Abstract: Unrealistic optimism in business can lead to a misallocation of resources and a reduction in welfare. But unrealistic optimism can also stimulate saving and investment and provide added incentives for hard work. In this paper, I explore the interaction of these two effects with respect to entrepreneurs who are overoptimistic about the productivity of their …rms, and I examine the nature of the competition between such optimistic entrepreneurs and their realistic counterparts. I demonstrate that in some technological environments (such as those characterized by small …rms with rapidly decreasing returns to scale) optimistic entrepreneurs may coexist with realists in competitive equilibria or even drive the realists out of business. Moreover, the resulting competitive equilibria will evince signi…cant distortions. This is important because a large body of evidence in the psychology literature indicates that unrealistic optimism is a widespread human trait, one that may be common among business executives.

Why do security analysts issue overly positive recommendations? We propose a novel empirical strategy to assess the relative importance of the leading explanations: strategic distortion, which reflects incentives to trigger small-investor purchases and please management, and non-strategic distortion, which reflects genuine over-optimism, due to self-selection or credulity. We exploit the concurrent issuance of recommendations and earnings forecasts by the same analyst to distinguish those motivations. While non-strategic distorters express their positive view both in recommendations and in forecasts, strategic distorters issue overly positive recommendations but slightly more negative (“beatable”) forecasts. We find that affiliated analysts who have the most positive recommendations outstanding make the most negative forecasts. The same does not hold for unaffiliated analysts. Affiliated analysts are also more likely to distort forecasts downwards just before earnings announcements, allowing management to beat the forecast. Our findings indicate widespread strategic distortion, though the heterogeneity across analysts is large. We show that strategic distortion is persistent within individual analysts, with potential forensic

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The paper analyzes a finite time economy with a single risky asset which pays a one-shot payoff (dividend). The payoff is random and its distribution is not known à priori. Agents observe public signals (random draws from the same distribution) and update their beliefs about the payoff. They trade in order to reshuffle their portfolios according to new beliefs. Agents may use various updating rules and are considered to be of two types: sophisticated who are aware of their future beliefs and prices, and naive who are not. Drawing on the methodology by Sandroni (2000), it is shown that among sophisticated agents, those with less accurate beliefs are driven out, in the sense that their wealth becomes arbitrarily small when the number of signals is sufficiently large. On the other hand, it is shown that this statement may not hold in economies with naive agents only, where even agents with less accurate beliefs may survive.

Major Subject: Financeiii Corporate Governance and Long-Term Stock Returns. (May 2005)

We use network analysis to quantify the flow of information through financial markets. Using unique ultra high frequency data, we compute network and financial variables for transactions that occured during August 2008 in the nearby E-mini S&P 500 futures contract – the cornerstone of price discovery for the S&P 500 Index. We find that network variables presage the information represented by financial variables. Most notably, we find that network variables strongly Granger-case intertrade duration and trading volume, suggesting that network metrics serve as primitive measures of information flow. Finally, we find that the dynamics of returns and volatility are rooted in the network mechanics of the information arrival process – as evidenced both in our data and the results of an agent-based simulation model.

The tendency of some investors to hold on to their losing stocks creates a spread between a stock's fundamental value and its equilibrium price, as well as price underreaction to information. Spread convergence, arising from the random evolution of fundamental values and updating of reference prices, generates predictable equilibrium prices that will be interpreted as possessing momentum. Cross-sectional empirical tests are consistent with the model. A variable proxying for aggregate unrealized capital gains appears to be the key variable that generates the pro¯tability of a momentum strategy. Past returns have no predictability for the cross-section of returns once this variable is controlled for. Kahneman and Tversky's (1979) theory of choice, \prospect theory, " can accommodate many behaviors that are inconsistent with standard expected utility theory. The main element of prospect theory is an S-shaped value function that is concave in the domain of gains and convex in the domain of losses, both measured relative to a reference point. Subsequently, Thaler (1985) constructed a framework known as \mental accounting"

Weber for sharing data with us.

their comments. We are especially grateful to Craig MacKinlay for many lengthy discussions on this subject. Previous versions of this work were circulated under the title “Momentum: Fact or factor? Momentum investing when returns have a factor structure. ” We retain the rights to all errors. The first author appreciates the financial support of the Buying recent winners and shorting recent losers guarantees time varying factor exposures in accordance with the performance of common risk factors during the ranking period. Adjusted for this dynamic risk exposure, momentum profits are remarkably stable across subperiods of the entire post 1926 era. Factor models can explain ninety-five percent of winner or loser return variability, but cannot explain their mean returns. Momentum strategies which base winner or loser status on stock-specific return components are more profitable than those based on total returns. Neither industry effects nor cross-sectional differences in expected returns are the primary cause of the momentum phenomenon. ii