Academic Faculty Member
Oxford-Man Institute of Quantitative Finance and University of Oxford
Conference Day One: 16 September 2019
Monday, September 16th, 2019
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.