Paper: arXiv 2501.07135

Abstract

We present a systematic, trend-following strategy, applied to commodity futures markets, that combines univariate trend indicators with cross-sectional trend indicators that capture so-called {"\em momentum spillover"}, which can occur when there is a lead-lag relationship between the trending behaviour of different markets. Our strategy utilises two methods for detecting lead-lag relationships, with a method for computing {"\em network momentum"}, to produce a novel trend-following indicator. We use our new trend indicator to construct a portfolio whose performance we compare to a baseline model which uses only univariate indicators, and demonstrate statistically significant improvements in Sharpe ratio, skewness of returns, and downside performance, using synthetic bootstrapped data samples taken from time-series of actual prices.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics, including Lévy areas (stochastic calculus), dynamic time warping, graph theory (adjacency matrices), and convex optimization, indicating high complexity. Empirical rigor is solid with backtests on commodity futures, bootstrap sampling, and statistical significance testing (Sharpe ratios), though it lacks raw code or high-frequency data implementation.

Research Flowchart

  flowchart TD
  A["Research Goal"] -->|Identify| B["Input Data"]
  B -->|Compute| C["Univariate Trends"]
  B -->|Detect| D["Lead-Lag Relationships"]
  C & D -->|Compute| E["Network Momentum"]
  E -->|Construct| F["Novel Trend Indicator"]
  F -->|Form Portfolio| G["Performance Analysis"]
  G -->|Compare to| H["Baseline Model"]
  H -->|Outcome| I["Key Findings"]

  A["Research Goal<br>Enhance trend-following<br>using momentum spillover"]
  B["Input Data<br>Commodity futures price series"]
  C["Univariate Trends<br>Market-specific indicators"]
  D["Lead-Lag Detection<br>Two methods used"]
  E["Network Momentum<br>Cross-sectional computation"]
  F["Novel Trend Indicator<br>Combined approach"]
  G["Performance Analysis<br>Portfolio construction"]
  H["Baseline Model<br>Univariate only"]
  I["Key Findings<br>Improved Sharpe ratio<br>Better skewness<br>Superior downside performance"]

Cited References (57)

  1. The Stationary Bootstrap (1994) — link
  2. Dissecting Investment Strategies in the Cross Section and Time Series (2015) — link
  3. Building Cross-Sectional Systematic Strategies By Learning to Rank (2021) — link
  4. Design and analysis of momentum trading strategies (2023) — link
  5. Profitability of Momentum Strategies: An Evaluation of Alternative Explanations (1999)
  6. Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models (2023) — link
  7. Derivative dynamic time warping (2001)
  8. Lead-Lag Estimation by Means of the Dynamic Time Warping Technique (2001) — link
and 49 more
  1. shapeDTW: Shape Dynamic Time Warping (2018) — link
  2. Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies (2023)
  3. Lead-lag detection and network clustering for multivariate time series with an application to the US equity market (2022) — link
  4. A Primer on the Signature Method in Machine Learning (2016) — link
  5. Dynamic time warping (2007) — link
  6. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping (2013)
  7. CVXPY: A Python-Embedded Modeling Language for Convex Optimization (2016) — link
  8. How to learn a graph from smooth signals (2016)
  9. Individual comparisons by ranking methods (1992)
  10. Generalizing DTW to the multi-dimensional case requires an adaptive approach (2017)
  11. Extracting information from the signature of a financial data stream (2014) — link
  12. Continuous Futures Contracts Methodology for Backtesting (2019) — link
  13. Network Momentum across Asset Classes (2023) — link
  14. Enhancing Time Series Momentum Strategies Using Deep Neural Networks (2020) — link
  15. Momentum Profits, Factor Pricing, and Macroeconomic Risk (2008) — link
  16. A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets (1999)
  17. Industry information diffusion and the lead-lag effect in stock returns (2007)
  18. A model of investor sentiment (1998)
  19. An institutional theory of momentum and reversal (2013)
  20. Returns to buying winners and selling losers: Implications for stock market efficiency (1993)
  21. International momentum strategies (1998)
  22. Momentum, ownership structure, and financial crises: An analysis of Asian stock markets (2000)
  23. Profitability of momentum strategies: An evaluation of alternative explanations (2001)
  24. A century of evidence on trend-following investing (2017)
  25. Stock and bond market interaction: Does momentum spill over? (2005)
  26. When are Contrarian Profits Due to Stock Market Overreaction? (1990)
  27. Of Shepherds, Sheep, and the Cross-autocorrelations in Equity Returns (1995)
  28. Investment analysis and the adjustment of stock prices to common information (1993)
  29. The lead–lag relationship between the spot and futures markets in China (2017)
  30. Detecting leaders from correlated time series (2010)
  31. Econometric measures of connectedness and systemic risk in the finance and insurance sectors (2012)
  32. The econometrics of financial markets (1998)
  33. A new measure of rank correlation (1938)
  34. Measuring and testing dependence by correlation of distances (2007)
  35. Information-theoretic approach to lead-lag effect on financial markets (2014)
  36. Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering (2017)
  37. Do industries explain momentum? (1999)
  38. Risk-managed industry momentum and momentum crashes (2018)
  39. Shared analyst coverage: Unifying momentum spillover effects (2020)
  40. Spatio-temporal momentum: Jointly learning time-series and cross-sectional strategies — → 2302.10175
  41. Building cross-sectional systematic strategies by learning to rank (2020) — link
  42. Lead–lag relationships in foreign exchange markets (2020)
  43. Momentum information propagation through global supply chain networks (2021)
  44. Momentum spillover from stocks to corporate bonds (2017)
  45. Trend-Following and Spillover Effects (2019)
  46. Currency News and International Bond Markets (2021)
  47. Cross-asset time-series momentum: Crude oil volatility and global stock markets (2023)
  48. Kolmogorov–smirnov test: Overview (2014)
  49. Speech discrimination by dynamic programming (1968)

1 of 57 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.