Enhancing Time Series Momentum Strategies Using Deep Neural Networks

The Journal of Financial Data Science, Fall 2019, https://jfds.pm-research.com/content/1/4/19

Posted: 8 May 2019 Last revised: 24 May 2020

See all articles by Bryan Lim

Bryan Lim

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: April 9, 2019

Abstract

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 also simultaneously learns both trend estimation 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 LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming 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.

Keywords: Momentum Strategies, Trend Following, Machine Learning, Deep Neural Networks, Time Series Prediction

Suggested Citation

Lim, Bryan and Zohren, Stefan and Roberts, Stephen, Enhancing Time Series Momentum Strategies Using Deep Neural Networks (April 9, 2019). The Journal of Financial Data Science, Fall 2019, https://jfds.pm-research.com/content/1/4/19, Available at SSRN: https://ssrn.com/abstract=3369195 or http://dx.doi.org/10.2139/ssrn.3369195

Bryan Lim (Contact Author)

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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