{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:00:47Z","timestamp":1760238047281,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004515","name":"Universiti Kebangsaan Malaysia","doi-asserted-by":"publisher","award":["GUP-2019-038"],"award-info":[{"award-number":["GUP-2019-038"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019 selection preferences are single and unclear, when actually, in reality, investors\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/sym12071148","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:45:31Z","timestamp":1594374331000},"page":"1148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System"],"prefix":"10.3390","volume":"12","author":[{"given":"Siti Nazifah","family":"Zainol Abidin","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia"},{"name":"Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Melaka, Jasin Campus, Merlimau 77300, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4413-8776","authenticated-orcid":false,"given":"Saiful Hafizah","family":"Jaaman","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Munira","family":"Ismail","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad Syafadhli","family":"Abu Bakar","sequence":"additional","affiliation":[{"name":"Mathematics Division, Centre for Foundation Studies in Science, University of Malaya, Kuala 50603, Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"ref_1","first-page":"020178-1","article-title":"Mean-value at risk portfolio selection problem using clustering technique: A case study","volume":"2112","author":"Kumari","year":"2019","journal-title":"AIP Conf. 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