Risk Adjusted Time Series Momentum
A 2014 paper by Dudler, Gmuer and Malamud on momentum and trend following across several asset classes: the abstract mentions a Sharpe ratio, excess returns, transaction costs and the size of the universe and leaves out a benchmark, an out-of-sample test and multiple testing.
“We test these strategies on a universe of 64 liquid futures contracts and show that RAMOM strategies outperform the time series momentum (TSMOM) strategies of Ooi, Moskowitz, and Pedersen (2012) for almost all combinations of holding and look-back periods.”
AssetMulti-assetfutures
StrategyMomentumvalue investing
GeographyEurope
ClaimSharpe ratioexcess return
Also stated: costs, universe of 64 futures contracts
Missing from abstract: benchmark, out-of-sample test, multiple testing
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
The paper's neighbourhoodMomentum and trend following by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| The Bottom-Up Beta of Momentum by Barroso | 2012 | ||||
| Under the Veil of Time Series Momentum: Return Decomposition Approach by Jin and Ren | 2026 | ||||
| Machine Learning and the Implementable Efficient Frontier by Jensen et al. | 2022 | ||||
| Multi-Asset A/A+ Momentum Fair Value Gap Strategy by Bindra | 2026 | ||||
| Has Machine Learning Defeated Trend Following Strategies in the Chinese Futures Market by Liang | 2024 |