Research

I investigated the predictability of factor returns using common economic and financial variables. Unlike traditional market-timing models, I found that most of economic and financial predictors exhibit low signal-to-noise ratios for factor timing. Using recent advances in Bayesian econometrics with variational inference, I showed that stochastic volatility improves density forecasts, while time-varying parameter models tend to overfit. Forecast combination and economically motivated constraints (e.g., Sharpe ratio bounds) enhance out-of-sample performance in terms of certainty-equivalent returns but not Sharpe ratio. (Link)

I investigated the role of subjective belief on portfolio level. Different from the previous research on subjective belief, I found that the divergence between subjective belief and rational belief may be exaggerated because of the heterogeneity in slopes and the cross-sectional dependence component in the error structure. Using the latest development on panel data econometrics, I found that analysts may underestimate the one-year cash flow growth and overestimate the second-year cash flow growth, and this relationship will change significantly during the recession. This evidence supports the time-varying rationality of expectation and adaptive market hypothesis.

I re-evaluated factor timing models using the "Virtue of Complexity" framework, integrating Goyal-Welch and factor-specific predictors to enhance predictive accuracy. And my finding demonstrates that timing benefits are not confined to major Principal Components (PCs), validating the use of complex models to capturing idiosyncratic alpha.(Link)