Marcos Lopez de Prado named “2019 Quant of the Year” by The Journal of Portfolio Management

Marcos Lopez de Prado has been named “2019 Quant of the Year” by The Journal of Portfolio Management. Here are some excerpts from their announcement and more detailed press release:

The Journal of Portfolio Management (JPM) has named Marcos Lopez 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. It complements the Bernstein Fabozzi/Jacobs Levy Award, which JPM established in 1999 to acknowledge the most innovative research paper published in a given year by JPM.

Machine learning (ML) has a growing importance in modern society. Today, many areas of scientific research rely on the use of ML algorithms to build new theories. As it relates to finance, ML algorithms have demonstrated their value in multiple applications, including asset pricing, portfolio optimization, outlier detection, sentiment extraction, credit ratings, algorithmic execution, and bet sizing.

Dr. Lopez de Prado has published an extensive body of academic work that has fostered the adoption of machine learning 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. Lopez 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.”

In receiving this award, Dr. Lopez de Prado said: “JPM is my favorite journal for learning about academic ideas that work in finance. The roster of authors who have published at JPM is difficult to match, and a reference for every academic journal in our field. I’m deeply honored to receive this year’s award, and I hope that it draws attention to the many applications of financial machine learning.”

Dr. Lopez de Prado is a principal and head of ML at AQR Capital Management. In this role, he leads a team responsible for analyzing complex data for strategy development and a variety of applications across the firm. He is also an adjunct professor at Cornell University, where he teaches a graduate course in machine learning at the school of engineering.

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