Raphael Douady has seen a lot of young quants. Currently a teacher-researcher in Paris (at the University of Paris I: Panthéon-Sorbonne), he previously spent four years as a professor of quantitative finance at Stony Brook University in New York and as a visiting professor at the University of New York. He has worked in quantitative finance on both sides of the Atlantic, and after a career spanning almost three decades, he has come to a notable conclusion: the mathematicians who come out of France are of a level far superior to almost all their international counterparts.
“The general level of education and mathematics in France is extremely high,” says Douady. “Stony Brook is one of the best universities in the United States and yet I taught my students there things that you will learn in high school in France. The only place where I have seen the same level of math education is in Russia.”
We already noticed the prevalence of Russian quants in the banking sector when war broke out in Ukraine earlier this year. Douady confirms that when you look at the population of quants on Wall Street, 33% tend to come from America, 33% from France, 15% from Russia, and the rest from places like China. The UK has “some good universities” in the form of Oxford, Cambridge, Imperial and Warwick, says Douady, but the general level of maths education in Britain just isn’t the same.
While the general level of mathematics in France is superior, Douady says there is one area in which France is lagging behind: statistics, and by implication data science. “France considers statistics as a branch of mathematics, when in fact it is a science in the same way as physics is a science that uses mathematics to work”, explains Douady. “There is a strong interaction between mathematics and physics, but physics is not a branch of mathematics.”
The weakness of French quants in statistics counts, but not that much. French mathematicians have such a broader base in mathematics that they can adapt very quickly, says Douady: “When you start getting interested in things like machine learning, French kids who have learned functional analysis and top-level geometry can acquire it at a speed that no American can copy.”
Douady himself specializes in machine learning and data science. In collaboration with his doctoral student, Thomas Barrau (currently a quantitative researcher at Axa IM in Hong Kong), he has just published a book on artificial intelligence for financial markets which proposes a new AI technique based on multiple non-univariate models. linear instead of traditional multivariate models. regressions. Known as polymodels, Douady says the technique is particularly suited to highly uncertain markets: in a traditional approach, quants would look for correlations between their investments or portfolio with a limited number of risk factors (typically five to 10). . As part of the Polymodel approach, they are able to take into account between 100 and 1,000 such factors: the system generates several models, which can be referenced depending on the situation. A layer of machine learning helps identify the most relevant signal.
Douady has been refining this approach for years. In the early 2000s, he says he was asked by a hedge fund asset allocator to find a way to predict fund risk from historical returns. He attended various meetings with the Frenchman who was “a pioneer in the hedge fund industry” and noticed that most of his time was spent discussing one-off events. “He spent five minutes talking about Sharpe ratios and volatility correlation, and 55 minutes talking about what happened on a specific date when his peers lost money. – What investments the funds liquidated first, etc.?” Douady says that classic risk models were too narrow to encompass the necessary breadth of information.
He says that the current markets are at a “critical moment” for quantitative risk managers, but that the quantitative risk profession is still fraught with contradictions: “Even if I tell you that there is a risk of 25% d a stock market crash as big as 2008, there’s still a 75% chance it won’t happen.”
Quants in France and around the world need to familiarize themselves with machine learning techniques, says Douady: “Mamachine learning is taking over all of the space.” Likewise, whether you want to work on the sell side (in a bank) or the buy side (in a fund), he says it will help to understand the kinds of complex technical math like the differential equations, differential geometry, algorithms, control theory and harmonic analysis in which French quants have such a good foundation.
Hedge funds are increasingly using quants to optimize trade execution and simply create markets, he says: “Great hedge funds have two main functions: one is to find the right bet to win money in the markets, the other is just to create liquidity and be a market maker. They will never give you the details, but I can absolutely tell you that they also make the market.
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