Size, value and momentum in \ninternational stock returns
Bibliographic record
Abstract
This thesis extends the empirical asset pricing literature by testing whether alternative \nspecifications of Fama and French’s (1993) three-factor and Carhart’s (1997) four-factor \nmodels capture size, value and momentum anomalies. Specifically, the alternative models \ntested include the modified and index-based models of Cremers et al. (2013) and decomposed \nmodels of Fama and French (2012). This thesis investigates international stock returns and \nwhether asset pricing models are integrated across four countries, namely the US, UK, Japan, \nand Canada. Finally, the information content of the empirically motivated size, value and \nmomentum factors is tested using Petkova’s (2006) ICAPM model. The models are tested using \nboth time-series and cross-sectional regression approaches. \nThe results show that the factors constructed using different approaches have quite \ndifferent average returns. In general, there is no size premium in average stock returns in any \ncountry. There is a value premium only for Japan and Canada that increases with size, while \nthere is a momentum premium everywhere except Japan, which declines with size. Both timeseries \nand cross-sectional results show that the alternative models significantly improve the \npricing performance, and especially the index-based model successfully explains the size and \nB/M portfolio returns for the four countries. None of the models can explain the size and \nmomentum portfolio returns except for Japan. Although the international index-based model \nreceives some empirical support in a combined international sample, the US and Japan, \ngenerally, the international models fail badly, which indicates a lack of integration. When \nrelating size, value and momentum factors with innovations to the state variables in an ICAPM \nspecification, the results are discouraging and contradict Petkova’s (2006) results for the US. \nThe size, value and momentum factors remain important factors in explaining the crosssectional \nreturns for all countries, even in the presence of the state variable innovations
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".