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

arXiv: https://arxiv.org/abs/2412.09769 · HTML: https://mlsanity.com/paper/2412.09769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 13 Dec 2024.

## Abstract (arXiv)

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.