Does Managerial Overconfidence Change with Market Conditions? Risk Management for Financial Institutions
Bibliographic record
Abstract
Overconfidence (hubris or overestimation of one’s ability to perform) has been viewed in the finance literature as a character trait that is stable over time, e.g., assuming that if a manager is overconfident, he/she is overconfident all the time. In this paper, we aim to show that managerial overconfidence can be state-contingent, i.e., the level of managerial overconfidence could be influenced by an external economic shock such as the global financial crisis in 2008. A novelty of this paper is to provide evidence for and application of the concept of state-based managerial overconfidence, which is new in the finance literature. Two empirical studies were reported. In the first study (Study 1), we analyzed real market data by linear regression. We found that managerial overconfidence could vary according to changes in the state of the macroeconomy or tightening of corporate governance policies. In the second study (Study 2), we conducted a lab experiment simulating how external manipulations could alter participants’ confidence level. Both our empirical studies provide strong evidence of state-contingent overconfidence by Student’s t-test and contribute to the current finance literature, which assumes overconfidence as a personality trait. Our findings have important practical implications for the credit market. According to the state-contingent overconfidence hypothesis, creditors might reduce the loan amount or the loan duration (or other loan contract terms) too excessively by more than the efficient level during an economic downturn if the offsetting effect of state-contingent overconfidence is ignored.
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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 | 0.004 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".