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Stefan Zohren

Academic Faculty Member
Oxford-Man Institute of Quantitative Finance and University of Oxford

Stefan Zohren is the Academic Faculty Member of Oxford-Man Institute of Quantitative Finance and University of Oxford. His research interests cover statistical physics approaches to machine learning and optimisation, quantum computing as well as machine learning applied to finance, particularly market microstructure and high-frequency data.

He is involved in cutting-edge research projects primarily at the University of Oxford and in cooperation with industry partners such as Nokia Technologies, Lockheed Martin and Man Group. Stefan is also working on machine learning, in particular with applications in finance, both in academia as well as industry.

Stefan holds both a Masters and PhD in Mathematics and Theoretical Physics.

Check out the incredible speaker line-up to see who will be joining Stefan.

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Conference Day One: 16 September 2019

Monday, September 16th, 2019

4:00 PM Use of deep learning for modelling high-frequency market microstructure data

After a brief introduction to market microstructure and high-frequency financial data, we present recent work on a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The model, which utilises convolutional filters and LSTMs, is trained using full-resolution market data from the London Stock Exchange. Our model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments and outperforms existing methods. Interestingly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features.