Can machine learning methods predict beta?

Emmanuel Alanis, Vance Lesseig, Janet D. Payne and Margot Quijano

Applied Economics, 2025, vol. 57, issue 21, 2742-2756

Abstract: It is common to estimate equity betas for private firms or non-traded assets through a comparable company analysis (CCA) of peer firms. Previous literature has questioned the accuracy of those estimates. We test if Machine Learning (ML) algorithms can provide superior forecasts. In out-of-sample tests from 1990 to 2021, we find that ML predictions reduce the mean absolute error by over 42% relative to the CCA. The improved accuracy of ML is most pronounced for smaller, younger firms with different capital structure from their peer group, suggesting potential large improvements are possible by applying ML methods to private firm valuation.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2024.2331039 (text/html)
Access to full text is restricted to subscribers.

Related works:
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:taf:applec:v:57:y:2025:i:21:p:2742-2756

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2024.2331039

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().