Corporate governance and informed trading
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically study the relationship between informed trading and overall corporate governance mechanisms. Design/methodology/approach A broad range of governance characteristics are used to measure the governance structure of firms in the Toronto Stock Exchange. The risk of informed trading is estimated using a PIN measure that avoids biases induced by trade classification errors. Our proxies for informed trading are regressed on measures of corporate governance. Findings Our most important result is that the observed trade‐off between CEO compensation and informed trading holds only for large firms. There is no correlation between CEO cash compensation and the risk of informed trading in small and medium sized firms. We find evidence that cross‐sectional differences in the risk of informed trading are explained by a firm's governance structure. Research limitations/implications Research finding a trade‐off between CEO compensation and informed trading merits closer examination. Practical implications Limitations on insider trading, and more broadly on informed trading, may involve different costs and benefits for large firms than for medium and small firms. Originality/value This paper expands the set of governance characteristics shown to interact with informed trading activity. The Toronto market is well suited to focusing on relations between informed trading and firm‐level governance characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| 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".