Evidence of Idiosyncratic Seasonality in ETFs Performance
Bibliographic record
Abstract
Studies of the seasonality of ETFs are relatively scarce compared with other financial assets. Moreover, most of the existing literature on ETFs did not assess the seasonality patterns of risk-adjusted returns and tracking error. This article seeks to suppress some of these gaps. The results provide evidence of a first-half of the year effect (higher returns), an outperformance of the second quarter and an underperformance of the fourth quarter compared with the remaining quarters, and higher (lower) returns in the first (third) month of the quarter vs the other months of the quarter. Furthermore, April exhibits a superior and December an inferior performance compared with the remaining months. Besides, higher (lower) returns on Wednesdays (Fridays) were observed compared with the other weekdays. Regarding the tracking error, some seasonal patterns are also reported. For example, the replication was more accurate in April than it was in remaining months and in the first month of each quarter. Finally, the effects detected in ETFs returns were not reflected in indices returns, with the exception of the April effect, indicating that the main seasonality patterns detected are caused by idiosyncratic ETFs factors and not to the constituents of the underlying indices.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".