The application of mathematical modeling for forecasting corporate bond spreads
Abstract
The application of mathematical modeling for forecasting corporate bond spreads
Incoming article date: 06.12.2024This study analyzes classical machine learning methods applied to the prediction of corporate bond yield spreads. Both linear methods, such as Principal Component Analysis and Partial Least Squares, and nonlinear methods, such as copula regression and adaptive regression splines, are examined. The study also explores the potential application of Random Forest models and classical neural networks. It includes a description of the data used for forecasting and presents some results of the empirical analysis. The findings have the potential to significantly impact practitioners and the scientific community striving to improve forecasting accuracy and optimize investment strategies.
Keywords: Machine Learning, Financial Engineering, Stock Market Modeling, Bond Market