Multi-stage Hybrid Attentive Networks for Knowledge-Driven Stock Movement Prediction

Stock trend prediction is challenging due to complex stock behavior including high volatility. Leveraging an additional source of information, such as social media, can improve predictions, social media data is highly unstructured. To address these challenges, we propose a Multi-Stage TCN-LSTM Hybrid Attentive Network (MSHAN), which utilizes historical stock data with selected technical indicators and weighted social media information to predict daily stock movement. MSHAN uses a multi-stage architecture and hybrid neural networks to capture long-range sequential context dependencies of time-series data and unstructured textual information. We present results using extensive experiments on the actual market public dataset StockNet and demonstrate that MSHAN outperforms other baselines and can successfully predict the directional movement of daily stock prices. The ablation study results show the effectiveness of each component of MSHAN. The market simulation analysis suggests that the application of MSHAN can enhance trading profits.