Author(s)

Seisuke Sugitomo¹, Shotaro Minami²

¹Epic Partners Investment Co., Ltd., Tokyo, Japan.
²Asuka Asset Management Co., Ltd., Tokyo, Japan.

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.

Multi-Factor Model, Fundamental Factor Model, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Neural Network (NN), Artificial Intelligence Finance

Sugitomo, S. and Minami, S. (2018) Fundamental Factor Models Using Machine Learning. Journal of Mathematical Finance, 8, 111-118. doi: 10.4236/jmf.2018.81009.