Advanced Computational Intelligence for Financial Market Forecasting and Decision-Making: A Synthesis of Deep Learning and Machine Learning Approaches

This paper synthesizes recent advancements in applying computational intelligence, particularly deep learning (DL) and machine learning (ML), to financial market forecasting and decision-making. The discussion reviews cutting-edge methodologies, including web-based investor sentiment indicators, Graph Neural Networks (GNNs) for cryptocurrency volatility prediction, Deep Reinforcement Learning (DRL) for autonomous trading, and ML models for fundamental stock analysis. The analysis highlights how these approaches address inherent market complexities such as non-stationarity, inter-market dependencies, and information asymmetry. Key themes identified include the evolving nature of information advantage, the critical role of data quality, and the increasing imperative for model interpretability and robustness. This synthesis provides a comprehensive overview of the transformative potential of artificial intelligence (AI) in finance, while also outlining persistent challenges and promising future research directions.

Keywords:

Financial Market Forecasting , Deep Learning , Machine Learning , Investor Sentiment , Graph Neural Networks , Reinforcement Learning , Fundamental Stock Analysis