Portfolio Diversification Strategy and the Impacts on the Middle East Real Estate Investment Decision
Bibliographic record
Abstract
This paper identified and examined the possible impacts of portfolio diversification strategy on the generated Property Companies’ (PCs) stocks returns, and the real estate industry performance and risk in the Middle East Real Estate Industry (MEREI) observed over the time period from Feb. 2008 to Feb. 2012. It is important to mention that there are two components of portfolio risk; the first one is non-systematic risk which can be diversified. The second one is systematic or non-diversifiable risk, which cannot be reduced by portfolio diversification; it is also called the market risk. Also, in order to measure the effectiveness of the portfolio, there are two critical variables must be considered, standard deviation and beta. The standard deviation reflects the unsystematic or company specific risk which can be avoided by diversification. However beta measures the type and degree of relationship between the company and the market, where it is very important for the investor to know how much the stock price will change due to a given change in the market. This paper clarifies the impact of diversification on the portfolio performance by including different companies from different sectors in one portfolio, and measuring both risk and return for this portfolio. This paper also aims to recommend the local and regional real estate industry investors as to how useful the diversification strategy is. The first impact considered in this paper is the independent relationship between the real estate portfolio diversification strategy and the PCs stocks returns generated by the portfolios from single stock to the portfolios of ten stocks. This study explains the second impact in terms of the relationship between the systematic risk (beta) of the PC stock and the degree of its correlation with the local and regional markets. The impact of portfolio diversification strategy on the non-systematic risk (standard deviation) is also being considered in this paper. Hence, by increasing the number of PCs stocks in the portfolio this risk can be eliminated. The final issue that the paper addresses is the advices to the investors that the portfolio diversification is a passive strategy and to secure maximum returns and lowest risk, which it is necessary to actively monitor their real estate portfolio and switch among investments if necessary. This study concluded that the investment from the ninth asset portfolio holds only the systematic risk of 0.005%. At this point the diversifiable risk is zero and the only risk that is relevant is the systematic or non-diversifiable or the company specific risk of 0.005% which cannot be eliminated even if an 11th asset is added to the portfolio. Also the study concluded that it is evident that there is no distinct relationship between expected return and the number of real estate assets held in the portfolio. In other words, the principle of diversification has nothing to do with the returns that the real estate assets in the portfolio generate together. In fact, it is the correlation of the return of these listed property companies with the real estate markets in the Middle East. The study recommended the investors in the Middle East to actively review their Real Estate portfolio and interchange the combinations of the assets in forming the portfolio. This may result earning a positive return from their Real Estate investment. Moreover we advise shareholders to not completely rely on the passive strategy of Real Estate portfolio diversification.
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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 | 0.003 |
| 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 | 0.001 |
| 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".