News sentiment and investment risk management: Innovative evidence from the large language models
利用GPT-4和RavenPack分类新闻情绪，研究其对股票收益波动持续性的影响，发现精确测量的新闻情绪能显著影响波动动态，且GPT-4分类更准确。
This paper reexamines the significance of news sentiment in explaining stock return volatility persistence and its role in driving underlying volatility states. Our simulation study demonstrates that more accurately measured news sentiment has a greater impact on volatility dynamics. Using data from firms in the Dow Jones Composite Average index spanning 2019–2023, we compare news sentiment classified by GPT-4 with that classified by RavenPack. Our findings show that both negative and positive firm-specific and macroeconomic news significantly affect intraday stock return volatility. The classification accuracy achieved by employing GPT-4 potentially surpasses that of using RavenPack. • News sentiment can substantially impact stock return volatility. • Simulation shows larger influence of precisely measured news on volatility dynamics. • Both firm-specific and macroeconomic news impact intraday stock return volatility. • GPT-4 outperforms RavenPack in classifying news sentiment for volatility analysis.