Predicting Corporate Financial Distress with NLP
Integrated credit risk model combining financial ratio analysis, structural modeling, and fine-tuned LLMs applied to news text for early corporate distress prediction.
Overview
Traditional corporate credit risk models rely almost entirely on structured financial data — balance sheets, income statements, and market prices. While useful, these signals are inherently backward-looking: by the time ratios signal distress, it may be too late for creditors or investors to act. This project builds a more holistic prediction framework that fuses three complementary data streams: financial ratio analysis, structural credit modeling, and NLP-based text mining of news articles.
The result is a unified credit risk score that responds faster to emerging signals, particularly those appearing first in financial news before they surface in quarterly filings.
Technical Approach
The methodology operates in three stages integrated into a single prediction pipeline.
Financial Ratio Analysis forms the baseline layer. Standard profitability, leverage, coverage, liquidity, and growth ratios are computed from firm financial statements and provide a historical snapshot of financial health.
Structural Credit Modeling applies the Black-Scholes-Merton framework, treating a firm’s equity as a call option on its assets. By estimating asset volatility, drift, and default-point distance, this layer provides a market-implied probability of insolvency that responds to real-time price movements.
Text Mining is the novel contribution. A two-step NLP pipeline first applies topic modeling (LDA/NMF) to filter news articles for credit-relevant content, using credit rating agency reports as training signal. The filtered articles then pass through fine-tuned large language models for credit-risk sentiment classification — converting raw news into quantifiable distress signals.
The three model outputs are combined with dynamic weighting: text mining receives higher weight when more article volume is available for a given firm. The combined model also incorporates downside momentum in credit risk and handles missing data gracefully, producing a final score from any available subset of inputs.
Significance
This work demonstrates that unstructured textual data carries predictive power for corporate default that is orthogonal to what structured financial data captures. News articles reflect current conditions — leadership changes, product recalls, litigation — days or weeks before these events appear in financial ratios. By operationalizing this timeliness advantage through fine-tuned LLMs, the model offers financial analysts, risk managers, and policymakers a more responsive early-warning tool.
The project also advances the methodology for applying LLMs to specialized financial risk tasks, showing that domain-specific fine-tuning on credit rating reports produces sentiment classifiers that outperform general-purpose sentiment tools on this narrow but high-stakes classification problem.