A data-driven deep learning approach incorporating investor sentiment and government interventions to predict post-crash stock return in China's A-share market

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory interventions. Predicting individual stock returns after a crash is especially challenging in China's A-share market, which is characterized by high volatility and active government involvement. Although deep learning has advanced stock return forecasting, most studies have focused on general market conditions or relied solely on sentiments extracted from texts, leaving firm-level government intervention metrics largely unaddressed. To bridge this gap, we propose a novel deep learning framework that leverages historical post-crash data ("distant relative data") to forecast future stock returns. Unlike conventional methods that rely on recent pre-crash data—often overlooking government interventions—our approach leverages post-crash data, where investor sentiment and regulatory responses are already reflected, to model stable relationships between financial and momentum factors and subsequent returns, thereby implicitly integrating the effects of government interventions on investor behavior. We validate our framework using data from four distinct "thousand-stock limit-down" events in China's A-share market from 2018 to 2023. For the Fully Connected Neural Network (FCNN) model, training with close neighbor data yielded average F1-scores of 0.219 (2019), 0.106 (2020), and 0.282 (2022), whereas using distant relative data improved these to 0.571 (2019), 0.311 (2020), and 0.412 (2022). Notably, incorporating two distant relative datasets further boosted the FCNN F1-scores to 0.627 and 0.533 for 2020 and 2022, respectively. Additionally, Long Short-Term Memory (LSTM) networks consistently outperform FCNN models, underscoring their advantages in capturing temporal dependencies. Overall, our findings indicate that leveraging multiple historical crisis data sets significantly enhances post-crash stock return predictions. This data-driven approach, analogous to the stand-alone application of SMOTE for data balancing, offers a robust framework that can be integrated with other post-crisis models, thereby providing promising directions for future research and practical implementation.