Network Momentum for Trend Following in Commodity Futures

Summary

The document describes a systematic trend-following strategy for commodity futures that combines conventional single-market trend indicators with cross-market signals. These cross-sectional signals aim to capture momentum spillover, where trends in one market may lead trends in another. The method identifies lead-lag relationships using two approaches, then combines them through a network momentum measure to create an enhanced trend indicator. That indicator is used to construct a portfolio and is compared with a baseline relying only on univariate indicators.

Using synthetic bootstrap samples drawn from actual price histories, the authors report statistically significant improvements in Sharpe ratio, return skewness, and downside performance. The excerpt does not specify the futures universe, sample period, transaction-cost assumptions, or exact tests, so the results cannot be fully evaluated from this description. The reported gains are based on resampled historical time series and do not guarantee similar results in live trading or other markets.

Key ideas

• The strategy combines single-market trend signals with cross-market trend information.
• It seeks to exploit lead-lag relationships and momentum spillover among commodity futures.
• Two methods are used to detect lead-lag links and calculate network momentum.
• The resulting portfolio is compared with a baseline using only univariate indicators.
• Bootstrapped price samples show reported improvements in Sharpe ratio, skewness, and downside performance.

Tags

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


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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