The impact of environmental accidents on the behaviour of TSE traded companies
Bibliographic record
Abstract
Event study methodology is used to assess the sensitivity of stock return and systematic risk (beta) to environmental accidents. This thesis analyses 19 oil spill events, four mining accidents, and 10 miscellaneous incidents including PCB fires, transportation spills, radioactive an other gaseous releases. Most of the events had little impact on beta. Two mining accidents showed a marked downward effect on the betas across the industry. One PCB fire had an upward impact. When the mining event data were pooled in a time series cross-sectional analysis, the location of the accident, the number of people affected, and time, were all statistically significant. When data for the 10 miscellaneous events were pooled, the same three factors were significant. The evidence suggests that when an accident occurs in an industry that is little understood by the general public, beta rises. When it occurs in an industry or jurisdiction where environmental regulation is well understood, systematic risk declines. In either case, the number of people directly affected by the accident is directly (positively) related to changes in beta. The pooled model was also used to study cumulative abnormal returns. Evidence of a size effect in the miscellaneous events supports the economic consequences theory of accounting. Companies cross-listed on the NYSE, AMEX, or NASDAQ provide conflicting evidence concerning voluntary disclosure theory. The significance of this study is fourfold. First, event study practitioners concerned with abnormal returns cannot assume the beta parameter is unaffected by the event. Second, while industry regulation entails limitations to management discretion, the results of this analysis suggest that in the mining sector at least, reduced flexibility in operating and disclosure decisions is associated with a decline in beta. This means that in their lobbyist role, mining industry representatives seeking to satisfy the environmentally conscious segment of the market by supporting additional regulation, need not worry that they do so at the expense of the overall investment community. Also, investors concerned with portfolio risk are interested in knowing the factors which contribute to changes in nondiversifiable risk. Finally, this thesis develops a foundation of Canadian work, upon which future research in accounting disclosure and operating decisions can be based.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".