The role of indemnification agreements and legal liability in railroad disasters
Bibliographic record
Abstract
Purpose The study aims to examine the stock price performance of publicly owned railroad companies following severe railroad accidents that resulted in the loss of human lives and/or hazardous material spills. The focus is on legal liability considerations as one of the primary factors that drives a firm's abnormal performance following a given accident. Design/methodology/approach This paper employs a sample of 97 railroad accidents that occurred between January 1967 and December 2006 and involved equipment (tracks and/or locomotives) owned by publicly traded US and Canadian railroad companies. The stock price reaction of the affected firms is examined following these disasters and a series of univariate and multivariate tests is used to investigate whether differences in abnormal returns following a given accident can be related to various factors that characterize the affected firm or the accident it was involved in. Findings The results suggest that legal liability considerations are one of the primary factors that determine a company's stock price reaction following a railroad disaster. Specifically, it is observed that firms that are likely to be sued in connection with an accident tend to incur larger stock price losses. On the other hand, it is found that firms that are protected through indemnification agreements suffer only insignificant price declines, even if initial accident reports hold them responsible for causing the accident. Originality/value The paper extends the prior literature on the stock market's reaction to firm-specific catastrophic events. While there are a number of studies that examine the financial consequences of aviation disasters, there is to the authors' knowledge only one prior study that performs a similar analysis for railroad accidents.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".