Distinct outside forces influencing firm outcomes: Social media and city crime
Bibliographic record
Abstract
Business entities face a multitude of external influences such as social, economic, and environmental factors. These outside forces have the capacity to instigate meaningful financial outcomes, emphasizing their importance for both the firms themselves and external stakeholders, namely, investors and regulatory authorities. In my dissertation, I explore two such outside yet discrete forces – social media engagement and city crime. Building on the concept of “online firestorms” that tweets can entice, I investigate a set of tweets sent by the S&P500 firms and their CEOs that the Twitterverse considers controversial. Consistent with social media’s cancel culture, I find evidence that the perceived controversial CEO tweets are associated with significant negative stock market reactions. The detrimental impact of CEOs’ controversial tweets is intensified for firms with “Star CEOs” and reversed for firms with high individual ownership. Using the same concept of “controversy” in firm tweets, I find contrasting outcomes that imply a positive significant reaction of controversial tweets on the stock market. This evidence complements inferences from previous studies that documented price-distortion behavior of social media messages firms post. Sentiment analyses of these firm-tweets reveal when the tweet contents are positively oriented, the market reacts more optimistically no matter the perceived controversy in the tweets. My dissertation expands the influence of outside forces by investigating crime rate in the city in which firms are headquartered. Using the Accounting and Auditing Enforcement Releases issued by the SEC to firms for fraudulent financial reporting and the city crime rates in the USA, I show evidence that indicates the higher the crime rate in the city in which firms are headquartered, the higher the likelihood of fraudulent reporting. Further, with subsample analysis, I demonstrate a nuanced influence of CEO compensation and of proximity to the SEC’s headquarter, on the relationship between crime and fraudulent financial reporting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".