Research Review on the Application of Random Forest Algorithm in Decision Risk of Fund Investment

Abstract

With the in-depth integration of financial technology and the asset management industry, the volatility of sectoral markets in China’s fund industry has intensified, making investment decision risks increasingly complex and nonlinear. Traditional risk identification and measurement methods are insufficient to explain the coupling relationship between multi-dimensional market factors and investment risks. In contrast, the random forest algorithm, characterized by superior nonlinear fitting capability, strong anti-overfitting performance, and interpretable feature importance, has been widely adopted in quantitative fund investment and financial risk management. Adopting a literature review approach, this study systematically sorts out domestic and international research findings, summarizes typical application scenarios and technical paths of the random forest algorithm in fund decision risk management, and compares methodological differences between Chinese and foreign studies. The review reveals a prevalent research bias that prioritizes investment returns over decision risk evaluation, resulting in the absence of a systematic theoretical framework centered on fund investment decision risks. Combined with the operational characteristics of China’s fund market, this paper identifies existing research gaps and proposes future research directions. This study aims to improve the theoretical system of random forest applications in fund risk control and provide practical references for academic research and industrial risk management practices.