Nie ma wersji polskiej
Lukas Joseph Nagy
- 29 July 2026
- WORKING PAPER SERIES - No. 3262Details
- Abstract
- Financial stability risks consist of two distinct components: vulnerabilities and possible trigger events. While there has been considerable progress regarding the measurement of vulnerabilities, the assessment of possible trigger events remains largely qualitative. To fill this gap, we employ Large Language Models to extract information about the Severity and Probability Of potential Trigger events (SPOT) from a large dataset of financial news articles over the period2005 – 2026. The SPOT indicator increases ahead of major historical trigger events, correctly identifies trigger sources, and helps to improve forward looking model estimates of downside risks to the economy. The results indicate that the use of AI-based signal extraction from text can be a promising avenue to improve the monitoring of financial stability risks.
- JEL Code
- C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
C88 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Other Computer Software
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G01 : Financial Economics→General→Financial Crises