Risk disclosure and firm risk: Evidence from Canadian firms
Bibliographic record
Abstract
In recent decades, financial and accounting regulators have turned the spotlight on risk management and disclosure. Like securities regulators in the United States, the United Kingdom and several other countries, Canadian Securities Administrators have set out requirements for the disclosure and discussion of risks in the MD&A section of annual reports. Responding positively to these new guidelines, organisations now report many risks in their MD&A. These disclosure requirements are intended to provide information about a company’s material risks to help stakeholders understand and evaluate interrelated risks, the risks’ impact and the company’s risk management strategies (Khandelwal, Kumar, Verma, & Pratap Singh, 2019). However, since the nature of the risks disclosed derives wholly from organisational decisions, the content of these disclosures can be considered voluntary. For this reason, some critics argue that risk disclosures are by and large boilerplate in nature (Bao & Datta, 2014; Hope, Hu, & Lu, 2016). From this perspective, this study aims to examine whether there is a relationship between the risks firms disclose in their annual reports and their systematic risk. The regression analyses were carried out on the risks disclosed by a sample of 200 Canadian companies included in the 2016 Toronto Stock Exchange S&P/TSX Composite Index. These analyses revealed a positive and significant relationship between the risks disclosed and the firms’ systematic risk. Our results support the regulatory approaches respecting this type of information adopted by a number of countries. Accordingly, disclosing the risks that companies face should help small investors understand and appreciate them.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".