MARCOS LOPEZ DE PRADO
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Portfolio Management Research (PMR) has named Marcos López de Prado ‘PMR Quant Researcher of the Year’ for 2019. PMR has instituted the annual Quant Researcher of the Year Award to recognize a researcher’s history of outstanding contributions to the field of quantitative portfolio theory. Marcos López de Prado is Professor of Practice at Cornell University’s School of Engineering. He has helped modernize finance for the past 20 years, by advancing the adoption of machine learning and supercomputing, and by developing statistical tests that identify false investment strategies (false positives). In recognition of this work, Marcos has received various scientific awards, including the National Award for Academic Excellence (1999) by the Kingdom of Spain, the PMR Quant Researcher of the Year Award (2019) by Portfolio Management Research, and the Buy-Side Quant of the Year Award (2021) by Risk. Marcos serves currently as global head of quantitative research and development at the Abu Dhabi Investment Authority, one of the largest sovereign wealth funds. Before that, he founded True Positive Technologies LP (TPT) after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. TPT has advised clients with a combined AUM in excess of $1 trillion. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3. Concurrently with the management of multibillion-dollar funds, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, is a founding co-editor of The Journal of Financial Data Science, has testified before the U.S. Congress on AI policy, and SSRN ranks him as the most-read author in economics. Marcos is the author of several popular graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020). Marcos earned a PhD in financial economics (2003), and a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid. He completed his post-doctoral research at Harvard University and Cornell University, where he is a faculty member. “For many years, Marcos has led the way towards the adoption of machine learning techniques in finance,” said Frank J. Fabozzi, Editor of JPM and JFDS. “His many publications have introduced innovative ways of thinking about financial problems and solving them in practice. Our Quant Researcher of the Year Award recognizes the totality of work by a researcher, and I think Marcos’ name was in everyone’s mind from the onset of the selection process.” |
Marcos López de Prado
Marcos López de Prado is Professor of Practice at Cornell University’s School of Engineering. He has helped modernize finance for the past 20 years, by advancing the adoption of machine learning and supercomputing, and by developing statistical tests that identify false investment strategies (false positives). In recognition of this work, Marcos has received various scientific awards, including the National Award for Academic Excellence (1999) by the Kingdom of Spain, the Quant of the Year Award (2019) by The Journal of Portfolio Management, and the Buy-Side Quant of the Year Award (2021) by Risk. Marcos serves currently as global head of quantitative research and development at the Abu Dhabi Investment Authority, one of the largest sovereign wealth funds. Before that, he founded True Positive Technologies LP (TPT) after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. TPT has advised clients with a combined AUM in excess of $1 trillion. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3. Concurrently with the management of multibillion-dollar funds, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, is a founding co-editor of The Journal of Financial Data Science, has testified before the U.S. Congress on AI policy, and SSRN ranks him as the most-read author in economics. Marcos is the author of several popular graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020). Marcos earned a PhD in financial economics (2003), and a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid. He completed his post-doctoral research at Harvard University and Cornell University, where he is a faculty member.
Crowdsourced Investment Research Through Tournaments
The Journal of Financial Data Science, Winter 2020
A Data Science Solution to the Multiple-Testing Crisis in Financial Research
The Journal of Financial Data Science, Winter 2019
The 10 Reasons Most Machine Learning Funds Fail
The Journal of Portfolio Management, Special Issue Dedicated to Stephen A. Ross 2018
Building Diversified Portfolios that Outperform Out of Sample
The Journal of Portfolio Management, Summer 2016
Discover his full portfolio of work published on Portfolio Management Research here: