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. TOPICS:Factor-based models, statistical methods, security analysis and valuation, equity portfolio management Key Findings ▪ More than three decades ago, the authors pioneered a cross-sectional approach to factor modeling that disentangled the unique contributions of numerous factors to the pricing of individual stocks. ▪ A time-series approach using portfolio sorts has dominated the asset pricing literature, but cross-sectional analysis using firm characteristics has greater explanatory power for stock returns and helps practitioners address one of the most fundamental issues in investment management: understanding and predicting the returns of individual stocks. ▪ The authors revisit disentangling returns using a cross-sectional model, compare factor models using cross-sectional factors with those using time-series factors, and discuss the benefits and challenges of cross-sectional models.