LLM-BASED SENTIMENT ANALYSIS FOR FINANCIAL DISTRESS DETECTION: EVIDENCE FROM THE 2023 U.S. BANK FAILURES

Abstract

This paper evaluates whether large language model (LLM)-based sentiment analysis can detect financial distress more accurately than traditional dictionary-based methods. Using the 2023 U.S. bank failures as a natural experiment, Silicon Valley Bank (SVB), Signature Bank, and First Republic Bank each failed during March–May 2023, we construct monthly sentiment indices for five banks using an LLM alongside VADER, TextBlob, and FinBERT under an identical weighting framework. The LLM index consistently declines ahead of and during the failure period for the three distressed institutions while remaining stable for the two control banks (Bank of America, JPMorgan Chase). VADER, TextBlob, and FinBERT fail to detect the distress, remaining strongly positive throughout. Cohen’s Kappa coefficients near zero (0.01– 0.22) confirm that the methods capture fundamentally different signals. The LLM index is constructed using a severity-weighted aggregation scheme incorporating source credibility, model confidence, and recency, normalised via a tanh transformation. These findings suggest that LLMs interpret financial context rather than merely counting sentiment-bearing words, offering a meaningful advance for early-warning and financial risk monitoring applications.