Cross-Asset Momentum Spillover Effects

Abstract

Cross-asset momentum spillover, which describes the trend of the price changes of one asset connected to the other assets, is an important aspect of financial markets. Such a situation is typically thought to arise from nudges or reciprocal interdependencies of the underlying products. Classic linear models do not generalize well over time-series data and do not capture complex, non-linear relationships. In addition, with the delayed information spreading, the linear model is challenged again due to its inability to understand the sentiment of such information. The need for sophisticated computing methods in order to identify and measure individual spillovers is the focus of this investigation. Nowadays, we are able to use daily stock prices for feature engineering and convert them to many momentum indexes such as Rate of Change and Moving Average differences. We develop a deep learning framework utilizing a three-layer Gated Recurrent Unit (GRU) neural network to detect and quantify complex nonlinear dependencies and delayed information diffusion underlying momentum spillovers across paired equities. Momentum-based features—including Rate of Change and Moving Average differentials—are constructed from daily stock returns data spanning 2022 to 2023. The GRU model, trained with a 30-day lookback window, is applied to forecast future returns for stock pairs such as TSLA–F and NKE–ADDYY, which represent competitive and supply chain relationships. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared metrics. While the explanatory power is modest (e.g., R² = 0.0103 for the TSLA–F pair), the model captures weak yet statistically meaningful predictive signals. These findings underscore the potential of recurrent neural networks to reveal subtle cross-asset dynamics in noisy financial environments, offering valuable insights into the mechanisms of momentum contagion in modern equity markets.