Deep Learning Architectures for Financial Forecasting: Integrating Market Sentiment and Economic Indicators

Abstract

This article examines the application of advanced artificial intelligence techniques to enhance financial forecasting accuracy and improve investment decision-making processes. By integrating Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) with economic indicators and real-time market sentiment analysis, the article develops a comprehensive predictive framework that outperforms traditional forecasting methods. A multi-factor approach addresses the limitations of conventional models by capturing complex temporal dependencies and nonlinear relationships in financial data while incorporating market psychology. The experimental results demonstrate that the proposed deep learning architecture provides more reliable predictions across various market conditions and time horizons. This article has significant implications for portfolio managers, individual investors, and financial institutions seeking to leverage AI-driven analytics for strategic advantage in increasingly volatile markets. This article contributes to the growing body of literature on applied machine learning in finance while offering practical insights for implementation in real-world investment scenarios.