Essays on Investor Beliefs and Asset Pricing

This dissertation is composed of three chapters addressing the connections between investor beliefs and asset pricing. Specifically, I focus on one prevailing pattern of investor beliefs in the finance literature, return extrapolation. The idea is that investor expectations about future market returns are a positive function of the recent past returns. In this dissertation, I use this concept to understand a number of facts in the asset pricing literature. Return extrapolation attracts growing attention in the literature, not only because it well explains real-world investors' expectations in the survey, but also because it significantly drives investor demand towards stocks. Therefore, we should anticipate a connection between return extrapolation measurement and the stock market dynamics. However, contrary to the intuition, previous empirical studies fail to document a significant connection. In Chapter 1, "Time-varying Impact of Investor Sentiment", I recover this connection. Specifically, I formally define investors who extrapolate past returns as extrapolators and incorporate their wealth level into analysis. My main finding is that return extrapolation interacts strongly with extrapolators' wealth level in predicting future market returns. Therefore, conditional on extrapolators' wealth level, return extrapolation significantly explains stock market returns. The return extrapolation concept also raises challenges to the asset pricing models under the rational expectation frameworks. Specifically, rational expectation theories lead to a positive correlation between expectations and future realized returns, whereas return extrapolation indicates a negative correlation. Given this discrepancy, there is a clear demand for a behavioral asset pricing model that can simultaneously explain survey evidence on investor expectations and the classical asset pricing puzzles. In Chapter 2, "Asset Pricing with Return Extrapolation", coauthored with Lawrence Jin, we present a new model of asset prices based on return extrapolation. The model is a Lucas-type general equilibrium framework, in which the agent has Epstein-Zin preferences and extrapolative beliefs. Unlike earlier return extrapolation models, our model allows for a quantitative comparison with the data on asset prices. When the agent's beliefs are calibrated to match survey expectations of investors, the model generates excess volatility and predictability of stock returns, a high equity premium, a low and stable risk-free rate, and a low correlation between stock returns and consumption growth. In Chapter 3, "Dark Matter" of Finance in the Survey, I investigate another attribute of investor beliefs—tail risk perceptions. Although tail risks play significant roles in explaining asset pricing puzzles, researchers have very limited knowledge about them because tail events are difficult to observe. I use Shiller tail risk survey to empirically investigate tail risk perceptions. In this survey, investors are asked to report their estimated probability for a crash event in the U.S. stock market. However, when using survey data to understand investors’ perception of tail risks, there are two fundamental challenges. First, is tail risks survey reliable? Second, to avoid cherry-picking, is there a unified framework to explain different attributes of investor beliefs? My analysis provides positive answers to both questions. First, I show that Shiller tail risk survey is reliable. More importantly, I show that return extrapolation can serve as a unified belief formation framework to explain not only variations in investor expectations but also in tail risk perceptions.