Enhancing Time Series Momentum Strategies Using Deep Neural Networks
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
Cite
@article{arxiv.1904.04912,
title = {Enhancing Time Series Momentum Strategies Using Deep Neural Networks},
author = {Bryan Lim and Stefan Zohren and Stephen Roberts},
journal= {arXiv preprint arXiv:1904.04912},
year = {2020}
}