Enhancing Financial Forecasting with Large Language Models: Capabilities and Challenges

The growing integration of Artificial intelligence (AI) into financial markets offers unprecedented opportunities for advanced forecasting, automated decision-making, and strategic trading. However, despite notable progress, current systems often fail to balance predictive performance with critical requirements such as explainability, dynamic risk management, and regulatory alignment, especially in institutional contexts. This paper introduces the Explainable Deep Trading Approach (EDTA), a novel modular framework that combines Large Language Models (LLMs), multi-agent architectures, explainable AI (XAI) techniques, and adaptive risk management into a unified, transparent trading system. Unlike prior models focused solely on accuracy, EDTA embeds explainability and risk differentiation directly within its decision-making process, ensuring outputs that are both high-performing and regulatorily compliant under frameworks such as MiFID II. We conduct extensive empirical evaluations across diverse financial assets and temporal resolutions, demonstrating that EDTA achieves a Mean Directional Accuracy (MDA) of 72.4%, with an F1-score of 0.71, outperforming established benchmarks such as FinGPT, FinBERT, and DeepScalper. Furthermore, a domain expert panel rated EDTA’s generated explanations highly, with average scores of 4.6/5 for clarity, 4.4/5 for regulatory alignment, and 4.5/5 for practical relevance. These results highlight EDTA as a robust, next-generation framework that addresses both technological and regulatory demands, paving the way for more trustworthy, explainable, and effective AI-driven financial systems.