Corporate governance and risk in cross-listed and Canadian only companies
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate if there is a differential effect of corporate governance mechanisms on firm risk in Canadian companies cross-listed on US markets and Canadian companies not cross-listed (Canadian only companies). Design/methodology/approach Using a sample comprised of all Canadian companies included in the S&P/TSX Composite Index for the period 2009–2014, this study applies OLS and fixed effect regressions to investigate the effect of corporate governance mechanisms on firm risk. Interaction variables between governance mechanisms and the cross-listing status are used to examine if this effect is different for cross-listed firms. Findings Results indicate that the effect of board characteristics such as size, independence and proportion of female directors remains the same in both cross-listed and not cross-listed firms. CEO duality and insider equity ownership impact firm risk only in cross-listed companies, while institutional shareholdings, environmental, social and governance disclosure and family control affect firm risk in Canadian only firms. Overall, the empirical results indicate that some governance mechanisms impact firm risk only in firms that cross-list, while others are well-suited for Canadian only firms. Practical implications This study suggests that some of the differences between Canadian companies that cross-list and the Canadian companies that do not cross-list in US stock markets may change the impact of governance mechanisms on firm risk. Therefore, these findings have important implications for the design of governance mechanisms in Canadian firms. Since some of these differences are common to other economies, the conclusions can be extended to companies in other countries with similar governance structures. Originality/value Although previous studies have investigated the effect of governance mechanism on firm risk, this is the first paper that studies the differential effect for companies that cross-list in US markets. Specifically, differences in the ownership structure, firm control and in the regulatory and institutional environment, may explain this differential effect. Unlike most of the previous studies that focus on the effect of individual governance mechanisms, this study uses several mechanisms and their interactions at the same time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".