Grinold, R.C. and Kahn, R.N. (1999) Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Selecting Superior Returns and Controlling Risk. McGraw-Hill Library of Investment and Finance.

has been cited by the following article:

• TITLE: Fundamental Factor Models Using Machine Learning

  AUTHORS: Seisuke Sugitomo, Shotaro Minami

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

  JOURNAL NAME: Journal of Mathematical Finance, Vol.8 No.1, February 12, 2018

  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.