Outside Investor Access to Top Management: Market Monitoring versus Stock Price Manipulation
Bibliographic record
Abstract
In recent decades, an increase in the importance of intangible assets, especially in the technology sector, has reduced how much information stock market participants can take away from accounting numbers. This means market participants increasingly rely on managers of public firms to obtain information about the firms’ future performance. Managers often provide additional voluntary disclosure to market participants in the form of special reports or investor conference calls and presentations. This can make a firm’s stock price more informative, thereby strengthening manager incentives to increase the firm’s value. But it also gives managers the opportunity to influence the value of their pay tied to the firm’s stock price in a way that reduces the firm’s value. This trade-off is important and needs to be evaluated empirically. However, this cannot be done without first identifying the relevant economic channels in theory. To that end, this paper develops a model of firm-value maximization. The model shows how voluntary disclosure, manager compensation, manager stock-price manipulation, firm cost of capital and firm capital structure are related in equilibrium. A significant part of variation in top-manager pay is known to be unrelated to performance. I assume the reason for this is that managers differ in their ability to manipulate voluntary disclosure and thus the firm’s stock price. A key cross-sectional prediction is that voluntary disclosure is related negatively to the cost of capital but positively to manager manipulation. The analysis thus implies that cost of capital is not a good measure of frictions in accounting or governance.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".