market participants are rational. Since the 1990s, a second framework has grown in prominence – the behavioral finance framework, which argues that many financial phenomena are the result of less than fully rational behavior by participants. BF borrows insights from cognitive and social psychology and tries to build financial models that are more (psychologically) realistic. BF has been around for decades, but began to be taken seriously in the 1990s. The field has since exploded.

Rational agent camp: U of Chicago, MIT Sloan, Wharton, Eugene Fama

BF camp: Yale, Harvard, Princeton, Robert Shiller, Daniel Kahneman

BF draws on cognitive psychology to understand how people depart from full rationality.

  • Psychology of belief formation (representativeness; overconfidence; conservatism)
  • Psychology of decision-making (loss aversion; probability weighting; ambiguity aversion)

Three main areas of application

  • Asset pricing
  • Investor behavior
  • Corporate finance

Possible to structure by psychology or by application – he’ll go with psychology


Three important ideas

  • Representativeness
  • Overconfidence
  • Prospect theory

Three emerging ideas

  • Experience effects
  • Neuroeconomics
  • Aspects of social psychology

Criticisms BF

“Prescriptive” BF

THEME: BF is essential to understand many financial phenomena (market fluctuations; momentum; trading volume; M&A activity volume)


  • Broad idea – e.g., belief in the “law” of small numbers
  • Key consequence – over-extrapolation of past trends (e.g., hot hand theory)
  • Stock market fluctuations – P/E moves, but why? No consensus
  • Fluctuations due to rationally varying forecasts of future earnings growth (decisively rejected by Shiller in 1981 – there was no predictive ability of these forecasts)
  • Similar theories re interest rates and risk rejected on similar grounds
  • Rationalists now argue that it’s due to concerns about falling below habit levels of consumption – changes in risk aversion
  • BF argues that fluctuations arise because some investors extrapolate past returns (mean reversion demands otherwise)
  • Key piece of evidence: survey experience data that clearly supports BF – beliefs are extrapolative (good markets will keep going up and vice versa)
  • Momentum and reversals – we usually see medium-term momentum but longer-term reversals; BF is a parsimonious way of understanding these trends
  • Application – we like funds with recent good returns
  • Application – bubbles


  • Type 1 – overplacement (overly rosy views – 80% see themselves as above average)
  • Type 2 – overprecision (too confident in the accuracy of their own beliefs – 90% confidence when 50% chance)
  • Hard to make sense of high volume of trading without appeals to overconfidence (Odean: individual investors who trade the most do the worst; men trade more and underperform)
  • Little evidence of enhanced value via M&A activity, but there’s lots of such activity (Buffet: many toads kissed but few become handsome princes)
  • CFOs are highly overconfident about stock market performance (80% confidence met only 36% of the time)

Prospect Theory

  • Loss aversion – we feel losses more acutely than similar gains
  • Probability weighting (nonlinear) – we overweight tail events (we buy too many lottery tickets and too much insurance)
  • Probability weighting is more broadly applicable – we buy too many IPOs, for example; but the equity risk premium is puzzlingly high; we overweight crashes and underweight equity returns generally
  • Loss aversion suggests that higher vol stocks should have higher average returns, but they don’t

Three emerging ideas

  • Experience effects – We should take account of all past data, but we overvalue things that are personal and recent
  • Neuroeconomics – “realization theory” case study: we should cut our losses and let winners run, but we do the opposite (the disposition effect) – we feel good when we realize a gain and a burst of pain when we realize a loss
  • Social Psychology – Cognitive dissonance (we manipulate beliefs to comport with our typically positive self-image); conformity (the Asch line experiment); groupthink (group cohesiveness more important than the facts, especially strong when the group is attractive and valued and when the situation is stressful)

Criticisms of BF

  • The field is undisciplined (emphasize new predictions)
  • Smart traders will undo the effects of the irrational (cheap for a reason; limits to arbitrage)

Prescriptive BF

  • Explicit debiasing techniques (for overconfidence – list three reasons “why not”; give a public rationale for positions)
  • Institutional design (“libertarian paternalism” – Nudge); don’t limit options, but create solid default (e.g., make 401k plans opt in rather than opt out)


  • “Rational” involves a statement about beliefs using Bayes Rule; re decisions, it involves expected utility
  • How can one tell a stock is overvalued if our own judgment can’t entirely be trusted? We need to learn more about the moment when “momentum crashes”
  • People extrapolate a past rend into the future, but the gambler’s fallacy applies too; reconcile them because the gambler’s fallacy applies when we know the model of the world (a fair coin), but we extrapolate when we don’t know the model
  • What’s the future of BF for us? Will knowledge of BF lead to the “advantage” be arbitraged away? These problems are very deeply ingrained – evolutionary roots; BF supports the idea of the mispricing of assets
  • Correlation of overconfidence and over-trading – the agency problem; the data is firm and deep if not totally dispositive
  • Future directions – what about meditation or focused contemplation? People prefer things that are familiar – home bias or employer stock issues explained; probably deeply engrained, but difficult to test
  • Is there a reconciliation possible between the rational school and BF? The Chicago School is useful and important; but it only takes you so far; no reconciliation possible, but there can be a convergence or synthesis
  • Confirmation and availability bias are distinct but related
  • Professionals outperform “amateur” investors; the puzzle is why they don’t outperform by a lot (they lose after fees); professionals share these problems too
  • Do risk questionnaires make sense? Important question (Martin Weber research) but not enough research yet
  • Is BF applicable to the pension crisis? There’s pain involved in admitting a problem; we’d rather kick the can down the road
  • Regret is a powerful emotional force too
  • What are the ethics of libertarian paternalism? Not an unmitigated good as it really is paternalistic; there needs to be a reasonable and sensible default option (e.g., default portfolio allocation)

1 thought on “CFA Conference: Nicholas C. Barberis

  1. Pingback: CFA Conference: Post Compendium | Above the Market

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