用大模型分析新闻情绪，提升通胀实时预测精度。
Enhancing Inflation Nowcasting with LLM: Sentiment Analysis on News
- 基于BERT微调情绪模型InflaBERT，捕捉新闻中通胀相关情绪。
- 融合新闻情绪指数后，疫情期间通胀预测误差降低1.2%。
- 适合关注经济预测与大模型应用的金融研究者。
本研究探讨将大语言模型（LLMs）融入经典通胀实时预测框架的可行性，尤其针对新冠疫情等高通胀波动时期。提出InflaBERT——一种基于BERT并针对通胀相关新闻情绪进行微调的模型，用于生成衡量月度新闻情绪的NEWS指数。将该预期指数引入仅依赖宏观经济自回归过程的克利夫兰联储模型，结果显示在疫情期间预测准确率有小幅提升。这表明结合新闻情绪与传统经济指标具有潜力，为更精准的实时通胀监测提供了新方向。代码已公开于https://github.com/paultltc/InflaBERT。
原文摘要 · Abstract (English)
This study explores the integration of large language models (LLMs) into classic inflation nowcasting frameworks, particularly in light of high inflation volatility periods such as the COVID-19 pandemic. We propose InflaBERT, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce NEWS, an index capturing the monthly sentiment of the news regarding inflation. Incorporating our expectation index into the Cleveland Fed's model, which is only based on macroeconomic autoregressive processes, shows a marginal improvement in nowcast accuracy during the pandemic. This highlights the potential of combining sentiment analysis with traditional economic indicators, suggesting further research to refine these methodologies for better real-time inflation monitoring. The source code is available at https://github.com/paultltc/InflaBERT.
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