Dr. Marcos López de Prado is a computational scientist and investment executive known for pioneering machine learning and statistical inference methods that are now widely adopted by some of the largest investment institutions. In 2024, His Majesty King Felipe VI and the Government of Spain appointed him Knight Officer of the Royal Order of Civil Merit, "for distinguished services to science and the global investment industry." He is currently Global Head of Quantitative R&D at the Abu Dhabi Investment Authority (ADIA), one of the largest sovereign wealth funds; a founding board member of ADIA Lab, Abu Dhabi's center for research in data and computational sciences; a Research Fellow at Lawrence Berkeley National Laboratory, part of the U.S. Department of Energy’s Office of Science; and Professor of Practice at Cornell University’s College of Engineering. The U.S. Congress has invited him to testify on AI policy, he is the named inventor on 15 patents, and the Social Science Research Network (SSRN) ranks him among the 10 most-read authors in Economics. He has published several influential graduate textbooks and more than 100 scientific journal articles with over 50 co-authors, including several Nobel laureates.

 

AWARDS

2024 - Knight Officer of the Royal Order of Civil Merit (OMC), Kingdom of Spain.
2024 - Bernstein Fabozzi / Jacobs Levy Award 2024, Outstanding Paper, The Journal of Portfolio Management.
2021 - Buy-Side Quant of the Year 2021, Risk.net.
2019 - Quant Researcher of the Year 2019, Portfolio Management Research.
2014 - Erdős #2 (via Neil Calkin) and an Einstein #4 according to the American Mathematical Society.
2013 - Extraordinary PhD Award, Universidad Complutense de Madrid (second doctorate. Resolution of October 31, 2013).
1999 - National Award for Academic Excellence, Kingdom of Spain (Ministerial Decree of July 12, 1999).
1998 - Extraordinary Graduation Award, Government of Galicia (Decree of December 29, 1998).

 

RECENT INNOVATIONS

Inventor, U.S. Patent # 11,410,240: Tactical investment algorithms through monte carlo backtesting (Exp. 09/15/2040).
Mathematical proofs:
    - The False Strategy Theorem (with Bailey and Borwein, 2014)
    - Closed-Form Solution to Optimal Mean-Reverting Strategies (with Lipton, 2020)
    - Closed-Form Asymptotic Distribution of the Sharpe Ratio under Non-Gaussian AR(1) (with Lipton and Zoonekynd, 2025).
    - Closed-Form Asymptotic Distribution of the Sharpe Ratio under GARCH (with Engle, Porcu and Zoonekynd, 2026).
    - FDR Non-Identifiability Theorems (with Fabozzi, 2026).
Inventor of widely used financial machine learning algorithms and statistical inference techniques, including:
    - HRP, NCO, A2A-MCOS (graph-based portfolio construction, robust portfolio optimization)
    - VPIN, OEH, BVC (high-frequency trading)
    - PSR, MinTRL, DSR, SR Indifference, SFDR (strategy selection)
    - Triple Barrier labels, Trend Scanning, Meta-labeling (feature engineering, corrective AI, bet sizing)
    - CPCV, OTRs (backtesting)

 

RECENT ACADEMIC CONTRIBUTIONS

Author, Causal Factor Investing (Cambridge University Press, 2023).
Author, Machine Learning for Asset Managers (Cambridge University Press, 2020). Published in English, Chinese, and Japanese.
Author, Advances in Financial Machine Learning (Wiley, 2018). Published in English, Chinese, Russian, Japanese, Korean, and Polish.
Author of over 100 peer-reviewed publications in scientific journals, including: