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

Yu Shao; Jiawen Bai; Yingze Hou; Xia'an Zhou; Zhanhao Pan

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arXiv · 2412.09769

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

Yu Shao·Jiawen Bai·Yingze Hou·Xia'an Zhou·Zhanhao Pan

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Abstract

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 decision-making.

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Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou, Zhanhao Pan. 2024-12-13. A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection. https://arxiv.org/abs/2412.09769

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