Bibliographic record
Abstract
The stock exchange industry has experienced strong competition in recent years. The commercial realities of the day have compelled some exchanges to change their ownership and governance structure from mutual to public ownership and have listed their shares on their own exchanges. This paper examines the value effects of self-listing, and the attendant change in business strategy, on the performance of listed exchanges. The results provide considerable support for the proposition that exchanges whose traditional sources of revenue have come under severe pressure, and those that have experienced a slow growth in net profit margin but high growth in market activities, are likely to change their ownership structure from mutual to public ownership. A comparison of the operating performance of the listed exchanges to that of a control group of non-listed exchanges shows that the self-listed exchanges have performed better than their non-listed counterparts. The self-listed exchanges also outperformed the stock market indexes and a control group of non-exchange firms that went public in the same year as the listed exchanges. I submit that better monitoring of managerial performance, the potential threat of takeover from the market for corporate control that accompanies self listing, and the reduction in agency costs associated with the mutual form of exchange, contribute to unlock growth opportunities and value for the publicly traded stock exchanges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".