Geographically dispersed ownership and inter-market stock price arbitrage—Ahold's crisis of corporate governance and its implications for global standards
Bibliographic record
Abstract
Scandals of corporate governance in the United States and Europe in the aftermath of the TMT bubble captured the public imagination. In play were the interests of senior executives in relation to investors, prompting debate over countries' standards of corporate governance in the global market place. Ahold was (and is) an especially important instance, involving significant internal accounting and reporting failures and poor public disclosure of market-sensitive information. Ahold is also a global corporation cross-listed on major financial markets. In this paper, we report the analysis of market trading in Ahold stock between Amsterdam and New York. It is shown that greater volatility in Amsterdam daily closing prices presaged the crisis to come in Ahold shares implying leakage of information to privileged local insiders. It is also shown that in the aftermath of Ahold's crisis, management responded to the lack of global investor confidence by improving transparency and governance standards consistent with the expectations of global investors. Implications are drawn for the pricing of corporate governance and the process of convergence in national standards of corporate governance. The continuity of different regimes of governance is subject to inter-market arbitrage especially if corporations seek to maintain and enhance their reputations in the global financial market place.
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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 | 0.000 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".