Bibliographic record
Abstract
The popularity of REIT in both the developed and emerging markets of the world attests to the significance of REIT in worldís economies and investment choices. The relative ease with which investors can indirectly invest in real property is transforming portfolio asset allocation. While the place of REIT in a mixed asset portfolio is of interest to investors, it is equally important for investors to know the cross market dynamics of the REIT markets so that they can make informed investment decisions especially with regard to diversifying their portfolios. Therefore, this paper examines the diversification benefits from investing in the sampled REIT markets of US, Canada, Belgium, South Africa, New Zealand, Australia, Hong Kong, Japan, South Korea, Malaysia and Singapore from 1987 to 2006. The focus is on the diversification benefits from extending Singapore REIT portfolio into the sampled REIT markets. Co-integration methodology is used to analyze both long and short term dynamics of the sampled markets. This is followed by F-test to ascertain the statistical significance of any improvement in performance resulting from extending a Singapore REIT portfolio into the REIT markets. The results show that Singapore REIT market is not co-integrated, in the long term, with any of the REIT markets except Canada. Furthermore there is no evidence of Granger causality between Singapore and the other markets (except Japan) over the short term. These findings imply apparent diversification benefits from extending Singapore REIT portfolio into the sampled REIT markets. The diversification benefits are found to be statistically significant.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".