UC San Diego

How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation, Allocation to Industry Portfolios under Markov Switching Returns, addresses the effect of parameter estimation error on the relation between asset holdings and the investment horizon. This paper assumes that returns follow a regime switching process with unknown parameters. Parameter uncertainty is accounted for through a Gibbs sampling approach. After accounting for parameter estimation error, buy-and-hold investors are generally found to allocate less to stocks the longer the investment horizon. When the dividend yield and T-bill rates are included as predictor variables, the effect of these predictor variables is minimal, and the allocation to stocks is still smaller, the longer the investor's horizon. The second chapter of my dissertation, Portfolio Choice Implications of Parameter and Model Uncertainty in Factor Models, uses industry portfolios to examine the implications of incorporating uncertainty about a range of (conditionally) linear factor models. The paper specifically examines a CAPM, a linear factor model with different predictor variables (dividend yield, price to book ratio, price to earnings ratio, and price to sales ratio) and a time-varying CAPM specification. All approaches incorporate parameter uncertainty in a mean- variance framework. Time-varying CAPM specifications are intuitive in the sense that one cannot expect the environment for each industry to stay constant through time, and so the underlying parameters can be expected to be time-varying as well. Accounting for time- variation in market betas improves the portfolio performance as measured, e.g., by the Sharpe ratio compared to both an unconditional CAPM and a linear factor model with different predictor variables. The paper also looks at the implications for portfolio performance of utilizing a Black-Litterman approach versus a standard mean-variance approach in the asset allocation step. The former can be thought as a model averaging approach and thus can be expected to help dealing with model uncertainty besides the parameter estimation uncertainty. The third chapter of my dissertation, Style Investing with Uncertainty, develops methods to look at style investing. This paper analyzes the determinants that affect style investing, such as style momentum, and predictor variables such as different macro variables (e.g. yield spread, inflation, term structure, industrial production, etc.) and looks at how learning about these variables affects the predictability of returns. Uncertainty in this paper is incorporated using a time-varying parameter model. Returns on style portfolios such as value and size appear to be related to inflation and other macro variables