Random Forests for Feature Selection: Concepts and Applications in Asset Management

Abstract

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.