Stock Return Predictability, Conditional Asset Pricing Models and Portfolio Selection

I examine an investor’s portfolio allocation problem across multiple risky assets in the presence of return predictability when, in addition to the predictability evidence, the investor uses conditional asset pricing models to guide him in the portfolio selection decision. I also explore how the uncertainty associated with the model dynamics affects the investor’s optimal portfolio. To analyze this, I introduce Bayesian techniques that have not been used before in the asset pricing literature. Using a market index and a small capitalization or a value portfolio, I find that the sample evidence on predictability plays a major role in the investor’s portfolio allocation decision. The optimal portfolio also depends on his beliefs about the extent to which this predictability can be attributed to time variation in risk premia and betas. Finally, I show that the portfolio allocation decision is also affected by the investor’s uncertainty about the beta risk dynamics.