Earnings Management through Transaction Structuring: Contingent Convertible Debt and Diluted Earnings per Share
Bibliographic record
Abstract
ABSTRACT In this article we examine whether firms structure their convertible bond transactions to manage diluted earnings per share (EPS). We find that the likelihood of firms issuing contingent convertible bonds (COCOs), which are often excluded from diluted EPS calculations under Statement of Financial Accounting Standard (SFAS) 128, is significantly associated with the reduction that would occur in diluted EPS if the bonds were traditionally structured. We also document that firms' use of EPS‐based compensation contracts significantly affects the likelihood of COCO issuance and find weak evidence that reputation costs, measured using earnings restatement data, play a role in the structuring decision. These results are robust to controlling for alternative motivations for issuing COCOs, including tax and dilution arguments. In addition, an examination of announcement returns reveals that investors view the net benefits and costs of COCOs as offsetting one another. Our results contribute to the literature on earnings management, diluted EPS, financial reporting costs, and financial innovation.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".