The effect of governance mechanisms on the financial and stock market performance: the case of Canadian companies
Bibliographic record
Abstract
The main contribution of this paper is to revisit the governance-performance relationship, by highlighting the non-linearity of the model expressing the effect of governance on the performance. The study is conducted over a 5-year period between 2011 and 2015 using panel regressions on a sample of Canadian publicly traded firms. Our estimations suggest that the effect of governance on the financial and the stock market performance remains undetermined. Using Hansen's (2000) model we show the presence of a threshold in the relationship between performance, measured by Tobin's Q and corporate governance. We conclude that the link between governance and firm performance is not linear and depends on the level of disclosure about governance. Our results are useful for regulators and investors by emphasising the impact that disclosure of governance information may have on firms' performance. Investors should therefore use the level of disclosure of governance information as performance indicators in their investment decision.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".