Learning to Learn Financial Networks for Optimising Momentum Strategies
“Backtesting on 64 continuous future contracts demonstrates a significant improvement in portfolio profitability and risk control, with a Sharpe ratio of 1.74 across a 20-year period.”
StrategyMomentumportfolio optimization
TechniqueFeedforward network
ClaimSharpe ratioprofit
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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
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.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Extracting Alpha from Financial Analyst Networks by Gorduza et al. | 2024 | ||||
| Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies by Wood et al. | 2024 | ||||
| The Application of Dual-Denoised Momentum Factors in Portfolio Management: A Study of ChiNext Stocks for Retail Investors by Yi | 2025 | ||||
| A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting by Liu et al. | 2026 | ||||
| Has Machine Learning Defeated Trend Following Strategies in the Chinese Futures Market by Liang | 2024 |