Bundle manipulation: the use of accounting and textual obfuscation
Bibliographic record
Abstract
Purpose The purpose of this study is to explore whether managers and firms engage in bundle manipulation. It examines the effect of discretionary accruals and real activities manipulation on the level of complexity in annual reports. Design/methodology/approach The findings from the examination of the 1,435 annual reports of Canadian listed firms engaging in discretionary accruals and real activities manipulation indicate that these firms produce complex annual reports. Findings The authors, therefore, suggest that managers and firms use bundle manipulation, both accounting and textual, to mislead shareholders and stakeholders. The analyses also suggest that it is more difficult to detect the manipulation of real activities than discretionary accruals through textual analysis. Originality/value The authors propose an in-depth examination of how accruals and real activities manipulations affect the level of readability of firms’ reports. Furthermore, the authors suggest that firms engage in bundle manipulation, including accounting and textual manipulation. This paper aims to provide an in-depth analysis of the relationship between accounting and linguistic manipulations. The study suggests that investors could use the complexity of annual reports to detect earnings management. More specifically, it seems that firms engaging in discretionary accruals produce complex annual reports.
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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 | 0.012 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 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".