Essays in behavioral asset pricing

Abstract

This dissertation consists of two essays in behavioral asset pricing. In the first chapter, I examine why a portfolio is valued less than the sum of its underlying components. I provide a novel explanation for this question by utilizing closed-end funds, mergers and acquisitions, and conglomerates, where the value of the aggregate portfolio and the values of the underlying components can be separately evaluated. Inspired by the model of Barberis and Huang (2008), in which lottery-like stocks are overvalued relative to the prediction of traditional utility theory, I argue that a portfolio holding lottery-like stocks should be valued less than the total value of its components, because lottery-like features get diversified away when lottery-like stocks do not hit “jackpots” together. I present evidence supporting this argument and provide a novel and unifying explanation for the closed-end fund discount puzzle, the announcement-day returns of mergers and acquisitions, and the conglomerate discount.

In the second chapter, I investigate skewness/lottery-like features and subsequent stock returns in the cross-section. I find a robust negative relation between skewness/lotter-like features, proxied by maximum return (MAX) over the last month, and future returns for stocks preferred by individual investors. This negative relation is nonexistent for the rest of stocks. I identify stocks preferred by individual investors through bundling 10 stock characteristics associated with their stock preferences. The negative relation between MAX and future return is produced by the stocks preferred by individuals that account for less than 5% of the overall market capitalization. My results are robust to alternative definitions of MAX and lotter-like features such as total, idiosyncratic, and expected skewness.