Factor Modeling: The Benefits of Disentangling Cross-Sectionally for Explaining Stock Returns

Abstract

More than three decades ago, Jacobs and Levy introduced the idea of disentangling stock returns across numerous factors. They identified the relationships between individual stock returns and firm characteristics using a cross-sectional analysis and examined the benefits of using the resulting time series of returns to the disentangled factors for return forecasting. Some years later, an alternative factor model proposed by Fama and French made use of time-series factors based on portfolio sorts (examples of these time-series factors include the return differences between small- and big-capitalization stocks and between high- and low-book-to-price stocks). Recently, Fama and French found that the cross-sectional approach using firm characteristics is better able to explain stock returns than the time-series approach based on portfolio sorts. This article compares models that use cross-sectional factors across firm characteristics with models that use time-series factors based on portfolio sorts and discusses the benefits and challenges of the cross-sectional approach for investment management.

Keywords: Factor models, Factor returns, Cross-sectional models, Disentangling, Firm characteristics, Time-series models, Portfolio sorts, Portfolio management, Stock returns

JEL Classification: G11, G12