Raphael Douady

Quantitative Finance & Risk Expert

About

Raphael Douady is a French mathematician and economist.

He held the Frey Family Endowed Chair of Quantitative Finance at Stony Brook University (SUNY, 2015-18), and was, prior to that, Academic Director of the Laboratory of Excellence on Financial Regulation (Labex ReFi, a joint initiative of Paris 1-Sorbonne University, ESCP-Europe, CNAM and ENA, 2013-16). He is affiliated with Paris 1-Pantheon-Sorbonne University and the French National Centre for Scientific Research (CNRS). He co-founded fin-tech firms Riskdata (1999) and Datacore (2015) and now advises Matrics, a cloud-based system using AI for the buy-side industry. He has more than twenty years of experience in the banking industry and thirty-five years of research in pure and applied mathematics.

His current research focus is systemic risk and the anticipation of financial market crises, as well as the use of advanced data science and statistical techniques for long-term investment. His background in pure mathematics is in dynamical systems, chaos theory and symplectic geometry. He studied at Ecole Normale Supérieure in Paris and earned his PhD in mathematics in 1982 from the University of Paris 7. He is a board member of Friends of IHES, a charity supporting the French Institut des Hautes Etudes Scientifiques.

CV

Publications

Consulting Missions

Manager Selection

Performance vs. risk thorough analysis

Identification of risk sources and strategy confirmation

Adequacy of fees vs. performances and risks

Expert analysis of quantitative managers (including AI methods)

Expert analysis of simulated strategies and their expected performance in real situations

Fund ranking by category

Rewarding funds who excel at properly managing their downside risk

Not all funds are equal in terms of risk/reward. Rather than indiscriminately pressuring all funds to lower their fees, it would be healthier for the industry to accept rewarding funds who excel at properly managing their downside risk, while preserving attractive performance, and request other funds, those whose returns hide serious black swan risks under some potentially catastrophic scenarios, to significantly lower their fees.

We propose a thorough risk analysis of your portfolio. You will get a comprehensive list of the scenarios that may most impact your portfolio, with a clear explanation of its most vulnerable components and some proposed actions – rebalancing or hedging – to properly weather these scenarios.

Data Science

Education

On-site executive education on customer-tailored program, covering any area of interest, from basic math and statistics, to the most advanced machine learning and data science questions.

CFA Workshops

Workshops organized by the CFA Institute and local CFA Societies.

Consulting

Consulting to improve, thanks to advanced techniques, risk analysis, investment selection and portfolio construction.

Data will only “speak” what we tell them to “speak"

“Let the data speak and the machine learn from it” is the new credo of Data Science. Nothing could be more wrong, unless, like Mother Nature, we had one billion years to train our machines… Data will only “speak” what we tell them to “speak”, that is, how we have selected them. We believe that there is tremendous value in new data science techniques, leveraging on the incredible growth in computer and memory performances, but this value can only be extracted with a clever collaboration of man and machine, that is, between a slow, highly connected biological computer, trained for a billion years, and a fast, powerful, but weakly trained electronic computer.

Machine Learning can turn out very useful when human analysis reaches its limits, in very complex fields, such as risk analysis, manager selection or portfolio construction. It should only be used to enhance human expertise, and not to naively replace it. For instance: anticipating the behavior of complex systems under unexpected circumstances (e.g. stress testing), identifying, by a thorough long-term and worldwide statistical analysis of financial markets, the various regimes that may occur and the probability of falling into one or the other.