Fundamental Factor Models Using Machine Learning

Authors: Seisuke Sugitomo, Shotaro Minami

ABSTRACT
Fundamental factor models are one of the important methods for the quantitative active investors (Quants), so many investors and researchers use fundamental factor models in their work. But often we come up against the problem that highly effective factors do not aid in our portfolio performance. We think one of the reasons that why the traditional method is based on multiple linear regression. Therefore, in this paper, we tried to apply our machine learning methods to fundamental factor models as the return model. The results show that applying machine learning methods yields good portfolio performance and effectiveness more than the traditional methods.

Source:

Journal: Journal of Mathematical Finance
DOI: 10.4236/jmf.2018.81009 (PDF)
Paper Id: 82430 (metadata)

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