Predicting Market Components Out of Sample: Asset Allocation Implications

The authors analyze out-of-sample return predictability for components of the aggregate market, focusing on the well-known Fama–French size/value-sorted portfolios. Employing a forecast combination approach based on a variety of economic variables and lagged component returns as predictors, they find significant evidence of out-of-sample return predictability for nearly all component portfolios. Moreover, return predictability is typically much stronger for small-cap/high book-to-market value stocks. The pattern of component return predictability is enhanced during business cycle recessions, linking component return predictability to the real economy. Considering various component-rotation investment strategies, the authors show that out-of-sample component return predictability can be exploited to substantially improve portfolio performance. TOPICS: Portfolio construction , exchanges/markets/clearinghouses , statistical methods