2024/10/26 by Marc-Antoine Allard, Allard, Marc-Antoine, Paul Teiletche +3
Business, Management and Accounting · Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computational Engineering #Digital Marketing and Social Media #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance #Finance #Web Data Mining and Analysis #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2410.20198
openalex publication_date 2024/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.