Exposition of Evidence for Idiosyncratic versus Induced Seasonality in ETF Performance
Bibliographic record
Abstract
An exchange-traded fund (ETF) is a marketable security that tracks a stock index, a commodity, bonds, or a basket of assets. Therefore, returns of ETFs that track a benchmark index portfolio should mimic the returns of their benchmark indexes. If a benchmark index’s performance exhibits a seasonal pattern, then the performance of its associated ETF should replicate that pattern. This type of ETF performance seasonality is induced by trends in the market of securities in the benchmark index portfolio. Any other seasonality can be considered to be idiosyncratic. Based on a sample of 148 ETFs listed in NYSE Arca, this article provides evidence of a half-year effect (higher performance in the first half-year), a quarter effect (outperformance of the second quarter and underperformance of the fourth quarter), and month within the quarter effect (higher and lower performance in the first and third months of each quarter, respectively). Additionally, superior and inferior performance were observed in April and December, respectively.These seasonal patterns are not visible on benchmark indexes, with the exception of the unusually positive performance in April, which can be considered induced seasonality. The other effects, which cannot be attributed to underlying markets, are evidence of idiosyncratic seasonality.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".