Bayesian-Optimized LSTM with Sentiment-Augmented Technical Indicators for Stock Return Prediction: Evidence from CSI 300

Abstract

Accurately predicting stock returns remains one of the core challenges in quantitative finance, exacerbated by nonlinear market dynamics, irrational investor behavior, and the stochasticity of financial time series. This paper proposes a novel hybrid framework—Bayesian Optimized Long Short-Term Memory Network Based on Sentiment Enhancement Technical Indicators (BOLSTM-SATI), which combines a Long Short-Term Memory (LSTM) neural network with classic technical analysis signals and multi-source investor sentiment indices from the CSI 300 index constituents. This study constructs a composite feature space including MACD, RSI, Bollinger Bands, volume balance indicator, and Average True Range (ATR). Investor sentiment is quantified through a Bayesian update mechanism that integrates news text sentiment scores obtained through FinBERT and social media sentiment indices from the Eastmoney Guba forum. To mitigate overfitting and improve generalization ability, we employ a Bayesian optimization method based on a tree-structured Parzen estimator (TPE) for LSTM hyperparameter search. Robustness to perturbations is verified through Gaussian noise injection experiments. Empirical evaluation using daily data from January 2015 to December 2022 shows that BOLSTM-SATI achieves a directional accuracy of 67.3%, a mean absolute error (MAE) of 0.0082, and a Sharpe ratio of 2.41 in backtesting simulations, outperforming baseline models including ARIMA, traditional LSTM, XGBoost, and Transformer variants. The results confirm that incorporating sentiment dynamics into technical feature learning can significantly improve prediction accuracy and the profitability of trading strategies.