Working Papers
[1] Decoding Anomalies through Alpha Dynamics
Presentations: FMA (2024), NFA (2024), EasternFA (2024), SWFA (2024), AFA Poster Session (2024), FMA Doctoral Consortium and Special PhD Paper Presentations (2023)
Working Papers
[1] Decoding Anomalies through Alpha Dynamics
Presentations: FMA (2024), NFA (2024), EasternFA (2024), SWFA (2024), AFA Poster Session (2024), FMA Doctoral Consortium and Special PhD Paper Presentations (2023)
Abstract: This paper studies how alphas of the characteristic-sorted stock portfolios evolve over the months after the sorting date, which I refer to as "alpha dynamics". I develop new tests to examine the alpha dynamics predicted by economic theories. The results provide new insights relevant to assessing whether anomalies (1) are attributable to collective ”data snooping” or are real, (2) can be attributed at least in part to mispricing, and (3) imply potential profits after considering trading costs. I study 205 published anomalies and find that t-tests of whether average alphas equal zero fail to detect many real anomalies, a problem that becomes more severe with higher t-statistic cutoffs. Further, the observed alpha dynamic pattern conforms to existing behavioral models rather than rational models for about thirty-two percent of characteristics, including investment, profitability, accruals, and momentum. Moreover, I show that after-cost profitability is significantly underestimated when alpha dynamics are not allowed for.
Abstract: We develop a new text-based measure of news complexity and show that the stock return predictability of shorting flows increases with news complexity. In fact, return predictability is lower when the news is simple than when there is no news. A portfolio strategy of buying lightly-shorted stocks and shorting heavily-shorted stocks earns an alpha that is 0.525% higher per month when past news complexity is high compared to when it is low. These findings are robust to controls for firm characteristics and alternative measures of news complexity. The results imply that short sellers' information advantage arises partly due to their skill in interpreting more complex public information releases.
Abstract: I study the relationship between investor disagreement and stock liquidity, and why this relationship exists. I find that disagreement is positively correlated to the adverse selection component of liquidity but negatively correlated to the inventory component, with the overall effect varying across stocks based on the relative strength of these correlations. The findings are robust to alternative measures of adverse selection and inventory components. To further understand the relationship between disagreement and adverse selection, I examine two sources of disagreement, information asymmetry (IA) and differential interpretation (IN). Using a novel empirical design, I show that IA mechanisms from Easley and O'Hara (1992) and Glosten and Milgrom (1985) have a greater impact on liquidity than the IN mechanism of Kyle and Wang (1997).
Abstract: This paper investigates the relation between short selling and corporate hedging. We examine how short sellers use hedging information to make trading decisions. We also investigate short selling around corporate hedging disclosures and analyze the predictability of these trades. The study focuses on the role of short sellers as informed traders and hypothesizes that firm hedging reduces their ability to exploit this information. Our empirical analysis reveals that short sellers tend to reduce their positions around corporate hedging disclosures when firms have high hedge ratios, reflecting a lower degree of return predictability. In contrast, short sellers’ positions are more predictive of future returns when firms exhibit lower hedge ratios. Additionally, we argue that the role of short sellers extends to influencing managerial decisions.
Abstract: Standard factor models and theories fail to explain the compensation discrepancy between persistent and transitory components of firm characteristics. We examine which drivers of investors' portfolio allocation choices contribute to the discrepancy by decomposing returns of characteristic-sorted portfolios into different demand-driven components: persistent demand shocks, which reflect idiosyncratic drivers such as emotion and liquidity needs, and demand for current and lagged characteristics, which reflect systematic drivers such as investment mandates and trading costs. We find that persistent demand shocks and demand for characteristics contribute significantly and oppositely to the discrepancy. Further, we provide new evidence that investors have a negative correlation between their demand for current and lagged characteristics.