MARCOS LOPEZ DE PRADO
![]() |
The Journal of Portfolio Management (JPM) has named Marcos López de Prado ‘Quant of the Year’ for 2019. JPM has instituted the annual Quant of the Year Award to recognize a researcher’s history of outstanding contributions to the field of quantitative portfolio theory. Dr. López de Prado has published an extensive body of academic work that has fostered the adoption of machine learning (ML) techniques in finance. His invention of the Hierarchical Risk Parity algorithm (first published in JPM) demonstrated that clustering algorithms can produce investment portfolios that outperform mean-variance-optimized portfolios out-of-sample. His innovative approaches addressed important challenges faced by financial researchers, including sampling of inhomogeneous data (the volume clock), labeling (triple-barrier method, meta-labeling), uniqueness-weighting of financial data, memory-preserving stationarity transformations (frac-diff), and purging and embargoing of cross-validation experiments. Dr. López de Prado has been a vocal advocate for the responsible use of ML in finance. His JPM article “The 10 Reasons Machine Learning Funds Fail” argues that, although ML tools are extremely powerful, it is very easy to misuse them. His book “Advances in Financial Machine Learning” proposed a new research paradigm, where ML is applied to the discovery of new economic theories, rather than black-box predictions. “For many years, Marcos has led the way towards the adoption of machine learning techniques in finance,” said Frank J. Fabozzi, Editor of JPM. “His many publications have introduced innovative ways of thinking about financial problems and solving them in practice. Our Quant 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
Marco López de Prado is Professor of Practice at Cornell University’s School of Engineering, and the founder of True Positive Technologies LP, a firm that specializes in the detection and avoidance of false discoveries in finance. Concurrently with the management of multi-billion-dollar funds, since 2011 Dr. López de Prado has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has testified before the U.S. Congress on AI policy, and is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018). He earned a PhD in financial economics (2003), a second PhD 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, 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: