Working Paper * = Presented by co-author
Which Macroeconomic Risks Predict Which Stock Factor Portfolio Returns? (JMP) | Draft Current version: February 2025
Best Paper Award, TFA Journal of Financial Studies (2025, Declined) | Record
Job Seminar: Illinois Institute of Technology, National Sun-Yat-Sen University, Hong Kong Polytechnic University (Postdoc), University of Oxford (Postdoc, Declined)
Conference Presentation: Taiwan Finance Association (TFA, 2025)
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.
Moment Fusing: A Construction of Informed Asset Returns | Draft
with Ngoc-Khanh Tran and Guofu Zhou Current version: June 2025
Conference Presentation: Taiwan Economics Research (TER, 2024), Inter-Finance PhD Seminar (IFPHD, 2024)
Summary: TBA (Undergone major revisions. New draft coming soon.)
Time-Varying Anomaly Premia: Stable Fact or Disappearing Act? | Draft
with Niels Groenborg, Bradley Paye, and Allan Timmermann Current version: May 2024
Best Paper Award, FMA Europe Conference (2025) | Record
Conference Presentation: *FMA Europe Conference (2025), *16th Society for Financial Econometrics Annual Conference (SoFiE, 2024), *FMA Applied Finance Conference (2024), Financial Management Association (FMA, 2023), *5th International Workshop in Financial Econometrics (2023)
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.
Inflation Learning and Stock Return Dispersion | Draft Current version: July 2025
Under review, Review of Asset Pricing Studies
Conference Presentation: 4th Frontiers of Factor Investing Conference (FoFI, 2024), Southwestern Finance Association (SWFA, 2024), World Finance & Banking Symposium (WFBS, 2023)
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.
Work in Progress
Characteristics Fusing
with Ngoc-Khanh Tran
Anomaly Learning under Model Complexity Constraints
Discussions
FMA 2025 (Scheduled) -
Uncertainty and Market Efficiency: An Information Choice Perspective (by Harrison Ham, Zhongjin Lu, Wang Renxuan, Katherine Wood, and Biao Yang)
Market Fear, Investor Sentiment, and the Beta Premium (by Christopher Stivers and Naresh Bansal)
TFA 2025 - Stock Return Comovements and Investor Attention (by Bai-Sian Chen, Hong-Yi Chen, and Robin K. Chou) | Slides
SWFA 2024 -
See it, Say it, Shorted: Strategic Announcements in Short-Selling Campaigns (by Jane Chen) | Slides
Disaster Recovery, Jump Propagation and the Multi-Horizon UIP Pattern (by Bowen Du and Jianfeng Xu) | Slides
Out-of-Sample Performance of Factor Return Predictors (by Du Nguyen) | Slides
WFBS 2023 - Stock Price Crash Risk: A Systematic Review (by Rubini Sena and R. Madhumathi) | Slides
FMA 2023 - Resurrecting the Value Effect: The Role of Technology Stocks (by Ryan Lee) | Slides
Session Chair
SWFA 2024 -
F.3. Factors
H.1. Theoretical Asset Pricing