Delay, deny, and defend: Public outrage at health insurance companies and stock market debacle
Bibliographic record
Abstract
This study investigates the stock market's response to the assassination of UnitedHealthcare's CEO, focusing on the cumulative abnormal returns (CARs) of publicly listed U.S. insurance firms. Using topic modeling on 59,644 Reddit comments, we identify and analyze key public narratives surrounding the incident, revealing nine topics including the themes of (1) public anger at the profit-driven practices of the insurance industry, (2) support for the shooter, criticism of the CEO, and (3) frustrations over healthcare costs and systemic inefficiencies. Sentiment analysis further shows that discussions are overwhelmingly negative, reflecting widespread dissatisfaction. Empirical analysis demonstrates that corporate characteristics such as profit, revenue growth, and media attention significantly amplify negative CARs, highlighting the market's sensitivity to perceptions of corporate profit and glamour. High executive compensation, particularly for CEOs, is also associated with more severe stock price declines, suggesting that leadership privileges intensify investor concerns. However, no evidence links CEO narcissism to negative stock impacts, indicating that public focus is more on systemic business practices than individual attitudes. Firms in the 'Hospital & Medical Service Plans' segment, including UnitedHealthcare and its peers, experienced the steepest declines immediately after the incident, reflecting heightened public discontent with companies closely tied to essential healthcare services. These findings support the views of 'social banditry theory and investor sentiment' and contribute to the broader debate between 'shareholder vs. stakeholder value maximization', emphasizing the risks of overlooking societal expectations.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".