Event detail

Name: 2nd Annual Machine Learning in Quant Finance

From: 20 Nov 2019

To: 22 Nov 2019

Address: London, United Kingdom

Organizer: Marcus Evans

Key speakers: Andrew Green, Head of CVA/FVA Quantitative Research, Lloyds Banking Group Alexei Kondratyev, Managing Director Financial Markets, Standard Chartered Bank Abhinandan Deb, Head of Global X-asset Quantitative Investment Strategies, Bank of America Merrill Lynch Alexander Giese, Head of Quantitative and Digital Development for Trading, Unicredit Bank AG Paul Ward, Head of European Quantitative Equity Research, Deutsche Bank Roberto Medina, Senior Machine Learning Engineer, Energias de Portugal Adhil Reghai, Head of Quantitative Research Equity Derivatives and Commodities, Natixis Mugad Oumgari, Managing Director and Global Head of Model Validation, Lloyds Banking

Web page: http://www.marcusevans-conferences-paneuropean.com/marcusevans-conferences-event-details.asp?EventID=25475#.XaBMsGaxU2w

Pricing: - variable


This marcus evans conference will offer case studies on how financial firms have overcome challenges to apply machine learning within the confines of the financial market. The biggest challenge addressed will be that of increasing data availability and how financial firms are doing this through construction, mining and sourcing. Drawing on the business needs that machine learning can solve in the financial market, the conference will showcase the very cutting edge of machine learning that is already yielding results. In addition to looking at the successes, cases will also cover the horror stories where quants can analyse what and why it all went wrong as well as how such cases can be avoided through more robust models as well as an enhanced risk management framework.

In today’s market, machine learning is everywhere and is making the most amount of noise under the scope of AI with its ability to provide enhanced risk analysis and process optimisation. However, the development of machine learning in financial firms is no easy task given data, risk management and regulatory hurdles. Another challenge is the sheer complexity of financial problems making it crucial to build an intuitive model that can supply for the business needs.


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