Could the global financial crisis improve the performance of the G7 stocks markets
Bibliographic record
Abstract
Financial crises are normally associated with negative effects on financial markets. In this paper, we investigate whether the most recent Global Financial Crisis (GFC) had any positive impact on the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom and the United States) indices. We carry out our investigation by employing mean-variance (MV) analysis, CAPM statistics, a runs test, a multiple variation ratio test, and stochastic dominance (SD) tests. Our MV and CAPM results conclude that most of the G7 stock indices are significantly less volatile and have a higher beta, higher Sharpe ratios and a higher Treynor’s index after the GFC. Run tests and multiple variation ratio results confirm that efficiency improved in the post-GFC period. Finally, SD results conclude that there is no arbitrage opportunity and the markets are efficient due to the GFC, and, in general, investors prefer investing in the indices after the GFC. Overall, we conclude that the GFC led to markets that are more efficient and mature, confirming that crises can also have positive impacts on stock markets.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".