A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection

The credit spread is a key indicator in bond investments, offering valuable insights for fixed-income investors to devise effective trading strategies. This study proposes a novel credit spread forecasting model leveraging ensemble learning techniques. To enhance predictive accuracy, a feature selection method based on mutual information is incorporated. Empirical results demonstrate that the proposed methodology delivers superior accuracy in credit spread predictions. Additionally, we present a forecast of future credit spread trends using current data, providing actionable insights for investment decisionmaking. Credit spread has long been a critical focus for investors, particularly in the context of investment-grade corporate bonds, which have garnered even greater attention.