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

This study attempts to extend the meaning of personalization and argues that not only personal information needs but also emotional or mental needs aroused by outside influences need to be taken into account. This study introduces a new dimension in the process of filtering out unnecessary information: the conformity behavior. Conformity means that people will tend to converge on similar behavior because they are affected by social norms. This study compares the effects of four personalization mechanisms on subjective decision quality. The results show that pure conformity is better than target conformity. Target conformity is no significant different from collaborative filtering. The result could help people re-examine the ideal approach in making personalization systems. 1.

This paper focuses on the process of expectation formation. Specifically, the question is addressed whether individuals think strategically when they form beliefs about other players' behavior. Most belief learning models assume that people abstract from strategic considerations. Using an incentive compatible mechanism, experimental data are o btained on subjects' expectations in a step-level public good game and in a game against nature. Beliefs in the interactive games develop in the same way as in the game against nature, providing evidence that strategic considerations do not play a role. The evidence is consistent with predictions derived from the naive Bayesian model.

Trading volume on the world’s markets seems high, perhaps higher than can be explained by models of rational markets. For example, the average annual turnover rate on the New York Stock Exchange (NYSE) is currently greater than 75 percent 1 and the daily trading volume of foreign-exchange transactions in all currencies (including forwards, swaps, and spot transactions) is roughly one-quarter of the total annual world trade and investment flow (James Dow and Gary Gorton, 1997). While this level of trade may seem disproportionate to investors’ rebalancing and hedging needs, we lack economic models that predict what trading volume in these market should be. In theoretical models trading volume ranges from zero (e.g., in rational expectation models without noise) to infinite (e.g., when traders dynamically hedge in the absence of trading costs). But without a model which predicts what trading volume

We analyze experimental evidence on whether untrained subjects can predict how trustworthy an individual is. Two players on a TV show play a high stakes prisoner’s dilemma with pre-play communication. Our subjects report probabilistic beliefs that each player cooperates, before and after communication. Subjects correctly predict that women, and players who voluntarily promise that they will cooperate, are more likely to cooperate. They are also able to distinguish truth from lies when a player is asked about his or her intentions by the host. In consequence, and in contrast with the psychology literature, our naive subjects are able to distinguish defectors from cooperators, with the latter inducing beliefs that are 7 percentage points higher. We also study Bayesian updating in the natural and complex context, and find mean reversion in beliefs, and reject the martingale property.

This study provides evidence that the out-of-sample predictive performance of conjoint analysis can be improved by measuring and modeling the uncertainty of preference statements. Preferences are measured in terms of rating scores for products, while uncertainty is considered as an indicator of the stability of preferences. Uncertainty is measured in six different ways and for each of these measures a Hierarchical Bayes model was developed. The models were empirically tested and the results indicate that including uncertainty measures leads to an improvement in out-of-sample predictive performance and the precision of the predictions. The best performance was found for a combination of two implicit uncertainty measures, location and test-retest, using a weighted regression model. The estimated weights suggest that preferences with a low score, relative to all of the scores from an individual, should have a smaller weight or be given a higher impact. In addition, we found some evidence that preferences change over time. While there appears to be evolution, we found that valuable information could be extracted from the initial data when the low preference scores were allowed to have a higher impact relative to the other preference scores, supporting the idea that preference about products that a consumer does not like is more informative and more stable over time.

This chapter develops a unified modeling framework for analyzing the strategic behavior of forecasters. The theoretical model encompasses reputational objectives, competition for the best accuracy, and bias. Also drawing from the extensive literature on analysts, we review the empirical evidence on strategic forecasting and illustrate how our model can be structurally estimated.