BERYL ELITES KNOWLEDGE CENTER INTERVIEW - Poul Bendix Kristensen, CFA, CQF of New York Life Investments, discusses how machine learning can help global macro portfolio managers. In order to generate trading signals, one needs to analyze time series, cyclical indicators from different countries and different industries. Figuring out what is the signal in all that noise, what type of volatility we are going to see across asset classes, and at which point in the cycle we need to get more cautious for given asset class, is something that Machine Learning can really help analysts and create efficiencies and economies of scale. Surveys are showing that only 10% of quantitative researchers are using machine learning. That number will most likely grow rapidly, according to Poul. Furthermore, monitoring and screening process within quantitative research teams themselves is an obvious place that can be done much more efficiently and that can help portfolio managers and their research teams to focus their minds on what is really important, i.e. identifying investment opportunities and dangers. Also, in terms of client analytics, Machine Learning will allow PMs, consultants, financial advisors to deploy their time more efficiently and in turn it would allow them to serve more people. Hence, high quality services will most likely be available to a much broader audience besides private banking clients, and that would be an ideal scenario. This interview was conducted by Alva Lin of The Beryl Consulting Group.
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