Stochastic Processes, Machine Learning, and Financial Market Analytics

The rapid advancement of financial markets has led to increasing uncertainty and complexity, necessitating more sophisticated analytical frameworks for risk management, asset pricing, and portfolio optimization. Traditional financial models, while powerful, often struggle to capture the intricate stochastic nature of market dynamics. To address these challenges, researchers have increasingly turned to interdisciplinary approaches that integrate stochastic processes with machine learning techniques. This article systematically reviews the theoretical evolution of stochastic differential equations in option pricing and credit risk modeling, with a focus on analyzing the portfolio optimization strategy combining stochastic optimal control and machine learning. It also explores the innovative application of Markov processes in volatility prediction. By integrating the literature from the past five years, this article reveals the significant potential of interdisciplinary methods in improving model robustness, computational efficiency, and prediction accuracy, providing theoretical support and practical guidance for the technical roadmap in the field of financial engineering.