Machine learning techniques have gained enormously in popularity in recent years, but so far only to a very limited extent in fixed income research. Potential reasons for this are a lack of high quality corporate bond data and a lesser focus on corporate bond markets for quantitative investing until recently. We believe that corporate bonds lend themselves especially well for a machine learning approach because of all the additional risk factor associated with them.
The goal of this research project is to compare various machine learning techniques and evaluate their ability to predict corporate bond returns. Robeco’s Quant Fixed Income team has access to multiple historical databases of corporate bonds that enable back-testing and evaluating of strategies.
The project covers the entire quant model development cycle: conducting a literature study, analyzing the data, programming the back-tests, analyzing the results, discussing results with researchers and portfolio managers, writing a research report and giving a presentation. As with all Super Quant internships, the assignment will be supervised by an experienced empirical researcher of Robeco’s Quantitative Research department. Creative, analytic and programming skills are essential to successfully complete the project.
Houweling and Van Zundert (2017) “Factor investing in the corporate bond market”, Financial Analysts Journal 73(2).
Rasekhschaffe and Jones (2019) “Machine learning for stock selection”. Financial Analysts Journal 75(3), 70-88
Bali, Goyal, Huang, Jiang, and Wen (2020) “The cross-sectional pricing of corporate bonds using big data and machine learning”, SSRN working paper
Kaufmann, Messow and Vogt (2020). “Boosting momentum”, SSRN working paper