Bibliographic record
Abstract
This thesis explores the returns of country exchange-traded funds (ETFs) with regime switching risk factors. Using the Bayesian information criterion, I select the model with six risk factors and three states among other models.The estimation results show that both the returns of country ETFs and their sensitivities to risk factors are highly regime dependent.Firstly, the U.S. size and value factors are significant in explaining all selected ETFs across regimes. More specifically, small capitalization is associated with lower returns for seven ETFs in some regimes. High book-to-market ratio generates higher returns for all ETFs in most regimes. Secondly, the global stock market has a positive impact on all selected country ETFs. Thirdly, all ETFs returns are negatively correlated to market volatility in bull and bear markets. Fourthly, stronger U.S. dollar generates a higher return for US ETF and lower returns for other seven ETFs across regimes. Finally, the returns of Australia, Canada and UK ETFs, which invest heavily in materials, are positively affected by commodity prices while other ETF returns are negatively influenced by them across regimes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".