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.

翻译：信用利差是债券投资的关键指标，为固定收益投资者制定有效交易策略提供了宝贵参考。本研究提出了一种利用集成学习技术的新型信用利差预测模型。为提高预测精度，模型引入了基于互信息的特征选择方法。实证结果表明，所提方法在信用利差预测中具有更优的准确性。此外，我们利用当前数据对未来信用利差趋势进行了预测，为投资决策提供了可操作的见解。