The Effect of Director Monitoring on Bid and Ask Spreads
Bibliographic record
Abstract
ABSTRACT: We examine whether the market assesses a lower level of information asymmetry to firms that are perceived to be monitored more intensely by members of the board of directors. We use changes in bid-ask spreads as proxies for changes in information asymmetry between the firm and the market around the time earnings are announced. Our study is innovative in its association of director monitoring with levels of information asymmetry as reflected in quoted spreads. Our sample includes 145 firms included in the Toronto Stock Exchange 300 Index (TSX-300). The TSX’s hybrid market structure provides a unique international setting in which to examine the effects of governance on information asymmetry. Results indicate that the market attributes a lower level of information asymmetry to firms with a larger proportion of outside directors on the board. Contrary to our predictions, we find the larger the proportion of voting rights held by directors, the higher the level of information asymmetry attributed to the firm. We provide some evidence that a separate CEO/Chair leadership structure is associated with reduced information asymmetry. From a practice perspective, we are able to provide some preliminary insight into the potential value attributed by market participants to the imposition of regulation surrounding certain director monitoring activities.
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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 | 0.033 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".