Application of machine learning in quantitative timing model based on factor stock selection

Yufei Duan; Xian-Ming Gu; Tingyu Lei; Yufei Duan; Xian-Ming Gu; Tingyu Lei

doi:10.3934/era.2024009

Electronic Research Archive

2024, Volume 32, Issue 1: 174-192. doi: 10.3934/era.2024009

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Research article

Application of machine learning in quantitative timing model based on factor stock selection

1.
School of Mathematics, Southwestern University of Finance and Economics, Chengdu 611130, China
2.
School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China

Received: 02 August 2023 Revised: 08 November 2023 Accepted: 20 November 2023 Published: 18 December 2023

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.
- quantitative investment,
- multi-factor model,
- decision tree model,
- LSTM model
Citation: Yufei Duan, Xian-Ming Gu, Tingyu Lei. Application of machine learning in quantitative timing model based on factor stock selection[J]. Electronic Research Archive, 2024, 32(1): 174-192. doi: 10.3934/era.2024009

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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.

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