A New Stock Selection Model Based on Decision Tree C5.0 Algorithm
Journal of Investment and Management
Volume 7, Issue 4, August 2018, Pages: 117-124
Received: Aug. 10, 2018; Accepted: Sep. 1, 2018; Published: Sep. 21, 2018
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Authors
Qiansheng Zhang, School of Finance, Guangdong University of Foreign Studies, Guangzhou, P.R. China
Jingru Zhang, School of Finance, Guangdong University of Foreign Studies, Guangzhou, P.R. China
Zisheng Chen, School of Finance, Guangdong University of Foreign Studies, Guangzhou, P.R. China
Miao Zhang, School of Finance, Guangdong University of Foreign Studies, Guangzhou, P.R. China
Songying Li, School of Finance, Guangdong University of Foreign Studies, Guangzhou, P.R. China
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Abstract
Due to the disordered characteristic and strong randomness of China's stock market, the typical data mining algorithms currently used to analyze and forecast the stock have imprecise prediction outcomes. In order to solve this problem, based on the industry rotation cycle theory, this paper constructs a new stock selection model combining Decision Tree C5.0 Algorithm and factor analysis. Industry rotation cycle theory aims to analyze the development trend of various industries to find promising industries as initial stock pool. According to this principle, this paper selects four industries and the A-share stocks of these industries are used as initial stock pool. This paper builds a stock index system consisting of six effective factors based on the factor analysis of stocks financial indicators and technical indicators. Then Decision Tree C5.0 Algorithm is presented to realize the prediction of stock returns and the classification of stocks. The empirical test of the proposed stock selection model, using the data from the second and the third quarter of 2017 in China A-share stock market, demonstrates that this model has significant difference in the classification accuracy between low-yielding stocks and high-yielding stocks in that case classification accuracy shows a trend opposite against stock return rate. In a conclusion, this model can effectively help investors to avoid risks and make rational investment but has little effect on obtaining excess return.
Keywords
Decision Tree C5.0, Factor Analysis, Stock Selection Model Introduction
To cite this article
Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li, A New Stock Selection Model Based on Decision Tree C5.0 Algorithm, Journal of Investment and Management. Vol. 7, No. 4, 2018, pp. 117-124. doi: 10.11648/j.jim.20180704.12
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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