6:00 PM Seminar Begins
7:30 PM Reception
140 West 62nd Street
New York, NY 10023
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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.