A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression
In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of fact...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/16/3502 |
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| author | Yumei Ren Guoqiang Tang Xin Li Xuchang Chen |
| author_facet | Yumei Ren Guoqiang Tang Xin Li Xuchang Chen |
| author_sort | Yumei Ren |
| collection | DOAJ |
| description | In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation. |
| first_indexed | 2024-03-10T23:45:13Z |
| format | Article |
| id | doaj.art-10a7fa9005034987ababd0ccfdc8cea6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:45:13Z |
| publishDate | 2023-08-01 |
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| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-10a7fa9005034987ababd0ccfdc8cea62023-11-19T02:02:56ZengMDPI AGMathematics2227-73902023-08-011116350210.3390/math11163502A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic RegressionYumei Ren0Guoqiang Tang1Xin Li2Xuchang Chen3College of Science, Guilin University of Technology, Guilin 541006, ChinaCollege of Science, Guilin University of Technology, Guilin 541006, ChinaCollege of Science, Guilin University of Technology, Guilin 541006, ChinaCollege of Science, Guilin University of Technology, Guilin 541006, ChinaIn the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation.https://www.mdpi.com/2227-7390/11/16/3502elastic netfalse discovery ratelogistic regressionknockoffmultifactor quantitative stock selection |
| spellingShingle | Yumei Ren Guoqiang Tang Xin Li Xuchang Chen A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression Mathematics elastic net false discovery rate logistic regression knockoff multifactor quantitative stock selection |
| title | A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression |
| title_full | A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression |
| title_fullStr | A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression |
| title_full_unstemmed | A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression |
| title_short | A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression |
| title_sort | study of multifactor quantitative stock selection strategies incorporating knockoff and elastic net logistic regression |
| topic | elastic net false discovery rate logistic regression knockoff multifactor quantitative stock selection |
| url | https://www.mdpi.com/2227-7390/11/16/3502 |
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