TITLE:
Multi-Factor Stock Selection Model Based on Categorical Prediction Model
AUTHORS:
Yufan Hu
KEYWORDS:
Stock Selection Model, Multifactor Models, Cluster Analysis, Random Forest, Logistic Regression
JOURNAL NAME:
Open Access Library Journal,
Vol.10 No.7,
July
20,
2023
ABSTRACT: In order to reflect the concept of value investing, this paper extracts the indicators that reflect the fundamental information of listed companies such as profitability, solvency, operating capacity, growth capacity, and cash flow from the annual reports of listed companies in A-share market in 2021 as factor characteristics, establishes a multi-factor stock selection strategy based on cluster analysis model and classification prediction model respectively, and conducts an empirical study. The results show that after clustering based on the fundamental factor indicators, the investment portfolio with investment value can be classified and far outperform the performance of the SSE index in the same period, showing a high potential value of the investment. When performing classification prediction modeling, the test results on the test set show that it has a high winning rate when selecting stocks based on the prediction results.