Bibliographic record
Abstract
Acknowledgments: I would like to thank INOVA research department of Faculdade de Economia da Universidade Nova de Lisboa for providing me with the data needed to develop this study. I would like thank my Advisor, Professor Pedro Santa-Clara for patiently guiding and motivating me during this project. This work project has greatly benefitted from his advices and comments. I am also extremely grateful to Professor Miguel Ferreira for the helpful comments, suggestions and for his invaluable support with the data and the application of the model. Finally, I would also like to thank Professor José Faias for helpful advices and comments. 2 Forecasting stock market returns has a long tradition in the academic literature, but most of the research focuses on the US stock market. In this study, I propose extending the sum-of-the-parts method (Ferreira and Santa-Clara (2008)) to four of the major international stock markets- Canada, France, Japan, and the UK- using variables that have been suggested as stock return predictors for the US. I find evidence that stock market return predictability varies substantially across countries. When compared to the US market, out-of-sample predictability is strong in Japan and the UK. Overall, the results of this study suggest that the sum-of-the-parts method is robust across countries as it always improves the forecasting performance on the traditional predictive regression 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".