Essays on investor sentiment, mispricing, and cross-section of stock returns

Abstract

This thesis studies how mispricing caused by investors’ biased expectations of future cash flow affects the cross-section of stock returns. Since the Capital Asset Pricing Model (CAPM), asset pricing models have long been associated with risk-based explanations, and the standard finance paradigm fails to account for investor irrationality. Studying the conditional CAPM model and using cuttingedge machine learning algorithms, this thesis provides novel evidence demonstrating that mispricing from the cash flow channel is systematic and explains characteristic-based anomaly returns.

In the Second Chapter, I explore the conditional version of the CAPM on sentiment to provide a behavioural intuition behind the value premium and other anomalies. I find betas and the market risk premium to vary over time across different sentiment indices and portfolios. More importantly, the state beta derived from this sentiment-scaled model provides a behavioural explanation of the value premium and several anomalies. Different from the static beta-return relation that gives a flat security market line, I document upward-sloping security market lines when plotting portfolio returns against their state betas and portfolios with higher state betas earn higher returns.

The Third Chapter extends the exploration of the relation between the timevarying sentiment effects and the cross-section of returns by analysing how beta overpricing drives returns of a large set of long-short anomaly strategies. Highbeta stocks are more prone to speculative overpricing (Hong and Sraer; 2016). I find that anomaly-shorts comprise more high-beta stocks, especially during optimistic periods, and thus earn lower returns than longs. Further supporting beta-driven anomalies, the principal component analysis shows that both long and short legs exhibit strong beta comovement. More importantly, beta overpricing gets attenuated in recent years, consistent with the post-publication decay in anomaly returns. I summarise two sources contributing to beta overpricing—investors’ biased expectation and leverage constraints and show that these two channels do not contradict each other but co-exist and affect prices of high-beta stocks simultaneously.

In the Fourth Chapter, I use classification-based machine-learning methods to decompose 32 anomaly payoffs into risk exposures and mispricing. The component driven by risk earns statistically insignificant returns, despite its efficacy in explaining the time-series variation in anomaly payoffs. The mispricing component is driven by biased cash flow expectations and earns significant returns that subsume anomaly payoffs. These findings indicate that the unconditional averages of anomaly returns can be fully explained by biased expectations, whereas risk exposures play an important role in explaining the time-series variation in anomaly returns.

This thesis contributes to both behavioural and asset pricing studies in several ways. First, findings in this thesis demonstrate that sentiment is an important conditional variable to the CAPM and incorporating it into the CAPM improves its ability to explain the cross-sectional variations in stock returns substantially. Moreover, by studying the sentiment-scaled CAPM I provide a behavioural insight to the value premium that the value effect arises from overpricing of growth stocks. Second, I consider overpricing of high-beta stocks as a mispricing-based explanation for a large number of characteristic-based anomalies; the attenuation to beta overpricing seems to drive the post-publication attenuation in anomaly payoffs. Finally, I develop a classification-based machine learning test to disentangle the risk- and mispricing- driven components in anomaly returns. I also introduce the real-time bias in analysts’ earning forecasts as proxies for investors’ biased expectations to examine the mispricing-based explanation for out-of-sample anomaly returns. My findings suggest that these anomalies are related more to investors’ incorrect expectations on future cash flows rather than a reflection of exposures to risk factors.