Illiquidity and the Measurement of Stock Price Synchronicity
Bibliographic record
Abstract
ABSTRACT This paper demonstrates that measures of stock price synchronicity based on market model R 2 s are predictably biased downward as a result of stock illiquidity, and that previously employed remedies to correct market model betas for measurement bias do not fix R 2 . Using a large international sample of firm‐years, we find strong negative and nonlinear relations between illiquidity and R 2 across countries, across firms, and over time. Because variables of interest frequently relate to illiquidity as well, we illustrate the consequences of not controlling for illiquidity in synchronicity research. More generally, we demonstrate the importance of using nonlinear control variable methods. Overall, we conclude that the illiquidity‐driven measurement bias in R 2 provides an explanation for why prior research finds low‐ R 2 firms to have weak information environments, and suggest future research carefully evaluate the sensitivity of its results to nonlinear controls for illiquidity.
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How this classification was reachedexpand
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".