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) from the Kingdom of Spain, the Quant Researcher of the Year Award (2019) from Portfolio Management Research, the Buy-Side Quant of the Year Award (2021) from Risk.net, and the Bernstein Fabozzi / Jacobs Levy Award (2024) from The Journal of Portfolio Management. The Social Science Research Network (SSRN) ranks him among the 10 most-read authors in Economics, and the U.S. Congress has invited him to testify 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 currently serves as 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. 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 has been a professor since 2015. 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)