Information leaks before CEO change: financial gain and ethical cost
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether there are information leaks immediately before CEOs change and – if so – whether some investors take financial advantage of such prior knowledge. It thirdly investigates the ethical, practical and professional options for communication managers to deal with such situations. Design/methodology/approach Working from sentiment theory of financial markets, the authors studied Internet search patterns for incoming CEO names and stock market movements immediately prior to the public mention or speculation of CEO change. Findings The authors find that in nearly a quarter of CEO changes at Fortune 500 companies, the name of the future CEO seems to have been leaked. Additionally, nearly half of those companies also experience extreme, otherwise unexplainable movements in the stock market. Originality/value This paper discovers the prevalence of extreme stock market movements for a company when the name of that company's next CEO has likely been leaked. Such leaks are an opportunity for unscrupulous investors, but they create ethical dilemmas for organizations. Communication managers typically respond by organizing tighter governance. However, to keep up with the speed of information and investments traveling through algorithms, organizing radical transparency could become an alternative instead.
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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 | 0.016 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".