webinar register page

Webinar banner
CAIA Association and State Street Associates Present: Making Sense of Machine Learning
Machine learning (ML) enables powerful algorithms to analyze financial data in new and exciting ways. But this excitement is often tempered by fear that investors don’t really understand why a model behaves the way it does. We need to move beyond this “black box” stigma. We propose a framework that demystifies the predictions from any ML algorithm. Our approach computes what we call a “fingerprint” for a given model’s linear, nonlinear, and interaction effects that drive its predictions — and ultimately its investment performance. In a real-world case study applied to currency return predictions, we find that popular ML models like neural network and random forest think in ways that do indeed make sense, and which we can begin to understand. These fingerprints empower investors to describe and probe the similarities and differences across ML models, and to extract genuine insight from machine-learned rules.

Aug 19, 2020 01:00 PM in Eastern Time (US and Canada)

* Required information
Loading

Speakers

Keith Black, Ph.D., CAIA, CFA, FDP
Managing Director, Content Strategy @CAIA
Keith Black has over thirty years of financial market experience, serving approximately half of that time as an academic and half as a trader and consultant to institutional investors. He currently serves as Managing Director of Content Strategy for the CAIA Association. During his most recent role at Ennis Knupp + Associates, Keith advised foundations, endowments and pension funds on their asset allocation and manager selection strategies in hedge funds, commodities, and managed futures. Prior experience includes commodities derivatives trading, stock options research and CBOE floor trading, and building quantitative stock selection models for mutual funds and hedge funds. Dr. Black previously served as an assistant professor and senior lecturer at the Illinois Institute of Technology.
David Turkington
Senior Managing Director, Head of Portfolio and Risk Management @State Street Associates
David is senior vice president and head of portfolio and risk research at State Street Associates. His team is responsible for research and advisory spanning asset allocation, risk management and quantitative investment strategy. David is a frequent presenter at industry conferences, has published research articles in a range of journals, and led the development of State Street’s systemic risk and turbulence indicators. He is also co-author of the book A Practitioner’s Guide to Asset Allocation. His research has received the 2013 Peter L. Bernstein Award, four Bernstein-Fabozzi/Jacobs-Levy Outstanding Article Awards, and the 2010 Graham and Dodd Scroll Award. David graduated summa cum laude from Tufts University with a BA in mathematics and quantitative economics, and he holds the CFA designation.
Yimou (Andrew) Li
Quantitative Research Analyst @State Street
Yimou (Andrew) Li is Assistant Vice President and Quantitative Researcher at State Street Associates. His research work is dedicated to leveraging machine learning to improve investment and portfolio solutions. Andrew received his Bachelor of Science in Applied Mathematics and Economics from Brown University and Master of Finance from MIT.