UC Berkeley

This dissertation studies the effects of asymmetric information and learning on asset prices and investor decision-making. Two main themes run through the work. The first is the linkage between investor decisions and the information used to make those decisions; that is, portfolio choices reflect the nature and quality of available information. The second theme is the interaction between investor learning and price informativeness. The information held by individual investors is reflected in market prices through their trading decisions, and prices thus transmit this information to other investors.

In the first chapter, Asymmetric Information in Financial Markets: Anything Goes, I study a standard Grossman and Stiglitz (1980) noisy rational expectations economy, but relax the usual assumption of the joint normality of asset payoff and supply. The primary contribution is to characterize how the equilibrium relation between price and fundamentals depends on the way in which investors react to the information contained in price. My solution approach dispenses with the typical “conjecture and verify” method, which allows me to analytically solve an entire class of previously intractable nonlinear models that nests the standard model. This simple generalization provides a purely information-based channel for many common phenomena. In particular, price jumps and crashes may arise endogenously, purely due to learning effects, and observation of the net trading volume may be valuable for investors in the economy as it can provide a refinement of the information conveyed by price. Furthermore, the value of acquiring information may be non-monotonic in the number of informed traders, leading to multiple equilibria in the information market. I show also that the relation between investor disagreement and returns is ambiguous and depends on higher moments of the return distribution. In short, many of the standard results from noisy rational expectations models are not robust. I introduce monotone likelihood ratio conditions that determine the signs of the various comparative statics, which represents the first demonstration of the implicit importance of the MLRP in the noisy rational expectations literature.

In the second chapter Do Fund Managers Make Informed Asset Allocation Decisions?, a joint work with Jacob S. Sagi, we derive a dynamic model in which mutual fund managers make asset allocation decisions based on private and public information. The model predicts that the portfolio market weights of better informed managers will mean revert faster and be more variable. Conversely, portfolio weights that mean revert faster and are more variable should have better forecasting power for expected returns. We test the model on a large dataset of US mutual fund domestic equity holdings and find evidence consistent with the hypothesis of timing ability, especially at three- to 12-month forecasting horizons. Nevertheless, whatever timing ability may be reflected in portfolio weights does not appear to translate into higher realized returns on funds' portfolios.