Management Going Concern Disclosure, Mitigation Plan, and Failure Prediction—Implications from ASU 2014-15
Bibliographic record
Abstract
ABSTRACT The going concern (GC) assumption forms the basis for preparing financial statements unless liquidation becomes imminent. ASU 2014-15 requires management to evaluate GC uncertainties quarterly and provide disclosures in the notes. I compare management GC disclosures between the pre-standard and post-standard regimes. I find that the market reacts negatively to substantial doubt in GC only after ASU 2014-15. Next, I find the effect of ASU 2014-15 for quarterly reports, but not annual reports. More importantly, by employing detailed textual analysis to extract and categorize mitigation-plan discussions, I show that certain types of management mitigation plans are interpreted more positively by investors after ASU 2014-15, thereby alleviating the negative market reaction. These plans include issuing debt, debt restructuring, increasing revenue, and selling assets. Finally, I demonstrate that management GC conclusions are more indicative of corporate failures after ASU 2014-15 and that mitigation-plan discussions are associated with firms' future viability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, not a consensus.
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".