Theoretical models predict that overcon�dent investors trade excessively. We test this prediction by partitioning investors on gender. Psychological research demonstrates that, in areas such as �nance, men are more overcon�dent than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997. We document that men trade 45 percent more than women. Trading reduces men’s net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women. It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so. Josh Billings, nineteenth century American humorist It is dif�cult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors. Rational investors make periodic contributions and withdrawals

People are overconfident. Overconfidence affects financial markets. How depends on who in the market is overconfident and on how information is distributed. This paper examines markets in which price-taking traders, a strategic-trading insider, and risk-averse marketmakers are overconfident. Overconfidence increases expected trading volume, increases market depth, and decreases the expected utility of overconfident traders. Its effect on volatility and price quality depend on who is overconfident. Overconfident traders can cause markets to underreact to the information of rational traders. Markets also underreact to abstract, statistical, and highly relevant information, and they overreact to salient, anecdotal, and less relevant information. MODELS OF FINANCIAL MARKETS are often extended by incorporating the imperfections that we observe in real markets. For example, models may not consider transactions costs, an important feature of real markets; so Constantinides ~1979!, Leland ~1985!, and others incorporate transactions costs into their models. Just as the observed features of actual markets are incorporated into models, so too are the observed traits of economic agents. In 1738 Daniel Bernoulli noted that people behave as if they are risk averse. Prior to Bernoulli most scholars considered it normative behavior to value a gamble at its expected value. Today, economic models usually assume agents are risk averse, though, for tractability, they are also modeled as risk neutral. In reality, people are not always risk averse or even risk neutral; millions of people engage in regular risk-seeking activity, such as buying lottery tickets. Kahne-

We examine changes in the stock trading behavior and investment performance of 1,607 investors who switch from phone based to online trading during the period 1992 to 1995. We document that young men who are active traders with high incomes and a preference for investing in small growth stocks with high market risk are more likely to switch to online trading. We also find that those who switch to online trading experience unusually strong performance prior to going online, beating the market by more than two percent annually. After going online, they trade more actively, more speculatively, and less profitably than before-- lagging the market by more than three percent annually. A rational response to reductions in market frictions (lower trading costs, improved execution speed, and greater ease of access) does not explain these findings. The increase in trading and reduction in performance of online investors can be explained by overconfidence augmented by self-attribution bias, the illusion of knowledge, and the illusion of control.

The field of modern financial economics assumes that people behave with extreme rationality, but they do not. Furthermore, people’s deviations from rationality are often systematic. Behavioral finance relaxes the traditional assumptions of financial economics by incorporating these observable, systematic, and very human departures from rationality into standard models of financial markets. We highlight two common mistakes investors make: excessive trading and the tendency to disproportionately hold on to losing investments while selling winners. We argue that these systematic biases have their origins in human psychology. The tendency for human beings to be overconfident causes the first bias in investors, and the human desire to avoid regret prompts the second. There is one important caveat to the notion that we live in a new economy, and that is human psychology... which appears essentially immutable.

∗This paper is an updated version of a previous working paper, “The Positive Role of Overconfidence and

Multicausal abductive tasks appear to have deliberate and implicit components: people generate and modify explanations using a series of recognizable steps, but these steps appear to be guided by an implicit hypothesis evaluation process. This paper proposes a hybrid symbolic-connectionist learning architecture for multicausal abduction. The architecture tightly integrates a symbolic Soar model for generating and modifying hypotheses with Echo, a connectionist model for evaluating hypotheses. The symbolic component uses knowledge compilation to quickly acquire general rules for generating and modifying hypotheses, and for making decisions based on the current best explanation. The connectionist component learns to provide better hypothesis evaluation by implicitly acquiring explanatory strengths based on the frequencies of events during problem solving. 1. Introduction Abduction is the process of generating a best explanation for a set of observations. Symbolic models of abductive rea...

A risk-averse manager’s overconfidence makes him less conservative. As a result, it is cheaper for firms to motivate him to pursue valuable risky projects. When compensation endogenously adjusts to reflect outside opportunities, moderate levels of overconfidence lead firms to offer the manager flatter compensation contracts that make him better off. Overconfident managers are also more attractive to firms than their rational counterparts because overconfidence commits them to exert effort to learn about projects. Still, too much overconfidence is detrimental to the manager since it leads him to accept highly convex compensation contracts that expose him to excessive risk. AVAST EXPERIMENTAL LITERATURE finds that individuals are usually overconfident in that they believe their knowledge to be more precise than it actually is. The incidence of overconfidence is likely to be even greater among CEOs than among individuals at large; for example, Goel and Thakor (2008) show that overconfident individuals are more likely to win the intrafirm tournaments that lead to the rank of CEO. Since overconfidence directly influences decision-making, it is logical to investigate the effects that overconfident managers have on corporate policies and firm value. How does overconfidence affect the investment decisions that managers make on behalf of shareholders? How do compensation contracts optimally adjust to these effects? Do firms benefit from managerial overconfidence? Can overconfidence ever benefit the biased

This article will consider Hogarth's 1975 assessment that "man is a selective, sequential information processing system with limited capacity, . . . ill-suited for assessing probability distributions." Particular attention will be paid to when people make normatively "good" or "poor" probability assessments, what techniques are effective in eliciting "good," coherent probability assessments, and on how these ideas are relevant to the practicing Bayesian statistician. While there are situations where experts can make well-calibrated judgments, it will be argued that more research needs to be done into the effects of expertise, training, and feedback.

Investigations of the degree to which people neglect or use base rates typically require subjects to make a judgment based on presumptive integrations of base rates and likelihood ratios. The present paper deals with a logically prior issue, whether people understand what data are needed to constitute a proper base rate. The method, which we will call the Partial Information Paradigm, has subjects select data relevant to, for example, diagnosis of a disease, D, based on a symptom, S. The question is whether subjects select those frequencies of cases for which information about the presence or absence of D is available, but for which information about the presence or absence of S is not. Only the former frequencies are relevant to the estimation of the base rate of D, hence to the probability of D given S. Six experiments are reported. Four experiments ask subjects to select those frequencies relevant to diagnosis, one of which also had subjects select frequencies relevant to prediction...

The article proposes a simple Bayesian technique for auditing property appraisals to determine whether state accuracy guidelines are met. The proposed technique addresses elicitation of appraisers’ prior beliefs, computation of reappraisal sample sizes and reporting of audit results. To facilitate communication of quantitative audit findings to nonstatistician stakeholders, the concept of variance appears nowhere in prior elicitation or reporting. In contrast to classical frequentist techniques, the Bayesian procedure easily integrates expert judgment and responds flexibly to the arrival of new information. In addition, the Bayesian procedure significantly reduces the number of reappraisals required to regulate appraisal systems when they are functioning well. The technique can be applied in other settings where government officials audit their own work and must convince overseers, especially the public, that accuracy requirements are satisfied.