Tuesday, November 15th @ 11:00-12:30 PM (ZOOM)
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.