Predicting Corporate Bankruptcy: Integrating Financial Ratios and Risk Disclosure Text
Bibliographic record
Abstract
This thesis investigates whether firms’ narrative risk disclosures can improve the accuracy of bankruptcy prediction models traditionally based on financial ratios. While financial indicators have long been used to assess distress risk, they may overlook forward-looking signals embedded in managerial language. To address this, the study incorporates textual variables extracted from the risk factor section (Item 1A) of 10-K filings into a logistic regression framework. The analysis is based on a sample of U.S. and Canadian public firms from 2014 to 2019, using Compustat financial data and SEC filings. Textual features, including tone, specificity, and references to legal risk are constructed using dictionary-based methods. The results show that these narrative indicators contribute modest but consistent improvements in predictive performance, particularly in out-of-sample tests. The findings suggest that Item 1A risk disclosures contain useful information and can complement traditional models. Future research may benefit from applying more advanced natural language models to uncover deeper signals within corporate text.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".