Dr. Marcos López de Prado OMC is a computational scientist, hedge fund manager and professor known for pioneering machine learning and statistical inference methods that are now widely adopted at some of the largest investment corporations. He is currently Global Head of Quantitative R&D at the Abu Dhabi Investment Authority (ADIA), one of the largest sovereign wealth funds, and is a founding board member of ADIA Lab, Abu Dhabi's center for research in data and computational sciences. 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 more than 100 scientific journal articles with over 50 co-authors, including several Nobel laureates. These contributions have earned him prestigious scientific, state, and industry awards. In 2024, His Majesty King Felipe VI and the Government of Spain appointed him Knight Officer of the Royal Order of Civil Merit (OMC), "for distinguished services to science and the global investment industry."

Before ADIA, Prof. López de Prado founded True Positive Technologies LP (TPT), a firm that researches and develops investment IP. TPT has advised clients with a combined AUM in excess of $1 trillion, and has licensed and sold several patents to some of the largest investment funds in eight-figure dollar deals. Before TPT, he was a Partner and the first Head of Machine Learning at AQR Capital Management. As a Senior Managing Director at Guggenheim Partners, he also founded and led its Quantitative Investment Strategies business, where he managed $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3.

Since 2011, concurrently with the management of multibillion-dollar funds, Prof. López de Prado has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He is a founding co-editor of The Journal of Financial Data Science, and the author of several influential graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018), Machine Learning for Asset Managers (Cambridge University Press, 2020), and Causal Factor Investing (Cambridge University Press, 2023). He earned a PhD in financial econometrics (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 has been Professor of Practice at the College of Engineering since 2015. He has an Erdős #2 (via Neil Calkin) and an Einstein #4 according to the American Mathematical Society.

 

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.
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: