Associate professor. Associate editor for Quantitative Finance, Journal of Economic Interaction and Coordination, Journal of Statistical Mechanics: theory and experiments.
- Agent behaviour (trader, investment fund, programmer). Recently, my interest has focused on agent-resolved data and aims at understanding what triggers activity and characterizing collective optimization in very heterogeneous populations.
- Statistics: I have introduced a new way to build nonparametric statistics for location and distribution equality tests from the number of records of sample cumulative sums, called r-statistics. One paper is about their power and another about their efficiency as an estimator of Sharpe ratios.
- Machine learning applied to financial data and fundamental economic data. E.g. Google Trends: see a comment and a paper.
CoordinatesRoom: SC.113, Bouyges building firstname.lastname@example.org Tel.: +33-(0) 1 75 31 67 55
See my CV.
Source codeThere is no reason to open-source mathematical computations and not computer code when submitting a paper. My repositories may be found on github.com. Some recent contributions:
- sharpeRratio (), an R package available on CRAN that implements my moment-free estimator of Sharpe ratios (see this preprint and the published paper) . A Python package of the same estimator, written by Amir Sani, is available on pypi.
- ReplacementBootstrap, an R package available on github.com repository that implements an max-entropy based bootstrap procedure, written in collaboration with Amir Sani. See this paper.
Teaching resourcesCurrent courses
- Physique des marchés (2012-), a mechanistic approach to financial market modelling and algorithmic trading, Applied Mathematics option for third-year students of CentraleSupélec and students following the Master in Statistics and Finance of Paris Saclay University.
- Financial Big Data at EPFL (Master in Financial Engineering) (2016-). The whole aim is to learn how to retrieve and wrangle annoyingly big data, use estimators adapted to financial data, find strategies with machine learning and apply these techniques to a meaningful financial problems.
- Big Data and Algorithmic Trading (2018-), MSt in Quantitative Finance, Solvay Business School, Brussels, Belgium. In short, big data and machine learning applied to trading.
- “Big data”, MSt in Quantitative Finance, Solvay Business School, Brussels, Belgium (2017)
- “Inefficient Markets” at HEC Lausanne between 2010 and 2015. It was about why financial markets cannot be efficient. The course plan was as follows: 1) financial markets deconstruction (behavioural finance), 2) reconstruction (agent-based modelling), 3) the art of speculative strategy backtesting. Lecture slides are available there.
InternshipsI will gladly supervise Master theses on a topic that is related to my interests (and, incidentally, abilities). Please check that yours are compatible with mine before contacting me.
Press Coverage (non-exhaustive list)
Minority Game: What stock investors can learn from the failure of Black Friday, Paul Ormerod, CITY A.M., 2 December 2015
- Tweaking taps for a constantly warm shower, New Scientist, 16 February 2008, p. 18
- The maths behind group showers Philip Ball, Nature (18/01/2008)
- Showered in surprises Michael Banks, Physics World, Volume 21 No 2, February 2008
- Openness makes software better sooner Nature Science update (2003)
- Open wide… Maths.org (2003)
- Le bazar et la cathédrale, ou comment l’Open Source élimine les bugs plus rapidement (2003)
- Forscher: Freierhältliche Quelltexte verbessern die Qualitaet von Computerprogrammen Wissenshaft.de (2003)
- Open Source liefert mehr Qualitaet silicon.de (2003)
- Debugging in OSS Always Faster slashdot.org (2003)
- Collective Effort Makes the Good Times Roll, Adrian Cho Science 2002 July 5; 297: 33
- Whole better than parts Nature Science update (2002)
- N/2 Wrongs Make a Right Physical Review Focus (2002)
- Defects combine to make perfect devices Physics Web (2002