Partial similarity measure of uncertain random variables and its application to portfolio selection

Article type: Research Article

Authors: Gao, Rong^{a; *} | Ahmadzade, Hamed^b | Rezaei, Kamran^c | Rezaei, Hassan^c | Naderi, Habib^b

Affiliations: [a] School of Economics and Management, Hebei University of Technology, Tianjin, China | [b] Department of Mathematical Sciences, University of Sistan and Baluchestan, Zahedan, Iran | [c] Department of Computer Science, University of Sistan and Baluchestan, Zahedan, Iran

Correspondence: [*] Corresponding author. Rong Gao, School of Economics and Management, Hebei University of Technology, Tianjin 300401, China. E-mail: [email protected].

Abstract: A similarity measure determines the similarity between two objects. As important roles of similarity measure in chance theory, this paper introduces the concept of partial similarity measure for two uncertain random variables. Based on maximum similarity principle, partial similarity measure are used to recognize pattern problems. As an application in finance, partial similarity measure is applied to optimize portfolio selection of uncertain random returns via Monte-Carlo simulation and craw search algorithm.

Keywords: Chance theory, uncertain random variable, partial similarity measure, portfolio selection, pattern recognition

DOI: 10.3233/JIFS-190942

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 155-166, 2020