This research paper presents an innovative systematic trend-following strategy for commodity futures markets that significantly enhances traditional approaches by incorporating cross-sectional momentum indicators. The core innovation lies in the development of 'network momentum' - a novel indicator that captures momentum spillover effects between different markets through lead-lag relationships. The authors employ two distinct methods for detecting these lead-lag relationships, allowing the strategy to identify when trending behavior in one market precedes or influences similar behavior in other markets.

The methodology combines these cross-sectional indicators with traditional univariate trend indicators to create a comprehensive trend-following framework. Performance evaluation is conducted using synthetic bootstrapped data samples derived from actual commodity futures price time-series, ensuring robustness and practical relevance. The resulting portfolio demonstrates statistically significant improvements across multiple performance metrics compared to baseline models using only univariate indicators, including enhanced Sharpe ratios, more favorable return skewness, and superior downside protection. This research contributes to the quantitative finance literature by providing a systematic approach to capturing inter-market momentum relationships and offers practical implications for trend-following strategies in commodity futures trading.