Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Bryan Lim, Stefan Zohren and Stephen Roberts

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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.

Date: 2019-04, Revised 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Published in The Journal of Financial Data Science, Fall 2019

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1904.04912

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