Nonlinear asset pricing in Chinese stock market: a deep learning approach

Date published

2023-03-24

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Publisher

Elsevier

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Article

ISSN

1057-5219

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Citation

Pan S, Long S(C), Wang Y, Xie Y. (2023) Nonlinear asset pricing in Chinese stock market: a deep learning approach. International Review of Financial Analysis, Volume 87, May 2023, Article number 102627

Abstract

The redesign of asset pricing models failed to integrate the frequent financial phenomenon that stock markets exhibit a non-linear long- and short-term memory structure. The difficulty lies in developing a nonlinear pricing structure capable of depicting the memory influence of the pricing variable. This paper presents a Long- and Short-Term Memory Neural Network Model (LSTM) to capture the non-linear pricing structure among five elements in the Chinese stock market, including market portfolio return, market capitalization, book-to-market ratio, earnings factor, and investment factor. The long-short-term memory structure implies that the autocorrelation function of the stock return series decays slowly and has a long-term characteristic. The LSTM model surpasses the standard Fama–French five-factor model in terms of out-of-sample goodness-of-fit and long-short strategy performance. The empirical findings indicate that the LSTM nonlinear model properly represents the nonlinear relationships between the five components.

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Github

Keywords

Nonlinear asset pricing, Long short-term memory neural network, Deep learning

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Attribution 4.0 International

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