NAFIS 2017: Industry’s Late ‘Thaw’ on Machine Learning Leaves Much to be Done

Joanne Faulkner reports on NAFIS panelists' views on machine learning and its current and future applications for the capital markets.

NAFIS 2017 Machine learning panel-stuart-tarmy-io-tahoe-elliot-noma-garrett-asset-management-li-yang-citi-neeraj-hegde-societe-generale

Neeraj Hegde, senior quantitative trading architect at Societe Generale, described the current climate as “the spring of machine learning after a long winter,” but warned that the required heavy investment in compute resources and sophisticated algorithms mean “we cannot keep up with the pace of innovation going on in this field…. The amount of effort that is being put into this particular field is phenomenal.”

Machine learning is transforming how firms investigate patterns in data, with even r

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