Financial time series forecasting is a long‐standing challenge due to the high complexity, randomness, and nonstationary of data. While predictions are typically made based on historical data with multiple features, existing models often fail to effectively integrate and utilize these diverse inputs, limiting forecasting accuracy. To address this, we propose MSA‐xLSTM, a multimodal stock price prediction model incorporating a sentiment attention mechanism. This model is designed to enhance the integration of sentiment features and technical indicators through a multiscale processing approach and sentiment attention module. Specifically, sentiment index sequences are extracted from textual sources such as financial news and stock comments. For technical indicators, both basic and derived metrics are analyzed for correlation with the prediction target, and only highly relevant ones are selected. We validate the effectiveness of each model component through comparative and ablation experiments. Furthermore, backtesting experiments demonstrate the model’s practical value in real‐world trading scenarios.
Lyu et al. (Thu,) studied this question.