Regulation and the financial performance of Canadian agribusinesses
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationships between regulatory changes, returns on equity and stock market valuations for Canadian food and non‐food agribusinesses. Design/methodology/approach Two empirical approaches are employed. First, an event study is used to evaluate the impact of official regulatory announcements on the stock market valuations of selected Canadian agribusinesses. Next, an approach introduced by Mishra et al. using the Du Pont expansion is applied to investigate the effect of regulations on firms' accounting profits. Data on Canadian food and non‐food agribusinesses are collected from Bloomberg, Thompson One Banker and SEDAR. Findings The event study demonstrates that official regulatory announcement dates do not correspond with abnormal stock market returns for Canadian firms, while the Du Pont model yields mixed evidence with respect to their accounting profits. Research limitations/implications This paper only considers publicly traded companies. As a result, survivorship bias may exist. Future research should include privately held and cooperative firms. Social implications Food regulations can influence firm profits and shareholder wealth, so understanding how government actions influence agribusiness is important when considering the total costs of current and future food policy. Originality/value The interaction between policy and the financial performance of Canada's publicly traded agribusinesses is an under‐researched area and no studies have examined Canadian data. The results of this study are valuable to both policy makers and researchers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".