Social Factors and Herd Behaviour in Developed Markets, Advanced Emerging Markets and Secondary Emerging Markets
Bibliographic record
Abstract
This paper examines the existence of herd behaviour in fifteen (15) global stock markets, which consist of Developed Markets (Canada, Hong Kong, Japan, Singapore and the United Kingdom), Advanced Emerging Markets (Brazil, Malaysia, Mexico, Poland and South Africa) and Secondary Emerging Markets (Chile, China, Indonesia, the Philippines and Russia) by using Cross Sectional Absolute Deviation (CSAD) method of Chiang and Zheng (2010). It also seeks to explore the impact of social factors such as prosperity, education, ageing society, industry orientation and gender on the existence of market-wide herding. The findings of this paper indicate that herd behaviour exists in Singapore (Developed Market), Mexico, Poland and South Africa (Advanced Emerging Markets) and China and the Philippines (Secondary Emerging Markets). No evidence of herding is observed for Canada, Hong Kong, Japan, United Kingdom, Brazil, Malaysia, Chile, Indonesia and Russia. Ageing society is also found to have significant impact on the existence of herd behaviour. Nonetheless, prosperity, education, industry orientation and gender are found to be insignificant to herding. This study sheds some light on whether social factors determine herding behaviour in the 15 selected stock markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".