Do Common Factors Really Explain the Cross-Section of Stock Returns?

71 Pages Posted: 17 Jul 2020 Last revised: 14 Mar 2023

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

University of Florida - Department of Finance, Insurance and Real Estate

Nikolai L. Roussanov

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Date Written: July 1, 2020

Abstract

We document challenges to the notion of a trade-off between systematic risk and expected returns when analyzing the empirical ability of stock characteristics to predict excess returns. First, we measure individual stocks' exposures to all common latent factors using a novel high-dimensional method. These latent factors appear to earn negligible risk premia despite explaining virtually all of the common time-series variation in stock returns. Next, we use machine learning methods to construct out-of-sample forecasts of stock returns based on a wide range of characteristics. A zero-cost beta-neutral portfolio that exploits this predictability but hedges all undiversifiable risk delivers a Sharpe ratio above one with no correlation with any systematic factor, thus rejecting the central prediction of the arbitrage pricing theory.

Keywords: Asset Pricing, Arbitrage Pricing Theory, Factor Models, Machine Learning

JEL Classification: G12

Suggested Citation

Lopez-Lira, Alejandro and Roussanov, Nikolai L., Do Common Factors Really Explain the Cross-Section of Stock Returns? (July 1, 2020). Jacobs Levy Equity Management Center for Quantitative Financial Research Paper , Available at SSRN: https://ssrn.com/abstract=3628120 or http://dx.doi.org/10.2139/ssrn.3628120

Alejandro Lopez-Lira (Contact Author)

University of Florida - Department of Finance, Insurance and Real Estate ( email )

P.O. Box 117168
Gainesville, FL 32611
United States

HOME PAGE: http://alejandrolopezlira.site/

Nikolai L. Roussanov

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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