Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum

ArXiv ID: 2501.07135 “View on arXiv”

Authors: Unknown

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.

Keywords: Trend-following Strategy, Momentum Spillover, Cross-sectional Momentum, Lead-lag Relationships, Commodity Futures, Commodities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: 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.

  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"]