IAQF & Thalesians Seminar Series: Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book. A Seminar by Nicholas Westray

6:00 PM Seminar Begins

7:30 PM Reception

Hybrid Event:

Fordham University

McNally Amphitheater

140 West 62nd Street

New York, NY 10023

Free Registration!

For Virtual Attendees: Please select Virtual instead of member type upon registration.

Proof of Vaccination Upon Entry is Required for In-Person Attendees

Abstract:

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. We also provide some color on hyperparameter sensitivity for the problem of high frequency return forecasting as well as a discussion of the importance of Seq2Seq based architectures for prediction. This is based on joint work with Petter Kolm and Jeremy Turiel.

Bio:

Nick is currently head of Execution Research & co-head of Trading in the Multi-Asset Solutions group at AllianceBernstein, 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.