Research

Abstract: This paper develops a framework to map macroeconomic risks to the space of stock factor portfolio returns. The framework identifies latent stock factors that are most relevant to latent macroeconomic risks, rather than relying on ad-hoc factors. I test this framework on the dataset of hundreds of macroeconomic series and factor portfolio returns. The analysis finds that the extracted macroeconomic factors show a more stable relationship with the stock factor portfolio returns, both in-sample and out-of-sample, than individual macroeconomic series. These results provide guidance for identifying the macroeconomic risks related to empirical asset pricing research.

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.