Ben Wellington: ML for Finance and Storytelling through Data

The Gradient: Perspectives on AI

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3M ago 1:07:40

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In episode 115 of The Gradient Podcast, Daniel Bashir speaks to Ben Wellington.

Ben is the Deputy Head of Feature Forecasting at Two Sigma, a financial sciences company. Ben has been at Two Sigma for more than 15 years, and currently leads efforts focused on natural language processing and feature forecasting. He is also the author of data science blog I Quant NY, which has influenced local government policy, including changes in NYC street infrastructure and the design of NYC subway vending machines. Ben is a Visiting Assistant Professor in the Urban and Community Planning program at the Pratt Institute in Brooklyn where he teaches statistics using urban open data. He holds a Ph.D. in Computer Science from New York University.

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Outline:

* (00:00) Intro

* (01:30) Ben’s background

* (04:30) Why Ben was interested in NLP

* (05:48) Ben’s work on translational equivalence, dominant techniques

* (10:14) Scaling, large datasets at Two Sigma

* (12:50) Applying ML techniques to quantitative finance, features in financial ML systems

* (17:27) Baselines and time-dependence in constructing features, human knowledge

* (19:23) Black box models in finance

* (24:00) Two Sigma’s presence in the AI research community

* (26:55) Short- and long-term research initiatives at Two Sigma

* (30:42) How ML fits into Two Sigma’s investment strategy

* (34:05) Alpha and competition in investing

* (36:13) Temporality in data

* (40:38) Challenges for finance/AI and beating the market

* (44:36) Reproducibility

* (49:47) I Quant NY and storytelling with data

* (56:43) Descriptive statistics and stories

* (1:01:05) Benefits of simple methods

* (1:07:11) Outro

Links:

* Ben’s work on translational equivalence and scalable discriminative learning

* Two Sigma Insights

* Storytelling with data and I Quant NY

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