Analysis of Influencing Factors of Bond Issuance Credit Spread Based on Stochastic Forest Regression Model

The analysis of the influencing factors of credit spread is critical in the credit of bond issuance, however it has an issue with erroneous performance positioning. The typical Ant colony algorithm is unable to address the inaccurate analysis and positioning issue in the credit of bond issuance, and the result is insufficient. As a result, a Random forest regression model-based analysis of influencing factors of bond issuance credit spread is provided, and the analysis of influencing factors of bond issuance credit spread is assessed. To begin, the decision tree theory is used to discover the influencing elements, and the indicators are split based on the analysis of the influencing factors of credit spread's needs to decrease interference factors in the analysis of the influencing factors of credit spread. The decision tree theory is then used to create a Random forest regression model analysis of the influencing factors of credit spread scheme, and the outcomes of the analysis of the influencing factors of credit spread are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Random forest regression model outperforms the standard Ant colony algorithm in terms of analysis of the influencing factors of credit spread accuracy and time of influencing variables.