Stock Market Response to Regulatory Reports of Deceptive Advertising: The Moderating Effect of Omission Bias and Firm Reputation
Bibliographic record
Abstract
Whereas a growing body of research has examined the consumer-related implications of deceptive advertising, the stock market consequences stemming from the regulatory exposure of such infractions remain largely unexplored. In a step to address this gap, the current research examines the effect of regulatory reports of misleading ads on firm stock prices. Results from an event study, focusing on the pharmaceutical industry as the empirical context, show an average abnormal return of −0.91% associated with regulatory reports of deceptive advertising. Analysis of the abnormal returns, however, reveals that the stock market response to these reports is shaped by omission bias, in that investors penalize commission violations more than omission violations. Furthermore, firm reputation is found to moderate the penalty for commission violations. In addition, two experiments examine the effect of such violations on investor beliefs. The first helps elucidate the process mechanism underlying the observed stock market effects and the second provides insights regarding the reputation-omission bias interaction for firms committing repeat violations. Overall, our findings provide important theoretical, managerial, and public policy implications regarding the role of financial markets in regulating deceptive ad practices.
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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 | 0.010 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".