Proceedings of the 2018 8th International Conference on Education and Management (ICEM 2018)

Stock Investment Selection Management Based on Bayesian Method

Authors
Zhixuan Gao
Corresponding Author
Zhixuan Gao
Available Online March 2019.
DOI
https://doi.org/10.2991/icem-18.2019.107How to use a DOI?
Keywords
Stock Investment Selection Management, Bayesian Model Average Method, Bayesian Naive Classification
Abstract
This paper aims to provide a stock selection management method based on bayesian in order to improve the investment management for investors. Firstly, the financial indicators of Shanghai A-shares were extracted, and those that had a significant impact on the stock increase were selected as the characteristic information of the stock by bayesian model average method. Secondly, the stock was classified into high yield stocks and other stocks by the stock characteristic information using naive Bayesian classification method. Finally, compare the increase of classified high yield stocks with the counterpart of benchmark. The results show that the classified high-yield stock by naive bayesian classification rose higher, indicates that the method provides the investors opportunity for higher returns on the stock investment, which is a meaningful method to improve their investment management.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 8th International Conference on Education and Management (ICEM 2018)
Part of series
Advances in Economics, Business and Management Research
Publication Date
March 2019
ISBN
978-94-6252-684-6
ISSN
2352-5428
DOI
https://doi.org/10.2991/icem-18.2019.107How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhixuan Gao
PY  - 2019/03
DA  - 2019/03
TI  - Stock Investment Selection Management Based on Bayesian Method
BT  - 2018 8th International Conference on Education and Management (ICEM 2018)
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/icem-18.2019.107
DO  - https://doi.org/10.2991/icem-18.2019.107
ID  - Gao2019/03
ER  -