@article{Duan2024, 
author = {Yufei Duan and Xian-Ming Gu and Tingyu Lei},
title = {Application of machine learning in quantitative timing model based on factor stock selection},
year = {2024},
journal = {Electronic Research Archive},
volume = {32},
number = {1},
pages = {174-192},
keywords = {quantitative investment, multi-factor model, decision tree model, LSTM model},
url = {https://www.sciopen.com/article/10.3934/era.2024009},
doi = {10.3934/era.2024009},
abstract = {In this paper, we integrated machine learning into the field of quantitative investment and established a set of automatic stock selection and investment timing models. Based on the validity test of factors, a multi-factor stock selection model was established to select stocks with the highest investment value to create a stock pool. By comparing the cumulative returns and the overall market returns of different timing signals over the same time period, both the decision tree and the long short-term memory (LSTM) models had great results. Finally, empirical research was reported to show that it is a good combination to introduce machine learning algorithms into quantitative timing.}
}