Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions
This paper addresses the challenge of jointly modeling temporal technical indicators and static fundamental information—tasks poorly handled by single-model approaches. We propose a hybrid LSTM–Random Forest forecasting framework: an LSTM module captures deep sequential patterns from price time series, while a Random Forest integrates technical indicators (e.g., MACD, RSI) with macroeconomic and firm-level fundamentals; crucially, it incorporates a feature-importance-driven technical indicator selection mechanism. Evaluated on 10-day return prediction for international public companies, our method significantly outperforms baseline models—including standard LSTM, Random Forest, and XGBoost—in both predictive accuracy (p < 0.01) and out-of-sample Sharpe ratio. Results demonstrate that heterogeneous data fusion yields substantial, statistically robust gains in quantitative trading performance. The framework offers a novel, interpretable, and robust paradigm for intelligent trading powered by multi-source financial data.