Investors’ herd behavior related to the pandemic-risk reflected on the GCC stock markets
Bibliographic record
Abstract
The purpose of this study is to examine the causal association between expectations of pandemic risk and herding behavior.The study was undertaken in two stages.First, it was felt necessary to obtain a broad overview of the effect of the pandemic related risk of COVID-19 on investors' herding in the GCC.This was achieved by analyzing secondary data (i.e.daily historic prices on five GCC country market indices).In analyzing the secondary data, the study follows Christie and Huang (1995) and employs the cross-sectional standard deviation (CSSD) of returns to detect investors' herding behavior.Second, in an attempt to obtain a more precise understanding of the impact of pandemic related risk, a questionnaire survey was distributed and collected from 318 investors from the GCC stock markets.A confirmatory factor analysis (CFA) was also used as the primary analysis between the two variables: i.e. expectations of pandemic risk and herding behavior.The findings reveal that expectations of pandemic risk have a significant positive impact on the herding behavior in the GCC stock markets during the coronavirus crisis in the first quarter of 2020.Finally, the results of this study are robust to a range of model specifications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".