Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market

Haixiang Yao, Shenghao Xia and Hao Liu

Pacific-Basin Finance Journal, 2022, vol. 76, issue C

Abstract: This paper proposes a long short-term memory (LSTM) neural network model to predict daily stock price movements based on asset pricing factors (i.e., the five factors proposed by Fama and French, and the short-term momentum factor). Based on three independent experiments, we systematically evaluate the explanatory power and the predictive power of the LSTM model by employing 3316 A-share listed companies in the Shanghai and Shenzhen stock exchanges from the in-sample period January 1, 2008 to December 31, 2019. Furthermore, we propose a four-step approach to dynamically update the underlying stocks in different portfolios based on the empirical findings. All portfolios are simulated using out-of-sample data (i.e., from January 1, 2020, to May 31, 2021) to avoid look-ahead bias. The trading results suggest that our dynamic investment strategies are superior to the benchmark index and are able to generate significant returns with relatively low risks.

Keywords: Long short-term memory (LSTM); Deep learning; Empirical asset pricing; Six-factor model; Quantitative investment (search for similar items in EconPapers)
JEL-codes: C45 C53 G12 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:76:y:2022:i:c:s0927538x22001810

DOI: 10.1016/j.pacfin.2022.101886

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