Fundamental Quantitative investment research based on Machine learning
In recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative inve...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2023/19/shsconf_cdems2023_01019.pdf |
| _version_ | 1797799555230597120 |
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| author | Xu Jiao |
| author_facet | Xu Jiao |
| author_sort | Xu Jiao |
| collection | DOAJ |
| description | In recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative investment method has garnered significant attention. In this paper, eight machine learning algorithms, including Lasso regression, ridge regression, partial least squares regression, elastic network regression, decision tree, random forest, support vector machine and K-nearest neighbor method, are used to construct the stock return prediction model. The empirical results show that linear machine learning algorithm outperforms nonlinear machine learning algorithm. The annual return rate of CSI 300 index in the same term is 1.47%, while the investment strategy based on OLS model has an annualized return rate of 35.96%, and the maximum withdrawal rate is only 29.61%, showing its strong return capacity. In this paper, machine learning is introduced in the field of fundamental quantitative investment, which provides investment reference for all kinds of investors and is helpful for the country to promote quantitative investment. |
| first_indexed | 2024-03-13T04:21:40Z |
| format | Article |
| id | doaj.art-530697ad6b32494988e530b46c4c37b8 |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-03-13T04:21:40Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-530697ad6b32494988e530b46c4c37b82023-06-20T09:11:35ZengEDP SciencesSHS Web of Conferences2261-24242023-01-011700101910.1051/shsconf/202317001019shsconf_cdems2023_01019Fundamental Quantitative investment research based on Machine learningXu Jiao0Soochow University, Economics DepartmentIn recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative investment method has garnered significant attention. In this paper, eight machine learning algorithms, including Lasso regression, ridge regression, partial least squares regression, elastic network regression, decision tree, random forest, support vector machine and K-nearest neighbor method, are used to construct the stock return prediction model. The empirical results show that linear machine learning algorithm outperforms nonlinear machine learning algorithm. The annual return rate of CSI 300 index in the same term is 1.47%, while the investment strategy based on OLS model has an annualized return rate of 35.96%, and the maximum withdrawal rate is only 29.61%, showing its strong return capacity. In this paper, machine learning is introduced in the field of fundamental quantitative investment, which provides investment reference for all kinds of investors and is helpful for the country to promote quantitative investment.https://www.shs-conferences.org/articles/shsconf/pdf/2023/19/shsconf_cdems2023_01019.pdf |
| spellingShingle | Xu Jiao Fundamental Quantitative investment research based on Machine learning SHS Web of Conferences |
| title | Fundamental Quantitative investment research based on Machine learning |
| title_full | Fundamental Quantitative investment research based on Machine learning |
| title_fullStr | Fundamental Quantitative investment research based on Machine learning |
| title_full_unstemmed | Fundamental Quantitative investment research based on Machine learning |
| title_short | Fundamental Quantitative investment research based on Machine learning |
| title_sort | fundamental quantitative investment research based on machine learning |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2023/19/shsconf_cdems2023_01019.pdf |
| work_keys_str_mv | AT xujiao fundamentalquantitativeinvestmentresearchbasedonmachinelearning |