Abstract: Investors who use biased information from news media subsequently tend to make irrational decisions about acquiring firm-specific information compared to rational expectations. This model of information acquisition yields testable predictions that are verified by using a novel dataset. First, when sentiment in news articles, as a proxy for biased public information, is more optimistic, investors tend to acquire less earnings-relevant information before the earnings announcement and vice versa. Second, the return predictability from firm-specific news sentiment confirms that it contributes to variations in asset information risk due, in a biased belief equilibrium, to the proportion of informed investors deviating from rational expectations. Overall, these findings suggest that biased public information inherent in news sentiment serves to irrationalize investors’ acquisition of firm-specific information through a biased perception of uncertainties in the risky asset payoff.
with Ian Marsh
Abstract: This paper studies the impact of public mood, measured by Twitter messages, on the cross-section of U.S. stock returns. Our Twitter-based mood measure is free of endogeneity from financial market influence and distinct from the weather proxy or sentiment indices more commonly used in existing studies. We show that moody stocks that are more sensitive to public mood earn a higher expected excess return than less mood-sensitive sober stocks. Sorting stocks to construct the risk factor portfolio based on mood betas as sensitivity to mood risk, we are the first to quantify the risk premium (0.56 % per month) by holding stocks subject to mood risk. Our results are consistent with the theoretical arguments that investors mistakenly use mood as information that biases investors' decision making and trading behaviors, thereby inducing mispricing in asset valuation.
with Ian Marsh, Paolo Mazza and Mikael Petitjean
Abstract: We use high-frequency tick data to study stylized facts on the return and volatility dynamics of the nine most liquid cryptocurrencies. Factor structures exist in both returns and volatility, but the explanatory power from the common factor is much stronger for volatility. The factor structures do not relate strongly to fundamental economic factors, and Bitcoin – which we propose is a “crypto market factor” –has only weak explanatory power. We date the bubble in Bitcoin pricing allowing us to split the sample into pre-bubble, bubble and post-bubble periods. The importance of these different periods is clear, revealing shifting relationships between the nine cryptocurrencies and Bitcoin. Model-free realized cryptocurrency betas with Bitcoin increase during the bubble period and the explained fraction of cryptocurrency variance remains at an elevated level after the bubble burst.