Speed of convergence to market efficiency in the ETFs market
Bibliographic record
Abstract
Purpose The aim of this paper is to examine the informational efficiency of prices of all exchange traded funds (ETFs) that are actively traded on the NYSE Arca, based on methodology developed by Chordia et al. Design/methodology/approach The authors estimate the speed of convergence to market efficiency based on short‐horizon return predictability from past order flows of 273 ETFs that were traded every day on the NYSE Arca during the first six months of 2008, and compare the resulting price formation process to that of shares traded on the NYSE and NYSE Arca. Findings Despite the significant differences in trading costs, volatility, and informational effects between ETFs and regular stocks, the paper documents that price adjustments to new information for ETFs occur in about 30 minutes, which is comparable to price adjustments for traditional stocks traded on Arca. In multivariate setting, the paper further shows that the speed of convergence to market efficiency of ETFs is not only significantly driven by volume, but also by the probability of informed trading. Research limitations/implications The findings provide direct answers and insights to questions posed in a recent SEC concept release document. The analysis of the speed of convergence provides a feasible measure to assess how efficiently prices of ETFs respond to new information. Originality/value The authors are first to utilize the short‐horizon return predictability from historical order flow approach to evaluate the price formation process of ETFs and to provide evidence on the determinants of its efficiency.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".