Enhancing Inflation Nowcasting with LLM - Sentiment Analysis on News

Does incorporating a news sentiment index improve inflation nowcasting, particularly during periods of high volatility such as the COVID-19 pandemic?

Code repository for the FIN-407 course project. The report is available at https://github.com/paultltc/InflaBERT/report.pdf.

Abstract

This study explores integrating 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 \texttt{InflaBERT}, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce \texttt{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.

Repository Description

• news_eda.ipynb: EDA Notebook.
• inflabert.ipynb: Notebook used to build the InflaBERT model.
• news.ipynb: Notebook used to construct the NEWS index. Note that this notebook is designed to be run on Google Colab.
• nowcaster.ipynb: Notebook used to derive results of the section Inflation Nowcaster.
• plots.ipynb: Notebook used to derived most of the plots of the report.

Code Requirements

• ALFRED: This code repository uses the ALFRED Database. To have access to it, you will need an API key.
• Python Libraries: Install all packages required by running the following command:

  pip install -r requirements.txt