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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Machine learning in investment management

4:50pm-5:30pm Thursday, September 6, 2018
Location: Yosemite A
Average rating: *****
(5.00, 1 rating)

What you'll learn

  • Learn the importance of domain expertise for investment management


Machine learning will provide a paradigm shift in investment management. There are immense opportunities to apply machine learning to investment management if you know where to look. Unlike many Silicon Valley challenges, it is not simply a matter of throwing capital and PhDs at the financial markets. Drawing on his experience with hundreds of funds in this space, Michael Weinberg explains why you must exploit domain expertise to achieve disruptive success and details the traps you may encounter and how to avoid them.

Photo of Michael Weinberg

Michael Weinberg


Michael Weinberg is the chief investment officer and a senior managing director of MOV37 and Protégé Partners. Michael has 25 years of experience investing directly at the security level and indirectly as an asset allocator in traditional and alternative assets. His portfolio management experience includes Soros Fund Management LLC, Credit Suisse First Boston, and Financial Risk Management (FRM). Previously, he was a research analyst at Dean Witter (now part of Morgan Stanley). Michael is a board member of AIMA and a member of the Economic Club of New York, serves on the management advisory council for the Michael Price Student Investment Fund, and was formerly the chair of value investing at CFANY, where he received multiple awards. He is a frequent keynote speaker at conferences and universities and a published author. He has been interviewed by top financial newspapers. Michael holds an MBA from Columbia Business School, where he is now an adjunct professor of finance and economics, and a BS in economics from New York University.

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09/07/2018 4:24am PDT

Hoping the materials will be posted here. I assumed they would so I didn’t take pictures constantly like many do.