Credit Factor Investing with Machine Learning techniques

Friday 08 July 2022


The most common models to assess asset returns are a linear combination of risk factors. We have employed tree-based machine learning algorithms to capture nonlinearities and detect interaction effects among risk factors in the EUR and USD credit space. We have built a nonlinear credit pricing model and compared it to our baseline linear credit pricing model using error metrics on training and testing sets and during different periods. In-sample error metrics revealed the benefit of treebased regressions.

To find more, download the full paper (EN)

Working Paper - July 2022

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