Can network analysis discover useful momentum spillover across asset classes? In their August 2023 paper entitled "Network Momentum across Asset Classes", Xingyue (Stacy) Pu, Stephen Roberts, Xiaowen Dong and Stefan Zohren employ a graph machine learning model to discover cross-class momentum connections and devise a network momentum strategy across 64 series of commodities, equities, bonds and currencies future contracts. They train the model on an expanding window of at least 10 years of history for eight momentum features, including volatility-scaled returns and normalized moving average crossover divergences (MACD) over different lookback intervals. They they then apply multiple linear regressions over different lookback intervals (seeking to avoid reversals) to devise a network momentum strategy for out-of-sample testing. Every five years, they retrain the graph model. Using daily prices of the 64 futures contract series during 1990 through 2022, such that out-of-sample testing commences in 2000, they find that:
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