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Machine Learning for Stock Selection




A Talk by Keywan Rasekhschaffe

Tuesday, December 10, 2019


5:45 PM Registration 6:00 PM Seminar Begins 7:30 PM Reception


Abstract Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools.

Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data.

We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.


Biography Keywan Rasekhschaffe, PhD, is a portfolio manager and senior quantitative strategist at Gresham Investment Management LLC where he focuses on Gresham’s systematic absolute return strategies. Prior to joining Gresham he oversaw quantitative research at System Two Advisors L.P. where he developed machine learning strategies for global stock selection. Previously he was a Chazen Visiting Scholar at Columbia Business School. He earned his Ph.D. at the University of Lugano, Swiss Finance Institute, and received his MBA from the University of Oxford. He holds a joint BSc in Politics and Economics from the University of Bristol. His research is focused on asset pricing anomalies in the macro and equities space and applied machine learning methods. His article Machine Learning for Stock Selection is forthcoming in the Financial Analysts Journal.


Acknowledgments Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.