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.”