A machine learning based asset pricing factor model comparison on anomaly portfolios

Ming Fang and Stephen Taylor

Economics Letters, 2021, vol. 204, issue C

Abstract: We frame asset pricing linear factor models in a machine learning context and consider related comparisons of their predictive performance against ordinary least squares linear regression over a dataset of anomaly portfolios. Specific regression models involved in the comparison include regularized linear, support vector machines, neural networks, and tree based models among others. Performance metrics are presented on a model, portfolio group, and sequential basis, and the strongest predictors are recommended as alternative techniques for the problem of excess return forecasting.

Keywords: Anomaly portfolios; Asset pricing; Factor models; Machine learning (search for similar items in EconPapers)
JEL-codes: C45 C53 C61 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001968

DOI: 10.1016/j.econlet.2021.109919

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