本研究探討新聞情緒對股票累積超額報酬（CAR）的影響，並從信念摩擦（belief friction）的觀點，分析不同大型語言模型對同一則新聞之情緒解讀分歧，是否會改變新聞情緒的價格傳導效果。研究以 2020 年至 2024 年間 S\&P 500 成分股相關新聞為樣本，利用多種大型語言模型衡量新聞情緒，並建構情緒解讀分歧指標，以分析新聞情緒、解讀分歧與股價反應之間的關係。

新聞情緒對股票累積超額報酬具有顯著正向影響，且其效果會隨事件時間逐步累積，顯示市場對新聞情緒的反應並非立即完成，而具有持續性的價格調整過程。此外，不同模型對同一則新聞的情緒解讀分歧會削弱新聞情緒對股價的影響，支持本研究所提出之信念摩擦觀點，亦即當市場難以對資訊形成一致解讀時，情緒訊號較不易轉化為一致方向的交易行為與價格變動。研究亦發現，新聞情緒與股價反應之間存在非線性關係，顯示市場對不同強度情緒訊號的反應程度並不相同。

This study examines the impact of news sentiment on cumulative abnormal returns (CAR) and, from the perspective of belief friction, investigates whether divergence in sentiment interpretation across different large language models alters the price transmission effect of news sentiment. Using news articles related to S\&P 500 constituent firms from 2020 to 2024 as the sample, the study measures news sentiment with multiple large language models and constructs an interpretation-divergence indicator to analyze the relationship among news sentiment, interpretive disagreement, and stock price reactions.

The results show that news sentiment has a significant positive effect on cumulative abnormal returns, and that this effect accumulates gradually over event time, indicating that the market's response to news sentiment is not completed immediately but unfolds through a persistent price adjustment process. In addition, divergence in sentiment interpretation across different models weakens the impact of news sentiment on stock prices, supporting the proposed belief-friction view—that when the market cannot form a consistent interpretation of information, sentiment signals are less likely to be translated into coordinated trading behavior and price movements. The study also finds a nonlinear relationship between news sentiment and stock price reactions, suggesting that the market responds differently to sentiment signals of varying intensity.

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