EconPapers    
Economics at your fingertips  
 

Empirical Asset Pricing via Machine Learning

Shihao Gu, Bryan Kelly and Dacheng Xiu

The Review of Financial Studies, 2020, vol. 33, issue 5, 2223-2273

Abstract: We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

JEL-codes: C52 C55 C58 G0 G1 G17 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (460)

Downloads: (external link)
http://hdl.handle.net/10.1093/rfs/hhaa009 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Empirical Asset Pricing via Machine Learning (2018) Downloads
Working Paper: Empirical Asset Pricing via Machine Learning (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:rfinst:v:33:y:2020:i:5:p:2223-2273.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

The Review of Financial Studies is currently edited by Itay Goldstein

More articles in The Review of Financial Studies from Society for Financial Studies Oxford University Press, Journals Department, 2001 Evans Road, Cary, NC 27513 USA.. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().

 
Page updated 2025-03-27
Handle: RePEc:oup:rfinst:v:33:y:2020:i:5:p:2223-2273.