Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)

<Previous Article In Volume
Next Article In Volume>

Stock Price Return Prediction Based on Multifactorial Machine Learning Approaches

Authors
Xingtong Wang1, Wen Wang2, *, Shuya Zhang3
1School of Accounting, Southwestern University of Finance and Economics, Chengdu, China
2School of Finance, Southwestern University of Finance and Economics, Chengdu, China
3School of Finance, Dongbei University of Finance and Economics, Dalian, China
*Corresponding author. Email: 41928018@smail.swufe.edu.cn
Corresponding Author
Wen Wang
Available Online 20 December 2022.
DOI
10.2991/978-94-6463-030-5_34How to use a DOI?
Keywords
Multifactorial Prediction; Linear Regression; Machine Learning Model
Abstract

Contemporarily, the combination of artificial intelligence and financial theory is a hot topic. In this paper, the multifactorial machine learning models for stock price prediction are implemented and compared after screening the effective factors. Specifically, four different linear models (OLS regression, Lasso regression, Ridge regression, Elastic Network regression) and nonlinear model XGBoost are applied. Based on the analysis, nonlinear model has better performance than different linear models, and the expected return rate constructed by this method has a higher correlation with the real rate of return. In terms of factor selection, this paper refers to the classification and construction of factors in relevant literature, including basic information factor, volume price factor, valuation factor, financial statement factor. In terms of data selection, the daily data from October 2018 to October 2021 among the four indexes with different industries, scales and market sentiment are selected to prevent extreme situations when using a single index. These results shed light on some extent that machine learning combined with quantitative investment has certain application value.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
20 December 2022
ISBN
10.2991/978-94-6463-030-5_34
ISSN
2589-4919
DOI
10.2991/978-94-6463-030-5_34How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xingtong Wang
AU  - Wen Wang
AU  - Shuya Zhang
PY  - 2022
DA  - 2022/12/20
TI  - Stock Price Return Prediction Based on Multifactorial Machine Learning Approaches
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 324
EP  - 333
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_34
DO  - 10.2991/978-94-6463-030-5_34
ID  - Wang2022
ER  -