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

Document Type

Article

Publication Date

7-1-2021

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.

Identifier

85107068011 (Scopus)

Publication Title

Economics Letters

External Full Text Location

https://doi.org/10.1016/j.econlet.2021.109919

ISSN

01651765

Volume

204

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