Bibliographic record
Abstract
In this dissertation, I explore the effects of exogenous shocks on firms' and managers' behaviors. The first essay examines the effect of shareholder-initiated litigation risk on opportunistic insider trading by exploiting US states' staggered adoption of Universal Demand (UD) laws, which weakened shareholders' ability to file derivative lawsuits against corporate insiders. I find that UD laws lead to significantly more profitable insider trades, specifically insider sales. After the adoption of UD laws, insider sales on average avoid an additional loss of about 2 percent ($24,000) per month in buy-and-hold abnormal returns. The benefit of UD laws is greater for insiders of firms where information asymmetry is high and where monitoring by institutional blockholders is low. Moreover, the greater profitability of insider trading after UD laws comes from more opportunistic timing of trades. For instance, insiders engage in more profitable insider trading, both purchase and sales during pre-QEA period after UD laws. Overall, this study suggests that a decrease in shareholder-initiated litigation threat increases more serious types of insider trading in US firms. The second essay examines the ex-ante risk of credit rating change on firms' payout polices. My results suggest that firms near a credit rating change pay less dividend yields and are less likely to pay dividends compared to other firms. I find that firms that are on the border of their rating categories (POM) and on investment-speculative cutoffs (IG/SG) on average pay 0.09% and 0.20% less dividend yields respectively in the next quarter than other firms with similar underlying credit quality. These results are novel and are not obvious predictions of traditional theories of dividends. Furthermore, I find that POM firms are less likely to initiate a dividend and increase dividend yields compared to other firms. My results indicate that POM and IG/SG firms pay less dividend yields in all industries and almost every year from 1986 to 2016. Overall, my results show that firms with similar ability to pay dividends can have significantly different dividend payouts in response to their risks of rating change.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".