Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions

2025-11-20

AI Summary

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.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested statistical advantages, thus improving results published in the literature. Our approach integrates Long Short-Term Memory (LSTM) networks with algorithms based on decision trees, such as Random Forest and Gradient Boosting. While the former analyze price patterns of financial assets, the latter are fed with economic data of companies. Numerical simulations of algorithmic trading with data from international companies and 10-weekday predictions confirm that an approach based on both fundamental and technical variables can outperform the usual approaches, which do not combine those two types of variables. In doing so, Random Forest turned out to be the best performer among the decision trees. We also discuss how the prediction performance of such a hybrid approach can be boosted by selecting the technical variables.

Problem

• Integrating LSTM networks with Random Forest for stock predictions
• Combining financial and microeconomic data to improve trading algorithms
• Enhancing prediction accuracy by selecting optimal technical variables

Innovation

• Integrates LSTM networks with Random Forest algorithms
• Combines technical price patterns and fundamental economic data
• Uses hybrid approach to outperform single-variable methods