NYU, Oxford, Queen Mary University Faculty Unveil Alternative Data Algorithms in Investment Data Sessions for Fintech Professionals

NYU, Oxford University, and QMUL faculty team up to reveal alternative data algorithms in Investment Data Sessions.

The limited enrollment workshops feature hands-on training for fintech analysts in a live teaching format. Attendees assemble custom workflows with proven code to create institutional investment products.

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A unique focus on the real-time format, advanced data analysis, and an interactive tutoring environment are core to the Investment Data Sessions.

Participants in the workshops explore datasets like consumer transactions, satellite imagery, and social media for investment decision-making. They will learn to solve everyday challenges in financial technology, including:

• Image detection
• Natural language processing (NLP)
• Ticker mapping
• Revenue modeling
• Transaction data de-biasing

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The organization is led by Saeed Amen (Cuemacro, Turnleaf Analytics, QMUL), Alexander Denev (TurnLeaf Analytics, University of Oxford), Gene Ekster (NYU, Alternative Data Group), and advised by Petter Kolm (NYU)

“This is the first time that a class exposes the intricate details of how institutional investors leverage non-traditional datasets to boost performance,” said Gene Ekster, who teaches Alternative Data at the Mathematics in Finance Masters Program, Courant Institute, New York University.

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