Asset managers of all stripes are ramping up their data science capabilities. I will provide an overview of this trend and discuss the key success factors going forward. In addition, I will present a few case studies spanning different functions within an asset management organization. I will look at an investment research project that used a new “big dataset” as well as a machine algorithm to create a more powerful prediction model. I will also discuss a machine learning model we developed to understand our clients better. From this presentation, the audience will gain a better understanding of how data science is being used in our industry.
Andrew Y. Chin is the Chief Risk Officer and Head of Quantitative Research for AB. As the Chief Risk Officer, Chin oversees all aspects of risk management to ensure that the risks being taken are well understood and appropriately managed. In the Quantitative Research role, he is responsible for the firm’s data science strategy and for optimizing the quantitative research infrastructure, tools and resources across the firm’s investing platforms. He joined the firm in 1997 and held various quantitative research roles in New York and London. In 2004, Chin became a senior portfolio manager for Style Blend Equities. In 2005, he was named director of Quantitative Research for Value Equities. Prior to joining the firm, Chin was a project manager and business analyst in Global Investment Management at Bankers Trust from 1994 to 1997.
Chin teaches in the School of Operations Research and Information Engineering (Master of Financial Engineering Program) at Cornell University. He also leads teams of students on capstone projects utilizing quantitative and data science skills to address investment issues.
Chin earned a BA and an MBA from Cornell University.
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