AI-Driven Financial Forecasting: Enhancing Predictive Accuracy in Volatile Markets

Abstract

The increasing volatility in financial markets demands robust forecasting models capable of adapting to dynamic conditions. Traditional statistical methods often fail to capture nonlinear patterns and rapid fluctuations inherent in modern financial systems. This paper explores the integration of Artificial Intelligence (AI) techniques, particularly machine learning and deep learning models, to enhance the predictive accuracy of financial forecasting. By leveraging advanced algorithms such as Long Short-Term Memory (LSTM) networks, Transformer models, and Reinforcement Learning strategies, the study demonstrates significant improvements in forecasting performance across multiple asset classes. Comparative evaluations reveal that AI-driven models outperform conventional approaches, especially during periods of heightened market turbulence. Additionally, the paper discusses the interpretability, reliability, and limitations of AI models, providing a comprehensive framework for future applications in financial forecasting. The findings underscore the critical role of AI in developing resilient, adaptive forecasting systems that better support investment decision-making in unpredictable market environments.