Measuring and Explaining Changes in REIT Liquidity: Moving Beyond the Bid–Ask Spread
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Bibliographic record
Abstract
This paper investigates changes in REIT liquidity since the REIT boom of 1993. We use trade‐by‐trade data for REITs traded on the major U.S. exchanges to estimate and compare Kyle's (1985) measure of inverse liquidity for the 1993 and 1996 time periods. For our full sample of equity REITs, there is a significant increase in REIT liquidity in terms of the median price impact of trades. The increasing importance of the self‐advised, self‐managed organizational structure is found to be a major factor driving increased REIT liquidity. Our results imply a decline in the asymmetric information faced by market‐makers. Our investigation of the changes in the size distribution and resulting price impacts of REIT trades over the 1993–1996 period yields evidence of increased importance of informed traders to REIT price dynamics. Our findings of increased liquidity indicate that the increase in adverse‐selection costs due to the presence of more informed traders is more than offset by the increase in market thickness as a result of an increase in the number of uninformed (liquidity) traders.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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 it