<scp>Short‐Termist CEO</scp> Compensation in Speculative Markets: A Controlled Experiment*
Bibliographic record
Abstract
ABSTRACT Bolton, Scheinkman, and Xiong (2006) model a setting where investors disagree and short‐sales constraints cause pessimistic views of stock prices to be less influential, which leads to speculative stock prices. A theoretical implication of the model is that existing shareholders can exploit the speculative stock prices by (i) designing managerial compensation contracts that encourage short‐term performance, and (ii) subsequently selling their shares to more optimistic investors. We document empirical support for this theory by finding that an exogenous removal (Regulation SHO) of short‐sales constraints curbs the provision of short‐term incentives, an effect reflected in longer CEO compensation duration. The effect is concentrated among stocks with high investor disagreement and short‐term‐oriented institutional ownership. Consistent with prior work, we also find that longer CEO compensation duration leads to longer CEO investment horizons, less overinvestment, and less earnings management. Collectively, our results speak to the contributing role of speculative stock prices in corporate short‐termism. Finally, our study implies that effective policies to curb corporate short‐termism should address stock market speculation and promote mechanisms that tie executive compensation to longer‐term stock price performance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".