dc.contributor.author | Pan, Shuiyang | |
dc.contributor.author | Long (Cheng), Suwan | |
dc.date.accessioned | 2023-04-11T10:26:42Z | |
dc.date.available | 2023-04-11T10:26:42Z | |
dc.date.issued | 2023-03-24 | |
dc.identifier.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 | en_UK |
dc.identifier.issn | 1057-5219 | |
dc.identifier.uri | https://doi.org/10.1016/j.irfa.2023.102627 | |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19435 | |
dc.description.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. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | Elsevier | en_UK |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Nonlinear asset pricing | en_UK |
dc.subject | Long short-term memory neural network | en_UK |
dc.subject | Deep learning | en_UK |
dc.title | Nonlinear asset pricing in Chinese stock market: a deep learning approach | en_UK |
dc.type | Article | en_UK |
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