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Soft Computing
Erschienen in: Soft Computing 12/2019

27.02.2018 | Methodologies and Application

A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms

verfasst von: Mohuya B. Kar, Samarjit Kar, Sini Guo, Xiang Li, Saibal Majumder

Erschienen in: Soft Computing | Ausgabe 12/2019

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Abstract

In this paper, a new bi-objective fuzzy portfolio selection model is proposed, for which Sharp ratio (SR) and Value at Risk ratio (VR) of a portfolio are chosen as objectives. SR is an important nonsystematic risk measurement that examines the investment risk by aspiring the diversification of the capital allocation. On the other hand, VR measures the systematic risk, which reduces the largest loss of an investment at a given confidence level. The proposed fuzzy portfolio model assumes both SR and VR as maximization objectives for which the associated fuzzy parameters are considered as triangular fuzzy numbers. The proposed model is solved using multi-objective genetic algorithms, namely multi-objective cellular genetic algorithm (MOCell), archive-based hybrid scatter search (AbYSS), and nondominated sorting genetic algorithm II (NSGA-II). We have used a data set from the Shenzhen Stock Exchange to illustrate the performance of the proposed model and algorithms. Finally, a comparative study in terms of five standard performance metrics is presented, among the MOCell, AbYSS, and NSGA-II algorithms that are mentioned extensively in various research articles to exhibit the best suitable algorithm.
Vorheriger Artikel Multiply information coding and hiding using fuzzy vault
Nächster Artikel An elitism-based self-adaptive multi-population Jaya algorithm and its applications

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Metadaten
Titel
A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms
verfasst von
Mohuya B. Kar
Samarjit Kar
Sini Guo
Xiang Li
Saibal Majumder
Publikationsdatum
27.02.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 12/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3094-0

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