Investor overconfidence and stock price crash risk
Bibliographic record
Abstract
Purpose The paper investigates how investor overconfidence affects stock price crash risk. Design/methodology/approach Following Adebambo and Yan (2018), we use mutual fund data from Thomson Financial, CRSP Survivorship Bias Free Mutual Fund Database and Morningstar Direct to construct our investor overconfidence proxy. We then conduct our analysis using the regression method in the US market for the sample period between 1988 and 2018. Findings We find that managers, to respond to unrealistic expectations from overconfident investors, are more likely to withhold bad news and overinvest, which increases stock price crash risk. Furthermore, firms with overconfident investors are more likely to have breaks in a string of consecutive earnings increases and engage in earnings management. Employing the Regulation SHO Pilot Program (Russell Index Reconstitution) as exogenous shocks to valuation (the participation of active investors), we find that the relation between investor overconfidence and stock price crash risk is more pronounced among overvalued firms (firms with a higher share of active investors). Finally, we show that strong corporate governance can discipline managers against catering for overconfident investors. Originality/value While existing literature has focused on how investor overconfidence increases stock price crash risk via excessive trading activities, we show a very different mechanism through firm's disclosure response to investor overconfidence.
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How this classification was reachedexpand
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 | 0.001 | 0.011 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".