Bibliographic record
Abstract
In this study the characteristics of REITs in Korea were investigated. REITs has been introduced and managed for almost 15 years in Korea. The research results show that K-REITs has higher return and lower risk structure than KOSPI, and higher return and higher risk than bond. These results indicate that we can attain portfolio effects by including REITs in an investment set. Regarding the correlation between return of K-REITs and that of stock and bond is smaller than that of the case of the U. S., which means there is possibility to attain more portfolio effects in Korea than the U. S. using REITs. Also the systematic risk of K-REITs is near zero. And alike from that of U. S., the asymmetric risk and return structure according to the market condition of K-REITs is not found. So if an investor analyzes the volatility of real estate market and the unique characteristics of REITs exquisitely and includes the REITs in his/her portfolio accordingly, he/she can achieve reduced risk and improve portfolio effects. In short, K-REITs can be very useful to diversify the investment and attain portfolio effects in the financial market.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".