Inference and Prediction of Stock Returns using Multilevel Models

23 Pages Posted: 28 Jun 2019 Last revised: 3 Sep 2019

See all articles by Brice Green

Brice Green

University of Texas at Austin

Samuel Thomas

Capital Group

Date Written: August 31, 2019


Multilevel models are a generalized form of traditional linear regression models and have several benefits relative to traditional OLS regression including the regularization of parameter estimates, the ability to incorporate prior information, better out-of-sample forecasts, desirable inferential properties, and the ability to directly model the time-series/cross-sectional nature of financial security returns. We demonstrate that multilevel models generalize well-known asset pricing regression techniques like Fama-Macbeth and Fama-French regressions. They also have stronger explanatory power than traditional regression techniques, with a substantially lower out of sample mean squared error and a 5% to 10% higher out of sample R2 vs. the comparable model fit with OLS.

Keywords: Bayesian Statistics, Multilevel Models, Stock Returns, Factor Model

JEL Classification: G12, C11, C58

Suggested Citation

Green, Brice and Thomas, Samuel, Inference and Prediction of Stock Returns using Multilevel Models (August 31, 2019). Available at SSRN: or

Brice Green (Contact Author)

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

Samuel Thomas

Capital Group ( email )

333 S. Hope Street, 53rd Floor
Los Angeles, CA 90071
United States

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