RT Journal Article
SR Electronic
T1 A Deep Trend-Following Trading Strategy for Equity Markets
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2023.1.120
DO 10.3905/jfds.2023.1.120
A1 Patrik Eggebrecht
A1 Eva Lütkebohmert
YR 2023
UL https://pm-research.com/content/early/2023/03/09/jfds.2023.1.120.abstract
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.