Momentum and network learning

The paper “Network Momentum across Asset Classes” introduces a multi-asset quantitative trading strategy based on network momentum—a trading signal derived from momentum spillover across different asset classes. Momentum spillover occurs when past performance in one asset predicts future returns in linked assets. While traditionally studied in pairwise connections (e.g., stock-bond pairs or supply chains), this paper expands the concept to a systemic network across four distinct asset classes.

The study analyzes daily price data from 1990 to 2022 across 64 continuous futures contracts covering Commodities, Equities, Fixed Income (FI), and Foreign Exchange (FX).

Methodology

Graph Learning - Uses a linear and interpretable graph learning model to infer dynamic asset networks strictly from historical price data, overcoming the lack of explicit fundamental links between disparate asset classes.

Momentum Features - Leverages 8 individual momentum features per asset, including volatility-scaled returns over multiple horizons (1-day to 1-year) and normalized Moving Average Convergence Divergence (MACD) indicators.

Portfolio Construction - Combines learned graph adjacency matrices with a linear regression model to forecast 1-day volatility-scaled returns across all assets.

Out-of-sample backtesting from 2000 to 2022 yields an annualized return of 22% and a Sharpe ratio of 1.51 after volatility scaling. The strategy exhibits low downside risk and low correlation with traditional individual momentum strategies. Inter-class (cross-asset) connections significantly enhance return predictability and diversification.