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2009 | 13 | 2 | 191-204
Article title

A FUZZY DECISION-MAKING APPROACH FOR PORTFOLIO MANAGEMENT WITH DIRECT REAL ESTATE INVESTMENT

Content
Title variants
Languages of publication
EN
Abstracts
EN
This study incorporated expert knowledge into the classical quadratic programming approach, i.e., Modern Portfolio Theory (MPT), through fuzzy set theory; in obtaining portfolio return optimization involving direct real estate investment. Two fuzzy mathematical programming models were uniquely specified and estimated in this study, namely, Zimmermann's (2001) fuzzy tactical asset allocation (FTAA) flexible programming model and Ramik and Rimanek's (1985) FTAA robust programming model. These approaches try to overcome the drawbacks of traditional asset allocation models by including expert adjustment in the presence of imprecise information. The findings suggest that the fuzzy tactical asset allocation (FTAA Flexible Model), with the inclusion of expert judgments which contain information usually not found in historical data, is able to produce a portfolio just as efficient as traditional asset allocation models while minimizing the potential issues due to imprecision and vagueness of information. Meanwhile, the FTAA Robust Model proffers a more evenly-distributed, yet with higher risks and lower returns, portfolio. Aside from the lack of emphasis on portfolio risks minimization, one reason attributed to such anomaly is the low level of returns of high-risk stocks that are not selected by MPT and FTAA Flexible Models. It results in a unique situation where portfolio diversification does not necessarily guarantee an efficient investment decision.
Contributors
  • Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
  • Eddie Chi Man Hui, Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
References
Document Type
Publication order reference
Identifiers
CEJSH db identifier
10LTAAAA080313
YADDA identifier
bwmeta1.element.5b3ccc22-15f7-39de-96e9-30a602d7321e
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