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

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  • Fang, Ming
  • Taylor, Stephen

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.

Suggested Citation

  • Fang, Ming & Taylor, Stephen, 2021. "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001968
    DOI: 10.1016/j.econlet.2021.109919
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    References listed on IDEAS

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    More about this item

    Keywords

    Anomaly portfolios; Asset pricing; Factor models; Machine learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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