Herding Behavior in Developed, Emerging, and Frontier European Stock Markets during COVID-19 Pandemic
Bibliographic record
Abstract
The behavior of market participants often does not rely on market signals, but replicates the investment decisions of other parties. The convergence of their investment behavior leads to the emergence of herd behavior with negative implications for financial stability. Moreover, this phenomenon may be even more pronounced in times of crisis. Although herding is an interesting topic which invites the interest of academic researchers, it still has not been sufficiently studied in terms of comparing the herd effect between differently developed stock markets. The first objective of this research was to determine the herd behavior during the COVID-19 pandemic using static and rolling regression analysis. The second objective was to investigate whether the herd behavior was triggered by the pandemic, while the third objective was to compare the differences in herd behavior between differently developed European stock markets. The results show that this phenomenon is most pronounced in emerging markets, followed by frontier markets and developed markets. Therefore, the results of this study are of particular importance for individual and institutional investors to achieve efficient risk diversification and for financial authorities to establish rules and avoid an increase in herd behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".