Consolidation and Business Strategies in the Securities Industry: How Securities Exchanges Create Value?
Bibliographic record
Abstract
The exchange industry had undergone in last years a process of rapid change starting with the listing of major exchanges. Technological developments and regulatory reforms contributed to the falling of national trade barriers and gave rise to growing competition which forced incumbents to react with cross-border mergers. The consolidation process in the securities industry followed a pattern of heterogeneous mergers aimed both to widen liquidity and diversify the business model. This would give rise to cost and revenue synergies leveraging on the joint use of common trading platforms and the development of cross selling opportunities, respectively. Nevertheless, mergers hide some threats as cuts in earnings and cash flows due to increasing competition may adversely affect goodwill and the capital base for exchanges going forward with sizable acquisitions. Governance arrangements at securities industry level turn also to be redefined, giving rise to widespread links between operators which may undermine the full exploitation of expected synergies. These developments have implications for regulators which are required to favor conditions for fair mutual access to exchange services between different jurisdictions in particular at a transatlantic level.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".