"Advances in Financial Machine Learning" Live Interview with Marcos López de Prado

Video Description

Join us for an insightful session featuring Dr. Marcos López de Prado, a renowned hedge fund manager, entrepreneur, and professor, as he discusses his groundbreaking book, Advances in Financial Machine Learning. With over 100,000 copies sold, this book has transformed the way quants approach finance. 🔍 Key Topics Covered: ✅ Why traditional finance methods fail in today's markets ✅ Seven common pitfalls in applying machine learning to finance ✅ The role of alternative data and specialized statistical techniques ✅ How hedge funds successfully extract alpha ✅ The impact of AI and large language models on algorithmic trading López de Prado also shares his views on industry trends, career advice for quants, and how small firms can still find opportunities despite the rise of large-scale financial labs. 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. Marcos was also the keynote speaker at QuantCon 2018. Learn more by subscribing to our YouTube channel to access all of our videos. As always, if there are any topics you would like us to focus on for future videos, please comment below or send us a quick note at [email protected]. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.