The Stock Price Performance and Return Style of the Pan-Infrastructure Reits Corporation: Evidence from U.S. and Japan Market
Bibliographic record
Abstract
The growth of big data analytics, cloud computing and 5G communication promotes the expansion of Pan-infrastructure REITs market. Despite previous studies confirmed the value-added role of pan-infrastructure REITs in a mixed-asset portfolio at the framework of mean-variance optimization, the anti-recession characteristics and stock-bond mixed feature of pan-infrastructure REITs is still scarcely investigated until now. In this paper, targeting at the U.S. and Japanese pan-infrastructure REITs market, we employ the capital asset price model (CAPM) and Sharpe model to conduct an empirical research to clarify the aforementioned issue, and the corresponding results indicate that in U.S. REITs market, the return style of new-infrastructure REITs corporation whose underlying asset covering data center, communication tower reveals the substantial anti-volatility characteristics under the increasing macroeconomic uncertainty, while the industrial REITs and infrastructure REITs corporation which belongs to the public utility sub-sector has revealed the completely opposite trend that the stock constituents account for a higher percentage of its return style, simultaneously. On the other hand, the results from horizontal comparison also suggest that the pan-infrastructure REITs corporation in Japan has more remarkable defensive characteristics with higher ratio of bond constituents than that in U.S. Such results uncover the impact of sectoral effect and market distinction on the stock price performance and return style of relevant pan-infrastructure REITs corporation in various countries and are also beneficial to the risk control activity of institutional investors.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".