Research

Abstract: This paper offers a comprehensive analysis of how macroeconomic risks predict stock factor portfolio returns. I use a dataset with hundreds of macro series and factor portfolio returns to identify which combinations show the greatest overlap between macro factors and stock factor returns. Through in-sample and out-of-sample analyses, I find that combining multiple macro series predicts stock factor portfolio returns more accurately than using individual series. The analyses reveal two key patterns. First, characteristics related to momentum and financial distress make stock factor portfolio returns more predictable by macro risks. Second, macro risks related to the manufacturing sector, housing market, and bond yields are more likely to predict these returns. These findings suggest that further exploration of the connections between firms' production activities and their exposure to systematic risks could explain momentum in stock returns.

Summary: TBA (Undergone major revisions. New draft coming soon.)

Abstract: We model the dynamics of expected returns for a large set of long-short portfolios based on characteristics from the return anomaly literature. Our models permit both cyclical forms of expected return variation and permanent decay effects. We document statistically and economically significant cyclical variation in anomaly portfolio expected returns. From an ex-post perspective, the majority of historical variation in expected anomaly portfolio returns is attributable to the cyclical component, rather than permanent decay effects. The most successful predictors appear to be the value spread, measures of anomaly portfolio momentum, and equity market sentiment. We emphasize the value of pooling information across anomalies via panel predictive regression models. Such models both clarify the evidence for predictability and generate out-of-sample forecast improvements relative to anomaly-specific forecasting approaches.

Abstract: This paper examines how heterogeneous learning speeds about inflation among investors contribute to cross-sectional variation in stock returns. I develop an asset pricing model in which investors update inflation expectations through Bayesian learning, which leads to persistent belief heterogeneity that impacts firm valuation. The model, supported by an empirical illustration, shows that return differentials widen when learning disparities increase. It further predicts that when slow learners dominate, pricing bias becomes more pronounced even as forecast errors remain limited. These results underscore the role of inflation expectation formation in shaping return dynamics and cross-sectional mispricing.