A New Stock Selection Model Based on Decision Tree C5.0 Algorithm

Qiansheng Zhang; Jingru Zhang; Zisheng Chen; Miao Zhang; Songying Li

doi:doi:10.11648/j.jim.20180704.12

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A New Stock Selection Model Based on Decision Tree C5.0 Algorithm

Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li

Published in Journal of Investment and Management (Volume 7, Issue 4)

Received: 10 August 2018 Accepted: 1 September 2018 Published: 21 September 2018

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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.

Published in	Journal of Investment and Management (Volume 7, Issue 4)
DOI	10.11648/j.jim.20180704.12
Page(s)	117-124
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Decision Tree C5.0, Factor Analysis, Stock Selection Model Introduction

References

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APA Style

Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li. (2018). A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. Journal of Investment and Management, 7(4), 117-124. https://doi.org/10.11648/j.jim.20180704.12

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ACS Style

Qiansheng Zhang; Jingru Zhang; Zisheng Chen; Miao Zhang; Songying Li. A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. J. Invest. Manag. 2018, 7(4), 117-124. doi: 10.11648/j.jim.20180704.12

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AMA Style

Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li. A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. J Invest Manag. 2018;7(4):117-124. doi: 10.11648/j.jim.20180704.12

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@article{10.11648/j.jim.20180704.12,
  author = {Qiansheng Zhang and Jingru Zhang and Zisheng Chen and Miao Zhang and Songying Li},
  title = {A New Stock Selection Model Based on Decision Tree C5.0 Algorithm},
  journal = {Journal of Investment and Management},
  volume = {7},
  number = {4},
  pages = {117-124},
  doi = {10.11648/j.jim.20180704.12},
  url = {https://doi.org/10.11648/j.jim.20180704.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jim.20180704.12},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - A New Stock Selection Model Based on Decision Tree C5.0 Algorithm
AU  - Qiansheng Zhang
AU  - Jingru Zhang
AU  - Zisheng Chen
AU  - Miao Zhang
AU  - Songying Li
Y1  - 2018/09/21
PY  - 2018
N1  - https://doi.org/10.11648/j.jim.20180704.12
DO  - 10.11648/j.jim.20180704.12
T2  - Journal of Investment and Management
JF  - Journal of Investment and Management
JO  - Journal of Investment and Management
SP  - 117
EP  - 124
PB  - Science Publishing Group
SN  - 2328-7721
UR  - https://doi.org/10.11648/j.jim.20180704.12
AB  - 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.
VL  - 7
IS  - 4
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li. (2018). A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. Journal of Investment and Management, 7(4), 117-124. https://doi.org/10.11648/j.jim.20180704.12

Copy | Download

ACS Style

Qiansheng Zhang; Jingru Zhang; Zisheng Chen; Miao Zhang; Songying Li. A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. J. Invest. Manag. 2018, 7(4), 117-124. doi: 10.11648/j.jim.20180704.12

Copy | Download

AMA Style

Qiansheng Zhang, Jingru Zhang, Zisheng Chen, Miao Zhang, Songying Li. A New Stock Selection Model Based on Decision Tree C5.0 Algorithm. J Invest Manag. 2018;7(4):117-124. doi: 10.11648/j.jim.20180704.12

Copy | Download

@article{10.11648/j.jim.20180704.12,
  author = {Qiansheng Zhang and Jingru Zhang and Zisheng Chen and Miao Zhang and Songying Li},
  title = {A New Stock Selection Model Based on Decision Tree C5.0 Algorithm},
  journal = {Journal of Investment and Management},
  volume = {7},
  number = {4},
  pages = {117-124},
  doi = {10.11648/j.jim.20180704.12},
  url = {https://doi.org/10.11648/j.jim.20180704.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jim.20180704.12},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - A New Stock Selection Model Based on Decision Tree C5.0 Algorithm
AU  - Qiansheng Zhang
AU  - Jingru Zhang
AU  - Zisheng Chen
AU  - Miao Zhang
AU  - Songying Li
Y1  - 2018/09/21
PY  - 2018
N1  - https://doi.org/10.11648/j.jim.20180704.12
DO  - 10.11648/j.jim.20180704.12
T2  - Journal of Investment and Management
JF  - Journal of Investment and Management
JO  - Journal of Investment and Management
SP  - 117
EP  - 124
PB  - Science Publishing Group
SN  - 2328-7721
UR  - https://doi.org/10.11648/j.jim.20180704.12
AB  - 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.
VL  - 7
IS  - 4
ER  -

Copy | Download