An Empirical Investigation of Herding Behavior in the U.S. REIT Market
Bibliographic record
Abstract
Our study investigates the market-wide herding behavior in the U.S. equity REIT market. Utilizing the quantile regression method, we find that herding is more likely to be present in the high quantiles of the REIT return dispersion. This implies that REIT investors tend to herd under turbulent market conditions. Our results also support the asymmetry of herding behaviors, that is, herding is more likely to occur and becomes stronger in declining markets than in rising markets. In addition, our findings show that the current financial crisis has caused a change in the circumstances under which herding can occur, as we find that during the current crisis REIT investors may not start to herd until the market becomes extremely turbulent whereas during the relatively normal period before the crisis, investors tend to herd when the market is moderately turbulent. Finally, we find that compared with the case of the 'pre-modern' era, REIT investors are more likely to herd in the 'modern' era, during which herding usually occurs when the market becomes tumultuous. This implies that the switch of REITs from passive externally managed entities into active self-managed ones has made the investors more responsive to market sentiment. © 2011 Springer Science+Business Media, LLC.
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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 | 0.005 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".