The 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado from QuantCon 2018

The 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado from QuantCon 2018

This talk, titled The 7 Reasons Most Machine Learning Funds Fail, looks at the particularly high rate of failure in financial machine learning. The few managers who succeed amass a large number of assets, deliver consistently exceptional performance to their investors. However, that is a rare outcome. This presentation will go over the 7 critical mistakes underlying most financial machine learning failures based off of Marcos López de Prado’s experiences and observations.

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Bio of the Speaker:
Dr. Marcos López de Prado is the chief executive officer at True Positive Technologies LP. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he applied cutting-edge machine learning to the development of high-capacity strategies that delivered superior risk-adjusted returns. After managing up to $13 billion in assets, López de Prado acquired QIS and successfully spun out that business in 2018.

López de Prado is a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). A top 10-most-read author in finance based on SSRN’s rankings, he has published dozens of scientific articles on machine learning and supercomputing and holds multiple international patent applications on algorithmic trading.

Marcos earned a Ph.D. in Financial Economics (2003), a Ph.D. in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain’s National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University.

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