The CEO's ethical dilemma in the era of earnings management
Bibliographic record
Abstract
Purpose The paper aims to argue that stock‐based compensation for top leaders is a very recent phenomenon that is associated with lower shareholder returns, bubbles and crashes and huge corporate scandals and that it is time to bring an end to it and find a better, more authentic approach that will enable corporations, stakeholders and the financial community to thrive. Design/methodology/approach The paper details how many executives engage in a dangerous and little‐discussed practice that comes very close to the line of illegality, one that betrays the spirit of securities laws and accounting regulation: earnings management. It concludes that far too many corporate leaders are now using their talents and corporate resources to smooth earnings, and bump up the stock price, rather than to build their companies. Findings The paper proposes that corporations find a way to restore the focus of the executive on the real market and on an authentic life by eliminating the use of stock‐based compensation as an incentive. Practical implications The author's remedy: top executives should be prevented from selling any stock – for any reason – while serving as a corporate leader, and indeed for several years after leaving their post. Originality/value The author calls for an end to stock‐based compensation because it is associated with lower shareholder returns, bubbles and crashes and huge corporate scandals.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| 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".