Random Forests for Feature Selection: Concepts and Applications in Asset Management
介绍随机森林在资产管理中用于特征选择的核心概念，包括变量重要性评估方法，并回顾其在金融资产和市场分析中的有效性研究。
Machine learning models are widely used in asset management to support data-driven analysis. Even though advanced models sometimes exhibit promising performance across various tasks, interpretability is often an issue in finance, especially in asset management. Random forests have become a popular choice among practitioners because their tree-based structure is relatively intuitive and the ensemble of multiple trees can capture nonlinear relationships while avoiding overfitting. Another key strength of random forests is their built-in measure of variable importance that helps interpret model decisions and guides feature selection. In this article, we describe the core concepts of random forests, including methods for assessing variable importance, and review studies demonstrating their effectiveness in analyzing financial assets and markets.