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Marcos López de Prado is a hedge fund manager, entrepreneur, inventor, and professor. He has helped modernize finance for the past 25 years, by pioneering machine learning and statistical inference methods, and by implementing the Big Science paradigm of national laboratories at some of the largest investment corporations.
Marcos López de Prado is a visiting professor and a global head of quantitative research at Cornell University and Abu Dhabi Investment Authority. He is a leading expert in machine learning, supercomputing, and financial data science, with several awards, publications, and patents.
Learn how machine learning (ML) can transform finance and investing from Marcos López de Prado, a quantitative researcher and author. Contact him by e-mail or follow him on Twitter for updates.
Advances in Financial Machine Learning. M Lopez de Prado. Wiley 1, 1-400. , 2018. 669 *. 2018. The microstructure of the ‘Flash Crash’: Flow toxicity, liquidity crashes and the probability of informed trading. D Easley, M Lopez de Prado, M O'Hara. The Journal of Portfolio Management 37 (2), 118-128.
Lopez de Prado, Marcos. 2020. Codependence. Despite its popularity among economists, correlation has many known limitations in the contexts of financial studies In this seminar we will explore more modern measures of codependence, based on Information Theory, which overcome some of the limitations of correlations.
AuthorsYearTitleAbstractLopez de Prado, Marcos ; Zoonekynd, ...2024We show that: (1) factor strategies that ...2023I differentiate between type-A and type-B ...2021While investment firms have attracted ...2021This seminar explains how to detect false ...Advances in Financial Machine Learning. Marcos Lopez de Prado. John Wiley & Sons, Feb 21, 2018 - Business & Economics - 400 pages. Learn to understand and implement the latest machine...
About Marcos López de Prado. Marcos López de Prado is Professor of Practice at Cornell University’s School of Engineering. He has helped modernize finance for the past 20 years, by advancing the adoption of machine learning and supercomputing, and by developing statistical tests that identify false investment strategies (false positives).
