Does an overconfidence bias affect stock return, trading volume, and liquidity? Fresh insights from the G7 nations
Bibliographic record
Abstract
The article extends the empirical literature on overconfidence bias in G7 stock markets during pre- and post-COVID-19 and provides additional evidence. Using vector autoregression and impulse response functions (IRFs), we analyze the overconfidence bias for the daily data from January 2015 to December 2021. Because the pertinent coefficients are positive and highly significant for only a few lags, there is a strong contemporaneity between market volume and market return in the pre-COVID-19 period of the Canadian and Italian stock markets. The study shows compelling evidence of overconfident behavior in the Italian market during the COVID-19 crisis. Along with trading volume, market liquidity influences overconfidence bias, which tracks market return but not vice versa. For investors, decision-makers, and market regulators, the study has significant ramifications in the current market turbulence caused by the COVID-19 pandemic. Furthermore, overconfidence contributes to the reported extra unpredictability due to the high level of sensitive data.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".