<scp>CEO</scp> Overconfidence and Stock Price Crash Risk
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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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".