Integrating LSTM with Fama-French six factor model for predicting portfolio returns: Evidence from Shenzhen stock market China

The study aimed to examine the effectiveness of long short-term memory (LSTM) model in predicting portfolio returns employing Fama and French's six-factor model. Monthly data were collected for A-share prices for firms listed on the Shenzhen Stock Exchange, China, for the period extended over 25 years (1997–2022). Portfolios are constructed on bivariate dependent sorting using the beta and downside beta. The random forest model was employed as a surrogate for LSTM to detect the nonlinear and threshold-based conditional relationship between risk factors and portfolio returns. The findings of the study reveal that LSTM has robust predictive ability and effectively captures the relationship between risk factors and portfolio returns. The relationship is more conspicuous in high downside risk portfolios than in the medium and high beta portfolio groups. Furthermore, portfolios within the high beta group, regardless of their downside beta levels, exhibit positive returns. The study suggests that investors need to consider high-risk portfolios for stable earnings and predictive returns.