Bibliographic record
Abstract
Herding behavior was concluded to exist in some sectors and under some market conditions in the Jordanian stock market when measured using the cross-sectional absolute deviation. The purpose of this study was to retest the existence of the sectoral herding using the cross-sectional dispersion of betas and compare the results with those reached using the measure of the cross-sectional absolute deviation. Behavioral finance theory represents the main base on which this study was built. In this study, the researcher tried to answer questions related to whether herding behavior exists in the Jordanian market and its sectors if measured using cross-sectional dispersion of betas and whether results will be different from those reached using other measures. In this quantitative study, data from Amman stock exchange were used and the period covered was from 2000 to 2018. These data were used to calculate the cross-sectional dispersion of betas which was tested using t-test, Kruskal–Wallis test, Mann-Whitney U test, and Wilcoxon Signed-Rank test. Results indicated that herding behavior existed in market and in each sector at the same level which was not affected by the financial crisis. Furthermore, the study revealed that herding level was the same when the market (sector) was rising and when it was falling and this similarity has not been changed by the occurrence of the global financial crisis. Finally, results indicated that herding was at its lowest level in the entire market and in the industrial sector during the time of financial crisis. These results are different from those of the study conducted in Jordan using cross-sectional absolute deviation which implies that using different herding measures yields different results.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".