Mathematical Finance & Financial Data Science Seminar

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Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book

Speaker: Nicholas Westray, Alliance Bernstein - Multi-Asset Solutions & NYU-Courant Institute

Location: Online Zoom access provided to registrants

Date: Tuesday, October 26, 2021, 5:30 p.m.

Synopsis:

We describe how deep learning methods may be applied to forecast stock returns from high frequency order book states. I will review the literature in this area and describe a study where we evaluate return forecasts for several deep learning models for a large subset of symbols traded on the Nasdaq exchange. We investigate whether transformation of the order book states is necessary and we relate the performance of deep learning models for a symbol to its microstructural properties. Joint work with Petter Kolm (NYU), Jeremy Turiel (UCL)

Bio:

Nick is currently Head of Execution Research in the Multi-Asset Solutions group at alliance Bernstein, where he focusses on automating and improving execution across Equities, Futures and Fx.  He is also a visiting researcher in Financial Machine Learning at the Courant Institute of Mathematical Sciences at NYU working on problems at the intersection of optimal execution, market microstructure and deep learning. Previously he was a Senior Quant Researcher in the Equity Execution group at Citadel focusing on block trading and market Impact. Prior to that he was at Deutsche Bank involved in the Central Risk Book and Algorithmic Trading. He holds a PhD from Imperial College London and was a Postdoctoral Research fellow at Humboldt Universitaet zu Berlin.

Notes:

This event is free, but requires registration. Please click here to register