| Title | Empirical Research on Multifactor Quantitative Stock Selection Strategy Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhang, Chengzhao, Tang, Huiyue |
| Conference Name | 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML) |
| Date Published | jul |
| Keywords | composability, Fitting, Forestry, gradient lifting regression, Input variables, linear regression, machine learning, machine learning algorithms, multifactor analysis, Pattern recognition, privacy, pubcrawl, quantitative investment, random forest regression, resilience, Resiliency, Support vector machines |
| Abstract | In this paper, stock selection strategy design based on machine learning and multi-factor analysis is a research hotspot in quantitative investment field. Four machine learning algorithms including support vector machine, gradient lifting regression, random forest and linear regression are used to predict the rise and fall of stocks by taking stock fundamentals as input variables. The portfolio strategy is constructed on this basis. Finally, the stock selection strategy is further optimized. The empirical results show that the multifactor quantitative stock selection strategy has a good stock selection effect, and yield performance under the support vector machine algorithm is the best. With the increase of the number of factors, there is an inverse relationship between the fitting degree and the yield under various algorithms. |
| DOI | 10.1109/PRML56267.2022.9882240 |
| Citation Key | zhang_empirical_2022 |
Empirical Research on Multifactor Quantitative Stock Selection Strategy Based on Machine Learning