Momentum Effect in China: A Deep Learning Perspective

This paper introduces machine learning methods, combined with the diversity of investors' behavior, to form a comprehensive momentum indicator to measure the momentum effect in the Chinese stock market. We find that neural networks are superior in capturing the nonlinear relationships of financial data. Furthermore, we empirically verify the existence of momentum effects generated by heterogeneous investor behaviors. Our results reveal a robust multi‐time‐horizon linkage momentum effect after controlling for risk factors, with medium‐ and long‐term sentiment serving as key determinants in shaping momentum indicator. Through the spanning regression test, the paper finds that the traditional momentum returns cannot fully explain the deep‐learning‐based momentum LS returns. Economically, information uncertainty and investor attention constraints partially account for the formation of multi‐term momentum. Moreover, the momentum effect is amplified under up‐trending markets, high‐sentiment regimes, expansionary business cycles, and low economic policy uncertainty. This study deepens the understanding of the existence and economic sources of the momentum effect across multiple time horizons.