How Domestic is the Fama and French Three-Factor Model? An Application to the Euro Area
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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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".