A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

Abstract

Due to the computer's powerful data computing ability, more and more traditional investors pay attention to computer-based quantitative investment. Moreover, computer-based quantitative investment has gradually replaced some traditional investment methods which rely on subjective judgments. As a typical quantitative trading strategy, stock selection has attracted a lot of attention. And many researchers have put forward various methods and published a large number of research results. In particular, with the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to this field to obtain more stable and efficient stock selection models. Even though there is a growing interest in developing methods for stock selection, there is a lack of review papers that are solely focused on different types of methods for stock selection. Hence, our motivation in this paper is to provide a comprehensive literature review on different types of methods for stock selection in the field of quantitative investment. Firstly, we introduce the basic concept of stock selection. Secondly, according to the classification of traditional methods and machine learning methods, we introduce the widely used stock selection methods in detail. Then, we give a statistical analysis about the relevant literatures in this field. Finally, the stock selection methods are summarized. The main contribution of this paper is we analyse various quantitative analysis methods from the perspective of stock selection for the first time. And it has some guidance for researchers who engaged in quantitative trading or interested in quantitative investment, and they will benefit from it.