Enhancing Time Series Momentum Strategies Using Deep Neural Networks
Momentum and trend following across several asset classes, studied by Lim, Zohren and Roberts in 2020 with a recurrent neural network. The abstract mentions a Sharpe ratio, excess returns and transaction costs but not a benchmark, an out-of-sample test, multiple testing or the size of the universe.
“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.”
AssetMulti-assetfutures
StrategyMomentum
TechniqueRecurrent neural networkfeedforward network
ClaimSharpe ratioexcess return
Also stated: costs
Missing from abstract: benchmark, out-of-sample test, multiple testing, universe
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The paper's neighbourhoodMomentum and trend following by year
FamilyMomentum and trend following289Factor investing240Machine learning return prediction153News sentiment and text signals14818 more · 854
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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