Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We examine whether the language used in 10‐K filings reflects a firm's risk of bankruptcy. Our sample contains 424 bankrupt U.S. companies in the period 1994–2015 and we use propensity score matching to find healthy matches. Based on a logit model of failing and vital firms, our findings indicate that firms at risk of bankruptcy use significantly more negative words in their 10‐K filings than comparable vital companies. This relationship holds up until three years prior to the actual bankruptcy filing. With our investigation, we confirm the results from previous accounting and finance research. 10‐K filings contain valuable information beyond the reported financials. Additionally, we show that 10‐Ks filed in the year of a firm's collapse contain an increased number of litigious words relative to healthy businesses. This indicates that the management of failing firms is already dealing with legal issues when reporting financials prior to bankruptcy. Our results suggest that analysts ought to include the presentation of financials in their assessment of bankruptcy risk as it contains explanatory and predictive power beyond the financial ratios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it