TY - JOUR
T1 - A machine learning based asset pricing factor model comparison on anomaly portfolios
AU - Fang, Ming
AU - Taylor, Stephen
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Anomaly portfolios
KW - Asset pricing
KW - Factor models
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85107068011&partnerID=8YFLogxK
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U2 - 10.1016/j.econlet.2021.109919
DO - 10.1016/j.econlet.2021.109919
M3 - Article
AN - SCOPUS:85107068011
SN - 0165-1765
VL - 204
JO - Economics Letters
JF - Economics Letters
M1 - 109919
ER -