But you don't need a PhD from MIT to understand what we do:
True Positive Technologies estimates fair values for securities with more scientific confidence than those generated by traditional methods.
access to differentiated unstructured data sources
superior big-data analytical tools
novel financial machine learning (ML) methods
vast super-computing power
solving some of the most intractable problems in Finance
and ML-informed execution capabilities
Click on the video below to see a fast-clock demonstration of our predictive capabilities:
Marcos López de Prado
co-founder and ceo
Dr. Marcos López de Prado is the chief executive officer of True Positive Technologies. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he developed high-capacity machine learning (ML) strategies that consistently delivered superior risk-adjusted returns.. After managing up to $13 billion in assets, Marcos acquired QIS and spun-out that business from Guggenheim in 2018.
Since 2010, Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML and supercomputing in the leading academic journals, and he holds multiple international patent applications on algorithmic trading.
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 teaches a Financial ML course at the School of Engineering. Marcos has an Erdős #2 and an Einstein #4 according to the American Mathematical Society.
A selection of recent SCIENTIFIC publications
- Advances in Financial Machine Learning, Wiley, 2018
- The Probability of Backtest Overfitting, Journal of Computational Finance, 2017
- Discerning Information from Trade Data, Journal of Financial Economics, 2016
- Solving the Optimal Trading Trajectory Trading Problem Using a Quantum Annealer, IEEE Journal of Selected Topics in Signal Processing, 2016
- Building Diversified Portfolios that Outperform Out-of-Sample, Journal of Portfolio Management, 2016
- Optimal Execution Horizon, Mathematical Finance, 2015
- Pseudo-Mathematics and Financial Charlatanism, Notices of the American Mathematical Society, 2014
- A Mixture of Gaussians Approach to Mathematical Portfolio Oversight: The EF3M Algorithm, Quantitative Finance, 2014
- VPIN and the Flash Crash: A rejoinder, Journal of Financial Markets, 2014
- High Frequency Trading: New Realities for Traders, Markets and Regulators, Risk Books, 2013
- The Sharpe Ratio Efficient Frontier, Journal of Risk, 2012
- Flow Toxicity and Liquidity in a High-Frequency World, Review of Financial Studies, 2012
For more journal articles, visit our research website at www.QuantResearch.org