Bibliographic record
Abstract
Due to the recent transformation of many securities exchanges into for-profit, publicly traded companies, we use portfolio theory and historical risk-return relationships to consider several scenarios (78 in total) based on hypothetical mergers between all possible pairings of these exchanges. We identify both the “best” and “worst” merger pairs solely based on these risk-return and correlation patterns, and thus do not include potential merger synergies related to economies of scale or scope. The analysis presented here thus provides an objective measure of the relative attractiveness of various mergers to investors in a relatively new but rapidly growing investment sector: for-profit securities exchanges. We find that Asian Pacific exchanges such as those based in Australia and Singapore consistently represent the strongest combinations of risk and return. In North America, the mergers associated with the Toronto Stock Exchange and Chicago Mercantile Exchange offer the best risk-return relationships. Overall, our approach suggests there is considerable variation in the risk-return characteristics of passively managed mergers of securities exchanges and that the “best” pairings typically include an Asian Pacific exchange as a partner whereas some of the weaker hypothetical mergers include European and / or North American exchanges. TOPICS:Portfolio theory, technical analysis, simulations, emerging
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.021 |
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".