Size and Calender Anomalies: The Case of International Property Shares
Bibliographic record
Abstract
Price anomalies have being intriguing both financial professionals and academics for many years. Academics like to think that asset pricing models have matured sufficiently in that enable those who use them to price assets according to the risks that are being modelled. However, at the same time we continuously observe price behaviour that cannot be attributed to the identified drivers in our theoretical frameworks. These repeating price irregularities, anomalies, create puzzles that are in need of solution, or at least an explanation. Recent studies in the mainstream finance literature show that some of these anomalies have either disappeared or been reversed in the general stock market. This change is often to changes in the market depth, the increase of institutional involvement and the rise of cross-border trading. For real estate shares the issue has been analyzed as well by several authors. However, so far these analyses have been limited to U.S. REITs and not considering potential time variations in price anomalies. In order to assess the effect of market maturity, liquidity and institutional involvement an international scope grants possibilities for new and valuable insights in the matter. Therefore we will study daily price returns of all real estate shares traded on the ten most prominent financial markets in the world: U.S., Canada, U.K., France, Germany, Spain, The Netherlands, Australia, Japan and Singapore. We analyze a period that dates back to 1985 and we will explicitly focus on variations over time.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".