The Development of Machine Learning in Quantitative Finance

The rapid growth of financial data and artificial intelligence has increased the use of machine learning in quantitative finance. Machine learning provides new ways to identify complex patterns in financial data and support financial prediction and decision-making. This paper provides an introductory review of machine learning methods and their applications in quantitative finance. It first introduces the basic machine learning process and then focuses on three main applications: stock price prediction, portfolio management and credit risk assessment. Support Vector Machine, Neural Network and Random Forest are discussed for stock prediction; Genetic Algorithm, Reinforcement Learning and Deep Reinforcement Learning are reviewed for portfolio management; and Logistic Regression, Random Forest and XGBoost are examined for credit risk assessment. The paper also discusses three major challenges, including limited model interpretability, insufficient incorporation of external information and limited adaptability to changing market conditions. Corresponding future directions include explainable artificial intelligence, the integration of alternative and multimodal data, and adaptive learning methods. Overall, this review provides a structured overview of current machine learning applications in quantitative finance and may help readers better understand existing developments, major challenges and possible future directions in this field.