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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.
Highlights
A typical tool of quantitative active operation (Quantz) has a multifactor model
The results show that applying machine learning methods yields good portfolio performance and effectiveness more than the traditional methods
The purpose of this paper is to explore the applicability of the machine learning method in the quantitative active operation, and to clarify the premise that it contributes to the development in the field of the future quantitative active operation
Summary
A typical tool of quantitative active operation (Quantz) has a multifactor model. This explains return on investment of stock with multiple factors. The method obtaining return on equity of individual company by giving macroeconomic variable a priori, and a method that derives factor by factor analysis from the past return on equity are classified as APT-type multifactor model. The methods of obtaining return on equity of individual companies by using brand. This paper is classified as multi-factor model of Fama-
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