Cherry Picking, Disclosure Quality, and Comprehensive Income Reporting Choices: The Case of Property‐Liability Insurers*
Bibliographic record
Abstract
Abstract Statement of Financial Accounting Standards No. 130: Reporting Comprehensive Incomeencourages enterprises to report comprehensive income on a performance statement rather than on a statement of equity. We investigate the reporting decisions of 82 publicly traded property‐liability insurers that are fairly evenly split in their choice. Our results demonstrate that insurers with a tendency to manage earnings through realized securities' gains and losses (that is, cherry pickers), as well as insurers with a reputation for poor disclosure quality, are more likely to report comprehensive income in a statement of equity. Apparently, these insurers face the highest cost of transparency. We do not find a relation between the reporting decision and the volatility of comprehensive income relative to the volatility of net income. Our findings that insurers' comprehensive income reporting choices are a reflection of their proclivity toward cherry picking as well as their level of disclosure quality should be of interest to standard‐setters because of the controversy over standard‐setters' preference for mandating all firms to report comprehensive income in a performance statement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".