Stephen Roberts, Director and Professor of Machine Learning - Oxford-Man Institute of Quantitative Finance, University of Oxford at Co-Founder - Mind Foundry
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Stephen Roberts


Director and Professor of Machine Learning - Oxford-Man Institute of Quantitative Finance, University of Oxford
Co-Founder - Mind Foundry

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Conference Day One: 16 September 2019

Monday, September 16th, 2019


1:35 PM Enhancing time series momentum strategies using deep neural networks

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model simultaneously learns both trend and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised recurrent neural network improved traditional methods by more than two times in the absence of transactions costs, and continued outperforming even when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.