Reimagining Credit Investing: Leveraging Data & Machine Learning
Designed for both fundamental and quantitative analysts, you will learn how quantitative methods — such as bond bucketing, curve fitting, and machine learning techniques — can complement traditional credit analysis to estimate bond fair value, based on issuer fundamentals and market dynamics, to achieve true value investing.
We will also explore how to backtest these strategies, enabling credit investors to evaluate the robustness of quantamental approaches before applying them in live investment workflows.
Speakers
Dr. Songyun Duan
Head of BQuant Research (CTO Office)
Bloomberg
Dr. Songyun Duan is the Head of BQuant Research in the CTO Office at Bloomberg. He leads the research and development of quantitative solutions by applying advanced methodologies, including Machine Learning (ML) and NLP across asset classes from equity to fixed income and macro instruments. Before this, he spearheaded AI innovation initiatives across the firm, enhancing AI research and integration for the Bloomberg Terminal and enterprise products, such as pricing products, trading platforms, and NLP-based signal research. Prior to joining Bloomberg, Dr. Duan spent over six years at AQR Capital Management in a managerial role, where he directed quantitative research and platform development teams focused on systematic multi-asset investing strategies. His professional journey began at IBM’s T.J. Watson Research Center as a researcher focusing on NLP and Semantic Web. Dr. Duan earned his Ph.D. from Duke University, where his dissertation centered around ML and statistics.