Automated machine learning in quant finance: promise, practice and pitfalls
Automated machine learning in quant finance: promise, practice and pitfalls


Using the example of a practical use case in quantitative finance, this workshop will look at how recent developments in automated machine learning and interpretability can help quantitative and fundamental investors build, test and understand powerful AI models that support their investment process. Ayub and Peter will use data from J. P. Morgan and automated machine learning from DataRobot to illustrate an end-to-end iterative workflow to analyse the factors that drive stock performance following a cut in dividend expectations and build a set of actionable models that may be incorporated into an investment process. They will contrast this approach with "traditional" quantitative research and also identify and address some common misconceptions and mistakes made in AI-driven investing.


Peter leads DataRobot’s financial markets data science practice and works closely with fintech, banking, and asset management clients on numerous high-ROI use cases for DataRobot’s industry-leading automated machine learning platform. He has twenty-five years’ experience in senior quantitative research, portfolio management, trading, risk management and data science roles at investment banks and asset managers including Morgan Stanley, Warburg Pincus, Goldman Sachs, Credit Suisse, Lansdowne Partners and Invesco, as well as spending several years as a partner at a start-up global equities hedge fund. Peter has an M.Sc. in Data Science from City, University of London, an MBA from Cranfield University School of Management, and a B.Sc. in Accounting and Financial Analysis from the University of Warwick. His paper, “Hunting High and Low: Visualising Shifting Correlations in Financial Markets”, was published in the July 2018 issue of Computer Graphics Forum.

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