The effect of risk management on the performance of Canadian firms
Bibliographic record
Abstract
Since the 2008 financial crisis, the relationship between investing in enterprise risk management (ERM) and its influence on business performance has continued to gain popularity and with the enormous volatility in the business world today, proper ERM is more important than ever (Chen, Tsao, Hsieh, & Hu, 2019; Maruhun, Atan, Yusuf, Rahman, & Abdullah, 2021). Is it the companies that manage risks better that perform better, regardless of the industry? The objective of this research is to analyze the effect of the way in which risks are managed by Canadian firms in different industries and the impact of this management on different levels of performance. A sample of 30 annual reports covering the fiscal years ending in 2019 and 2020 from fifteen Canadian companies that trade on the Toronto Stock Exchange (TSX) has been completed. The analysis of Pearson’s correlation coefficients as well as the coefficients of determinations made it possible to assess the relationship between the various ERM variables and company performance. By analyzing the correlations obtained for the 2019 and 2020 financial years, no significant relationship could be demonstrated between ERM, and 5 performance indicators analyzed. However, several significant correlations have indeed been demonstrated between each industry studied, these affecting different performance indicators depending on the sector.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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".