Clustering Real Estate Investment Trusts: Brazil versus United States

Gabriel, Fernanda Sousa; Post, Kárem Cristina de Sousa Ribeiro; Rogers, Pablo

doi:10.5296/jmr.v7i4.7848

Cited by 1 publication

(5 citation statements)

References 23 publications

(9 reference statements)

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“…For step 3, processes such as fuzzy rule base, fuzzy inference system, and defuzzification were performed. Fuzzy rule bases were developed based on the results of [3,4,7,8] and characterized by IF THEN rules. These rule bases were then aggregated in the fuzzy inference system, and the products were converted into crisp values that represent stock performance.…”

Section: Research Formulationmentioning

confidence: 99%

“…In this step, fuzzy rule bases were developed to aggregate the inputs considered. These rule bases were obtained based on the results achieved from past known works [3,4,7,8]. In total, there were 125 rule bases developed, utilizing the five linguistics terms for the input variables and the four linguistic terms for the output variables defined in step 2.…”

Section: Step 3: Fuzzy Rule Base Fuzzy Inference System and Defuzzimentioning

confidence: 99%

“…In the literature of modern portfolio theory, the initial stock selection approach developed for investment focuses on clustering stocks based on risk tolerance and return expectations [3,[5][6][7][8]. When employing this approach, risk is measured using variance, while asset return data are assumed to be normally distributed.…”

Section: Introductionmentioning

confidence: 99%

“…The established clustering analysis-based methods, such as k-means and hierarchical, generally cluster stocks based on the averages of variables involved, where stocks with similar average scores are presumed as having the same performances [4]. Among the advantages of using these methods in stock clustering are the reduced time taken to develop the optimal portfolio given many diverse stocks, the important investment-related information made available to investors, and the minimal risk level obtained on the optimal portfolio [7,8,11].…”

Section: Introductionmentioning

confidence: 99%

“…The established clustering methods in the literature consider several options to ensure that the stocks preferred by investors are good and the best to invest in. Among investors' preferences studied are stocks that have high return rates, low standard deviation and high Treynor index values [3,4], low standard deviations, moderate return rates, moderate turnover ratios, and moderate Sortino index values [7], or high return rates, high Sharpe index values, high appraisal ratios, high Sortino index values, low standard deviations, and downside risks [8]. However, investors' preferences considered by these methods are ambiguous, since such evaluation focuses only on one unknown investor's selection preference when clustering stock performance.…”

Section: Introductionmentioning

confidence: 99%

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Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System

et al. 2020

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The fact that many stocks are traded in the marketplace makes the selection process of choosing the right stocks for investment crucial and challenging. In the literature on stock selection, cluster analysis-based methods have usually been used to group and to determine the best stock for investment. Many established cluster analysis-based methods often cluster stocks under consideration using the average of the variables, where stocks with similar scores are concluded as having the same performances. Nevertheless, the performance results obtained do not reflect the actual performance of the stocks. Depending only on the average score of each variable is inefficient, as market situations usually involve uncertain extreme values. Moreover, when grouping stock performance, the established clustering methods assume that investors’ selection preferences are single and unclear, when actually, in reality, investors’ selection preferences vary; some investors are pessimistic, while others may be more optimistic. Due to this issue, this paper presents a novel fuzzy clustering method using a fuzzy inference system to flexibly assess the consistent evaluations given to stock performance that differentiate between pessimistic and optimistic investors that are symmetrical in nature. All variables considered in this study were defined in terms of linguistic inputs, where the consensus among them was aggregated using rule bases. These rule bases provide assistance in obtaining the linguistic output, which is the actual performance of the stock. Next, each stock under consideration was ranked using the proposed novel stock selection strategy based on investors’ confidence levels and preferences. The proposed method was then applied to a case study of 30 Syariah stocks listed on the Malaysian stock exchange, where the results obtained were empirically validated with established cluster analysis-based methods.

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Section: Research Formulationmentioning

confidence: 99%