Testing Financial Integration: Finite Sample Motivated Mothods
Bibliographic record
Abstract
This paper examines financial market integration in North-America from January 1984 to December 2003, using two basic CAPM and APT test models. We introduce a methodology valid in finite samples for the CAPM model. A pivotal statistic is introduced to correct for the so-called dimensionality curse which affects the critical points of the LR test statistic under the null hypothesis. When using this methodology, the null hypothesis of integration is strongly rejected for all sub-periods. Our results differ from those obtained in previous studies such as Mittoo (1992) using an asymptotic methodology. Next, an APT model with pre specified factors is used in order to test the null hypothesis of integration. The factors used are the Fama and French factors. In the latter two-pass test context, we introduce a split sample methodology in order to correct for the pre-estimation of BETAS. Moreover, we consider (and form) Fama and French factors for Canada for the 1984-2003 period. With this methodology, we again strongly reject the hypothesis of integration except for two sub-periods. Fama and French factors appear to have a different effect on the Canadian and American stock returns
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".