MétaCan
Menu
Back to cohort
Record W2061257103 · doi:10.1080/15427560.2013.849705

Seasonal Anomalies in Pension Plans

2013· article· en· W2061257103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Behavioral Finance · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersEuropean Social FundGobierno de Aragón
KeywordsPensionPortfolioVolatility (finance)Quarter (Canadian coin)EconomicsFinanceAccountingBusinessGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.250
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it