PT  - JOURNAL ARTICLE
AU  - Patrik Eggebrecht
AU  - Eva Lütkebohmert
TI  - A Deep Trend-Following Trading Strategy for Equity Markets
AID  - 10.3905/jfds.2023.1.120
DP  - 2023 Mar 09
TA  - The Journal of Financial Data Science
PG  - jfds.2023.1.120
4099  - https://pm-research.com/content/early/2023/03/09/jfds.2023.1.120.short
4100  - https://pm-research.com/content/early/2023/03/09/jfds.2023.1.120.full
AB  - In this article, the authors present a new deep trend-following strategy that selectively buys constituents of the S&P 500 Index that are estimated to be upward trending. Therefore, they construct a binary momentum indicator based on a recursive algorithm and then train a convolutional neural network combined with a long short-term memory model to classify periods that are defined as upward trends. The strategy, which can be used as an alternative to traditional quantitative momentum ranking models, generates returns up to 27.3% per annum over the out-of-sample period from January 2010 to December 2019 and achieves a Sharpe ratio of 1.3 after accounting for transaction costs on daily data. The authors show that volatility scaling can further increase the risk–return profile and lower the maximum drawdown of the strategy.