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Record W2750713623

Does Real Estate Fund in Thailand Provide Diversification Benefits for Stock Investment

2016· article· en· W2750713623 on OpenAlexaff
Nopphon Tangjitprom, Preechaya Chavalittumrong, Veeranuch Leelalai

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsAssumption University
Fundersnot available
KeywordsReal estateReal estate investment trustBusinessCapitalization rateDiversification (marketing strategy)Fund of fundsFinancePortfolioOption feeClosed-end fundAlternative investmentStock (firearms)Financial economicsEconomicsMarket liquidityMarketing
DOInot available

Abstract

fetched live from OpenAlex

The real estate investment has been an alternative investment as a diversifier for traditional invest- ment like the stock market. Previous studies have shown the diversification benefits of real estate investment for stock portfolios. This paper aims to examine whether and to what extent real estate funds can provide diversification benefits to investors. The information of stock returns and real estate funds in Thailand was gathered for the period during 2007 and 2015. The results show that there is no diversification benefit in terms of hedge or safe haven from real estate funds in Thailand. However, the average beta of real estate funds is relatively low and the risk-return performance of real estate funds remains attrac- tive to be included in the portfolio.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.030
GPT teacher head0.221
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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