CEO Overconfidence and its Relationship with Overinvestment in the Context of the COVID-19 Pandemic
Bibliographic record
Abstract
This paper primarily focuses on examining the correlation between CEO overconfidence and firm investment. Initially, I aimed to investigate whether there are any differences in confidence levels between male and female CEOs. The outcome of my research was that I was unable to identify any notable differences in the effects of CEO overconfidence on investment between male and female executives. This implies that regardless of gender, when a CEO displays overconfidence, they are likely to exhibit a similar inclination towards overinvestment. Additionally, my research focuses on the impact of the COVID-19 pandemic on CEO overconfidence and its subsequent influence on firm investment behavior. My study successfully establishes this connection. Specifically, in the current context of the COVID-19 pandemic, the relationship between CEO overconfidence and investment ratios is expected to weaken. However, the primary reason for this weakening effect is the volatile economic climate brought about by the COVID-19 era. The pandemic has significantly influenced companies to reduce their investments, resulting in the emergence of underinvestment as a prevalent issue, irrespective of the level of CEO confidence. In addition, my research reveals that a high level of CEO confidence is not able to substantially augment investment levels or adequately mitigate the problem of underinvestment in light of the substantial disruptions caused by the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".