Style Investing with Uncertainty

This paper analyzes the predictability of different style portfolio returns. Styles, as used in this paper and Barberis and Shleifer (2003), can be defined as groups of securities with a common characteristic, such as value (Graham and Dodd (1934)) and size (Banz (1979)). I specifically look at the determinants of style investing, such as style momentum and predictor variables such as macroeconomic variables (e.g. yield spread, inflation, industrial production, etc.), and show how learning about these variables affects the predictability of different style portfolio returns compared to linear models. A time-varying parameter model and a Kalman filter are used to take into account the effect of learning in this paper. At the end, I find that returns on style portfolios such as value and size appear to be related to the yield spread and other macroeconomic variables. This paper also finds that time-varying parameter models provide better in-sample and out-of-sample predictions then simple benchmark constant parameter models.