MillGNN learns delayed (lead-lag) influences between time series and between groups of series at multiple grouping scales, reporting state-of-the-art forecast errors on 11 benchmarks.
Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum
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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.
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MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
MillGNN learns delayed (lead-lag) influences between time series and between groups of series at multiple grouping scales, reporting state-of-the-art forecast errors on 11 benchmarks.