Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. <b>TOPICS:</b>Portfolio theory, technical analysis, simulations, emerging
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it