Semantic Shift and the Cross‐Section of Stock Returns in China

ABSTRACT This paper studies the role of linguistic instability, which we term the semantic shift, in explaining the cross section of expected stock returns. Using 3 million news articles from over 500 media outlets spanning 2010 to 2024, we construct a monthly semantic shift measure for each firm via a pre‐trained large language model in China. Empirically, we find a robust and significant return spread for low‐minus‐high semantic shift portfolio, with a monthly excess return as high as 1.39% after controlling for common risk factors. We further construct a semantic shift factor and a tradable mimicking portfolio and document consistent results. Exposure to negative innovations in the SemShift‐sorted portfolio return carries a larger estimated price than exposure to positive innovations. Our findings highlight media narrative dynamics as a key dimension in asset pricing beyond traditional fundamentals.