Ambiguity of tone in annual reports and bank risk taking
Bibliographic record
Abstract
Abstract In this paper, we examine whether abnormal ambiguity of tone in banks' 10‐K filings is associated with banks' risk‐taking behaviour. We estimate abnormal ambiguous tone using residual of a tone model that disentangles the obfuscation component from the information component in the ambiguous tone. We find that banks that intentionally use more ambiguous tone in 10‐K filings exhibit higher risk taking subsequently, consistent with the argument that an intentional ambiguous tone adds to the opacity of financial statements, which potentially masks managerial risk‐taking activities and curbs market discipline from external stakeholders. Our results remain robust to alternative model specifications and sensitivity tests which address potential endogeneity concerns. Furthermore, banks with higher abnormal ambiguous tone are associated with more aggressive investment decisions. Together, our findings suggest that abnormal ambiguous tone in annual reports is informative of banks' risk‐taking activities.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 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".