How Domestic is the Fama and French Three-Factor Model? An Application to the Euro Area
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Bibliographic record
Abstract
The euro area has faced a high number of monetary and policy changes in the recent past as a\nconsequence of the European integration process and, naturally, these developments have\nimportant implications for portfolio diversification and asset pricing. Therefore, this paper concentrates on the performance of a specific asset pricing model: the Fama and French threefactor model. Griffin (2002) shows that the Fama and French factors are country specific for the U.S., the U.K, Canada, and Japan. We apply the same methodology to the euro area countries and find that even in this very integrated area the domestic three-factor model outperforms the euro area three-factor model. However, the relative performance of the euro area wide model is increasing, especially for countries with a high number of listed stocks. This could be interpreted as evidence of a higher level of equity market integration caused by lower investment barriers and a changing point of view of institutional investors. Furthermore, we extend the methodology and also test an industry-specific three-factor model. Our findings suggest that lower pricing can be acquired using an industry-specific model relative to the euro area three-factor model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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