Clarification or Confusion: A Textual Analysis of ASC 842 Lease Transition Disclosures
Bibliographic record
Abstract
We study the transition disclosures in firms’ 10-K filings preceding the mandatory adoption of Accounting Standards Codification 842 on leases. We find that ASC 842 transition disclosures become more unreadable and dissimilar the closer to adoption, potentially because the SEC guidance on transition disclosures emphasizes detail on the specifics of the standard and whether it has material effects on future financial statements. As a result, firms’ ASC 842 transition disclosures reflect an increasing amount of technical and complex language over the transition period. Firms’ increasing use of technical and complex language may not benefit all investors and the market as a whole. Based on tests of the change in analysts’ earnings forecast delay and market uncertainty in stock returns, we find that ASC 842 transition disclosures mostly favor investors with superior information processing skills. This is contrary to the statutory goal of the SEC to increase transparency for all investors who may prefer firms’ use of clear and straightforward language to describe the effects of future accounting changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".