Enhancing Inflation Nowcasting with LLM: Sentiment Analysis on News

TL;DR

This paper introduces InflaBERT, a BERT-based sentiment analysis model for inflation news, which marginally improves inflation nowcasting accuracy during volatile periods like the COVID-19 pandemic.

Contribution

The study develops InflaBERT, a novel LLM fine-tuned for inflation sentiment analysis, and demonstrates its integration into existing models to enhance nowcasting during high volatility periods.

Findings

InflaBERT effectively captures inflation sentiment from news articles.

Incorporating sentiment index marginally improves nowcast accuracy during pandemic.

Sentiment analysis complements traditional economic indicators for inflation monitoring.

Abstract

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.