FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES BAYESIAN PORTFOLIO OPTIMIZATION WITH TIME-VARYING FACTOR MODELS By

We develop a modeling framework to simultaneously evaluate various types of predictability in stock returns, including stocks’ sensitivity (“betas”) to systematic risk factors, stocks’ abnormal returns unexplained by risk factors (“alphas”), and returns of risk factors in excess of the risk-free rate (“risk premia”). Both firm-level characteristics and macroeconomic variables are used to predict stocks’ time-varying alphas and betas, and macroeconomic variables are used to predict the risk premia. All of the models are specified in a Bayesian framework to account for estimation risk, and informative prior distributions on both stock returns and model parameters are adopted to reduce estimation error. To gauge the economic significance of the predictability, we apply the models to the U.S. stock market and construct optimal portfolios based on model predictions. Out-of-sample performance of the portfolios is evaluated to compare the models.