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Forecasting betas with random forests

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  • Emmanuel Alanis

Abstract

It is common to estimate equity betas for private firms or non-traded assets through a comparable company analysis, and we test if the Random Forest algorithm can provide superior forecasts. In out-of-sample tests from 1992 to 2018, we find that Random Forest forecasts produce substantially lower average errors and mean absolute errors every year.

Suggested Citation

  • Emmanuel Alanis, 2022. "Forecasting betas with random forests," Applied Economics Letters, Taylor & Francis Journals, vol. 29(12), pages 1134-1138, July.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:12:p:1134-1138
    DOI: 10.1080/13504851.2021.1912278
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    Cited by:

    1. Pedro Antonio Martín-Cervantes & María del Carmen Valls Martínez, 2023. "Unraveling the relationship between betas and ESG scores through the Random Forests methodology," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-29, September.

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