Charles-Albert Lehalle

Head of Data Analytics Capital Fund Management

Currently Head of Data Analytics at Capital Fund Management (CFM, Paris) and visiting researcher at Imperial College (London), Charles-Albert Lehalle studied machine learning for stochastic control during his PhD 20 years ago. He started his career being in charge of AI projects at the Renault research center and moved to the financial industry with the emergence of automated trading in 2005.

He became an expert in market microstructure and has been appointed Global Head of Quantitative Research at Crédit Agricole Cheuvreux, and Head of Quantitative Research on Market Microstructure in the Equity Brokerage and Derivative Department of Crédit Agricole Corporate Investment Bank after the crisis. He provided research and expertise on these topics to investors and intermediaries, and is often heard by regulators and policy-makers like the European Commission, the French Senate, the UK Foresight Committee, etc. He chairs the Index Advisory Group of Euronext, is a member of the Scientific Committee of the French regulator (AMF), and has been part of the Consultative Workgroup on Financial Innovation of the European Authority (ESMA). Moreover, Charles-Albert received the 2016 Best Paper Award in Finance from Europlace Institute for Finance (EIF) and published more than fifty academic papers and book chapters. He co-authored the book "Market Microstructure in Practice" (World Scientific Publisher, 2nd ed 2018), analyzing the main features of modern markets. He is chairing the “Finance and Insurance Reloaded” transverse research program of the Louis Bachelier Institute; this program explores the influence of new technologies (from blockchain to artificial intelligence) on our industries.

Conference Day One: 16 September 2019

Monday, September 16th, 2019

2:15 PM Machine learning to monitor hundreds of algorithms on a trading floor

With the rise of automation, being able to give back control to humans at the best moment with the adequate information is a challenge. I will present how machine learning can detect in real-time potential causes of bad performance of hundreds of trading algorithms, grouping  them in ‘meaningful clusters’ so that a trader can adjust their parameters if needed. It will be the occasion to provide guidelines on how to respond to new industrial needs due to having a lot of AI in production.

Check out the incredible speaker line-up to see who will be joining Charles-Albert.

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