Essays On Empirical Asset Pricing And Behavioral Finance PDF Download

This dissertation consists of four essays exploring how people form beliefs and make decisions in the financial markets and their implications for asset prices. Two common threads run through this dissertation: the persistence of key state variables and the less-than-fully-rational approach to economic decision-making.Chapter 1 studies how professional forecasts of interest rates across maturities respond to new information. I document that forecasts for short-term rates underreact to new information while forecasts for long-term rates overreact. I propose a new explanation based on "autocorrelation averaging,'' whereby, to limited cognitive processing capacity, forecasters' estimate of the autocorrelation of a given process is biased toward the average autocorrelation of all the processes they observe. Consistent with this view, I show that forecasters over-estimate the autocorrelation of the less persistent term premium component of interest rates and under-estimate the autocorrelation of the more persistent short rate component. A calibrated model quantitatively matches the documented pattern of misreaction. Finally, I explore the pattern's implication for asset prices by showing that an overreaction-motivated predictor, the realized forecast error for the 10-year Treasury yield, robustly predicts excess bond returns.Chapter 2, joint with Ye Li, generalizes an exponential-affine asset pricing model to show that the prices of dividend strips reveal the underlying state variables, and thus, strongly predict future market return and dividend growth. We derive and empirically show that expected dividend growth is non-persistent, under which condition the ratio of market price to short-term dividend price, "duration,'' reveals only expected returns information. Duration predicts annual market return with an out-of-sample of R2 19%, subsuming the price-dividend ratio's predictive power. After controlling for duration, the price-dividend ratio predicts dividend growth with an out-of-sample R2 of 30%. Our results hold outside the U.S. We find the expected return is countercyclical and responds forcefully to monetary policy shocks. As implied by the ICAPM, shocks to duration, the expected-return proxy, are priced in the cross-section.Chapter 3, joint with Cameron Peng, shows that mutual funds contribute to cross-sectional momentum and excess volatility through positive feedback trading. Stocks held by positive feedback funds exhibit much stronger momentum, almost doubling the returns from a simple momentum strategy. This ``enhanced'' momentum is robust to alternative positive feedback trading measures and cannot be explained by other stock characteristics, ex-post firm fundamentals, fund flows, or herding. Moreover, enhanced momentum is almost entirely reversed after one quarter, suggesting initial overshooting and subsequent reversal. We argue that the most likely explanation is the price pressure from positive feedback trading. Finally, we relate positive feedback trading to mutual fund performance and show that it can positively predict a fund's return from active management.Chapter 4, joint with Ye Li, presents an intrinsic form of uncertainty in asset management, which we call ``delegation uncertainty.'' Investors hire managers for their superior models of asset markets, but delegation outcome is uncertain precisely because the managers' model is unknown to investors. We model investors' delegation decisions as a trade-off between asset return uncertainty and delegation uncertainty. Our theory explains several puzzles on fund performances. It also delivers asset pricing implications supported by our empirical analysis: (1) because investors partially delegate and hedge against delegation uncertainty, CAPM alpha arises; (2) the cross-section dispersion of alpha increases in uncertainty; (3) managers bet on alpha, engaging in factor timing, but factors' alpha is immune to the rise of their arbitrage capital -- when investors delegate more, delegation hedging becomes stronger. Finally, we offer a novel approach to extract model uncertainty from asset returns, delegation, and survey expectations.