Marcos López de Prado is a hedge fund manager, entrepreneur, inventor, and Cornell professor. Over the past 25 years, Marcos has helped modernize finance by pioneering machine learning and statistical inference methods that are now widely adopted at some of the largest investment corporations. His contributions have earned him several scientific, state and industry awards, including the National Award for Academic Excellence (1999) by the Kingdom of Spain, the Quant Researcher of the Year Award (2019) by Portfolio Management Research, the Buy-Side Quant of the Year Award (2021) by Risk.net, and the Bernstein Fabozzi / Jacobs Levy Award (2024) by The Journal of Portfolio Management. The Social Science Research Network (SSRN) ranks him among the 10 most-read authors in Economics, and he has testified before the U.S. Congress on AI policy. 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."

Marcos serves currently as global head of quantitative research and development 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. Before ADIA, he 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 8-figure dollar deals. Before TPT, Marcos 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.

Concurrently with the management of multibillion-dollar funds, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published approximately 100 scientific articles on financial machine learning and statistical inference in the leading academic journals, 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). Marcos 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 is a professor. Marcos has an Erdős #2 (via Neil Calkin) and an Einstein #4 according to the American Mathematical Society.

 

AWARDS

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.
Buy-Side Quant of the Year 2021, Risk.net.
Quant Researcher of the Year 2019, Portfolio Management Research.
National Award for Academic Excellence, Kingdom of Spain (Ministerial Decree, 12/07/99).
Extraordinary PhD Award, Universidad Complutense de Madrid.

 

RECENT INNOVATIONS

Inventor, U.S. Patent # 11,410,240: Tactical investment algorithms through monte carlo backtesting (Exp. 09/15/2040).
Mathematical proof of The False Strategy Theorem.
Inventor of widely used financial machine learning algorithms and statistical inference techniques, including:
    - HRP, NCO, A2A-MCOS (portfolio construction)
    - VPIN, OEH, BVC (high-frequency trading)
    - PSR, DSR, FST, SR Indifference (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 approx. 100 peer-reviewed publications in scientific journals, including:
    - Information Fusion (JCR IF: 14.7)
    - Journal of Financial Economics (JCR IF: 10.4)
    - Review of Financial Studies (JCR 5Y IF: 9.5)
    - IEEE Journal of Selected Topics in Signal Processing (JCR IF: 7.695)
    - Annals of Operations Research (JCR IF: 4.4)
    - Quantitative Finance (JCR 5Y IF: 2.2)
    - Journal of Financial Markets (JCR 5Y IF: 2.1)
    - Algorithms (JCR IF: 1.9)
    - Mathematical Finance (JCR IF: 1.6)
    - Critical Finance Review (JCR IF: 1.6)
    - Journal of Computational Finance (JCR 5Y IF: 1.417)
    - Journal of Portfolio Management (JCR IF: 1.400)
    - Journal of Risk (JCR IF: 0.915)
    - Notices of the American Mathematical Society (JCR IF: 0.912)
    - International Journal of Theoretical and Applied Finance (JCR IF: 0.5)
    - American Mathematical Monthly (JCR 5Y IF: 0.5)
    - Journal of Investment Strategies (JCR: 0.2)