<scp>CEO</scp> Overconfidence and Stock Price Crash Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study examines the association between chief executive officer ( CEO ) overconfidence and future stock price crash risk. Overconfident managers overestimate the returns to their investment projects and misperceive negative net present value ( NPV ) projects as value creating. They also tend to ignore or explain away privately observed negative feedback. As a result, negative NPV projects are kept for too long and their bad performance accumulates, which can lead to stock price crashes. Using a large sample of firms for the period 1993–2010, we find that firms with overconfident CEO s have higher stock price crash risk than firms with nonoverconfident CEO s. The impact of managerial overconfidence on crash risk is more pronounced when the CEO is more dominant in the top management team and when there are greater differences of opinion among investors. Finally, it appears that the effect of CEO overconfidence on crash risk is less pronounced for firms with more conservative accounting policies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it