Nonlinear asset pricing in Chinese stock market: A deep learning approach
نویسندگان
چکیده
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. difficulty lies in developing nonlinear structure capable depicting influence variable. This paper presents Long- Short-Term Memory Neural Network Model (LSTM) capture among five elements Chinese market, including market portfolio return, capitalisation, book-to-market ratio, earnings factor, investment factor. long–short-term implies autocorrelation function return series decays slowly has long-term characteristic. LSTM model surpasses standard Fama–French five-factor terms out-of-sample goodness-of-fit long–short strategy performance. empirical findings indicate properly represents relationships between components.
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ژورنال
عنوان ژورنال: International Review of Financial Analysis
سال: 2023
ISSN: ['1873-8079', '1057-5219']
DOI: https://doi.org/10.1016/j.irfa.2023.102627