Seminar 217, Risk Management: Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book

Seminar | November 15 | 11 a.m.-12:30 p.m. |  Zoom

Featured Speaker:  Petter Kolm, NYU

Sponsor:  Consortium for Data Analytics in Risk

We describe how deep learning methods can be applied to forecast stock returns from high frequency order book states. We 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 transformations of the order book states are necessary and relate the performance of deep learning models to the stocks' microstructural properties. In addition, we provide some color on hyperparameter sensitivity for the problem of high frequency return forecasting. This is joint work with Jeremy Turiel and Nicholas Westray.

Event Contact:  leenders@berkeley.edu

Access Coordinator:  Wouter Leenders,  leenders@berkeley.edu,  510-