How Predictable are Components of the Aggregate Market Portfolio?

Abstract:     
We analyze return predictability for components of the aggregate market, including portfolios sorted on industry, size, and book-to-market. Considering a variety of economic variables and lagged industry returns as predictors, both in-sample and out-of-sample tests highlight substantial differences in return predictability across components. Among industry portfolios, construction, textiles, apparel, furniture, printing, automobiles, and manufacturing exhibit the most predictability, while portfolios of small-cap and high book-to-market firms also display considerable predictability. Three key findings provide economic explanations for component predictability: (i) component predictability is markedly more evident during recessions, linking predictability to business-cycle fluctuations; (ii) based on a novel out-of-sample decomposition, time-varying macroeconomic risk premiums captured by the conditional CAPM and conditional Fama-French 3-factor model largely account for component predictability; (iii) industry concentration and market capitalization significantly explain differences in return predictability across industries, consistent with the information-flow frictions emphasized by Hong, Torous, and Valkanov (2007). We further show that predictability can be exploited to improve portfolio performance for component-rotation investment strategies.

Keywords: Return predictability, Industries, Size, Book-to-market, Macroeconomic fundamentals and risk, Information-flow frictions

JEL Classifications: C22, G11, G12

Working Paper Series