Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper examines the seasonal patterns of Spanish pension plan returns at quarter and year end. Consistent with existing literature, results indicate that a set of portfolios obtain levels of performance during certain months, especially December, that are significantly different from the rest of the months. However, when the relationship between seasonal patterns and previous performance is analyzed, results suggest that top performers during the year experience a penalization in the performance of December. This finding can be explained for different reasons such as window dressing practices and a negative influence of high investment inflows during this month. Nevertheless, the observed decrease in the volatility level at the end of the year seems to suggest that managers follow their benchmarks more closely when they have to report their portfolio returns. Keywords: Calendar anomaliesPension plansPortfolio pumpingTax-loss sellingWindow dressing ACKNOWLEDGMENTS The authors are grateful to participants at 18th Finance Forum held in Elche and to the anonymous review process for allowing us to improve the quality of the paper. The authors also acknowledge financial support from the local Government of Aragon and the European Social Fund (Project 268-196) and from the University of Zaragoza (Project 268-207). Any possible errors contained in the paper are the exclusive responsibility of the authors. Notes 01. SMBt is the difference between the returns on a portfolio of small and large EMU companies, while HMLt is the difference in returns on a portfolio of high book-to-market and low book-to-market EMU companies. These data have been provided by Morgan Stanley Capital International (MSCI). 02. ERit denotes both excess return over the market and excess return from the three-factor model depending on the analysis. 03. SDMit denotes both the monthly square deviation to the mean excess return over the market and to the mean excess return from the three-factor model depending on the analysis. 04. The use of moving averages provides similar results. These results are available upon request to the authors. 05. The analysis is focused on money flows in December because investment flows to Spanish pension plans are concentrated in the last months of the year.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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