Developing reliable and intuitive interpretation is essential for the application of machine learning to investing. This presentation discusses a framework for decomposing any machine learning model into linear, nonlinear, and interaction effects that drive both prediction and performance. With a case study of predicting US large cap stock returns, this presentation will show how the "Model Fingerprint" tool enables practitioners to summarize key characteristics, similarities and differences among different models, thereby enhancing their understanding of the market. Dr. Kathryn Wilkens, Founder of Pearl Quest LLC and current member of the FDP Institute curriculum team will explore this further with Andrew Li, Vice President, Quantitative Researcher, State Street Associates.
The Financial Data Professional Institute (FDPI), established by CAIA Association, has designed a self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science.
Presentation will last approximately 45 minutes followed by 15 minutes for Q&A. Session will be recorded, and the recording link will be sent to those who have registered for the webinar.