Research
Working Papers
Abstract
We show that the returns to many prominent cross-sectional anomalies are driven by a small number of extreme, positively skewed stock returns. Consistent with this, we propose a skewness-managed strategy that predicts ex-ante skewness using firm characteristics and modifies each anomaly portfolio by selecting high-skewness stocks in the long leg and low-skewness stocks in the short leg. Applied to 18 well-known anomalies, the strategy improves returns by an average of 5.45 percentage points, with gains concentrated in periods of economic and financial stress. Sharpe ratios also increase consistently, with an average gain of 0.12. Despite closely tracking the original factors, these skewness-managed portfolios deliver significant alphas even relative to models constructed from the same characteristics, suggesting that modern asset pricing models may systematically overlook higher-order moments.
Abstract
Anomaly strategies generate positive and significant CAPM alphas even after becoming public information. Common explanations emphasize non-market risks, trading costs, and investment frictions. This paper introduces a complementary channel: allocation uncertainty. Investors who learn about an anomaly remain uncertain about the optimal weight for combining it with the market portfolio, making its future factor regression alpha unattainable. We introduce the real time investor alpha, which measures the Sharpe ratio improvement from adding an anomaly to the market portfolio using weights estimated in real time. Empirically, anomalies retain positive factor regression alphas after publication, but their average real time investor alpha is close to zero. Investors can profitably combine multiple anomalies, but only with shrinkage. We show theoretically that this uncertainty-induced shrinkage makes investors trade less aggressively than full information investors, allowing CAPM alphas to survive in equilibrium. This model explains 15% to 30% of the cross-sectional variation in anomaly alphas.
Abstract
This paper provides comprehensive tests for the presence of demand-side risks in investment and profitability premiums. Consumption risk, intermediary risk, macroeconomic risk, and investor sentiment are all considered as potential demand-side drivers of return. Factors based on the consumption and macroeconomic risk models have only a limited ability to price portfolios sorted on size and investment and are unable to price portfolios based on size and profitability. Of the two intermediary models, only the broker-dealer leverage factor succeeds in pricing both premiums, but this performance disappears with an updated version of the underlying data for broker-dealer leverage. Sentiment is unable to price either set of portfolios. All in all, the demand models considered are unlikely to explain empirically the supply-based premiums.
Published and Forthcoming
Abstract
Our paper sheds light on the complexity of liquidity injection programs by showing unintended consequences that arise when firm heterogeneity is overlooked. Utilizing firm-level data from the Paycheck Protection Program, we find government lending effectively reduced closures, particularly if received during the first two weeks. However, we find significant heterogeneity in the effectiveness of funds, resulting from broad-brush eligibility guidelines and differences in how firms process information. The implementation relied on the banking system, which exacerbated the distributional effects by favoring firms with stronger customer capital. Our findings highlight the importance of thoughtful liquidity distribution design to maximize its benefits.