Please join us for the next installment of the Bloomberg Quant (BBQ) Seminar Series. The seminar takes place every month and covers a wide range of topics in quantitative finance.
In this session, chaired by Bruno Dupire, Marcos López de Prado will present his current research, followed by several “lightning talks” of 5 minutes each in quick succession. This format gives the audience the opportunity to be exposed to a wider variety of topics.
Keynote: Marcos López de Prado, Professor of Practice at Cornell University, and CIO at True Positive Technologies
Machine Learning for Asset Managers
We introduce the nested clustered optimization algorithm (NCO), a method that tackles both sources of efficient frontier’s instability. Monte Carlo experiments demonstrate that NCO can reduce the estimation error by up to 90%, relative to traditional portfolio optimization methods (e.g., Black-Litterman).
A lightning talk is a very short presentation lasting only 5 minutes. Several ones will be delivered in a single session by different speakers in quick succession.
- Peter Carr (NYU Tandon) – A completely useless formula for pricing American puts
- Ioana Boier (Independent) – Cause, effect and imagination
- Harvey Stein (Bloomberg L.P.) – Covid-19 – The data abuse pandemic
- Alex Lipton (Sila, HUJI, MIT) – Exit strategies for Covid-19
- Jin Meyerson (American artist) – Essential asymmetrical connections
- David Mitchell (Bloomberg L.P.) – Index derivative relative value
- Antoine Savine (Danske Bank) – Differential machine learning
Marcos López de Prado
Marcos López de Prado is the CIO of True Positive Technologies (TPT), and professor of practice at Cornell University’s School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. Marcos launched TPT after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Concurrently with the management of investments, since 2011 he has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). Marcos is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020). Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain’s National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he is a faculty member. In 2019, Marcos received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.
When & Where
Thursday, May 28th, 2020
5:30 pm – 7:00 pm ET