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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number109919
JournalEconomics Letters
Volume204
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Keywords

  • Anomaly portfolios
  • Asset pricing
  • Factor models
  • Machine learning

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