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

Ming Fang, Stephen Taylor

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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
Article number109919
JournalEconomics Letters
Volume204
DOIs
StatePublished - Jul 2021

Keywords

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

Fingerprint

Dive into the research topics of 'A machine learning based asset pricing factor model comparison on anomaly portfolios'. Together they form a unique fingerprint.

Cite this