Applications of Machine Learning in Finance: A Review of Futures Prediction, Asset Pricing, and Risk Management

Abstract

With the rapid development of fintech, machine learning has been widely applied in core financial fields, becoming an important technical tool to address practical challenges in financial research and practice. Traditional quantitative and qualitative methods show obvious limitations in handling complex futures price fluctuations, precise valuation of diversified enterprise assets, and dynamic early warning of multi-factor financial risks. Many international studies have explored machine learning applications in futures price forecasting, enterprise asset pricing, and risk management separately, yielding fruitful results. However, a lack of systematic review and integrated analysis across these three areas restricts a comprehensive understanding of machine learning’s overall role in corporate investment and management. This paper reviews recent international research on machine learning in futures price prediction, enterprise asset pricing, and corporate risk control. It summarizes the performance of different machine learning algorithms, compares their methodological features, data processing approaches, and practical effects. The findings indicate that machine learning effectively compensates for the defects of traditional methods, yet faces common challenges including weak model interpretability, high dependence on high-quality data, and low cross-market adaptability. This study fills the gap in systematic reviews of integrated machine learning applications in the above financial fields, improves the relevant research system, offers practical guidance for global investors, enterprises and financial institutions, and points out directions for future cross-field research.