eprintid: 11171
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/11/71
datestamp: 2023-11-10 03:25:42
lastmod: 2023-11-10 03:25:42
status_changed: 2023-11-10 01:14:37
type: article
metadata_visibility: show
creators_name: Zhang, H.
creators_name: Watada, J.
creators_name: Wang, B.
title: Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk
ispublished: pub
keywords: Electronic trading; Genetic algorithms; Particle swarm optimization (PSO); Reactive power; Sensitivity analysis; Uncertainty analysis; Value engineering, Calculation formula; Degree of stability; fuzzy VaR; Fuzzy variable; Improved particle swarm optimization algorithms; New York Stock Exchange; Portfolio model; Portfolio selection, Financial markets
note: cited By 1
abstract: To quantitatively discuss the effects and uncertainties of yield changes in each stock for portfolio selection results and then to provide a more reliable portfolio solution for investors, sensitivity analysis is introduced to improve the multi-objective portfolio model with fuzzy VaR (SA-VaR-FMOPSM). Compared with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM), the calculation formulas of expected and VaR value of parabolic fuzzy variable are derived when stock yields are set as a more generalized parabolic fuzzy variable as well as the sensitivity of the total objective to the VaR-FMOPSM model is defined. Meanwhile, based on the fuzzy simulation technique, the model adapted to a series of different distributed fuzzy variable, an improved particle swarm optimization algorithm (IPSO) is used for numerical simulations. To illustrate the proposed model, New York Stock Exchange and National Association of Securities Dealers Automated Quotations-Global Select Market (NASDAQ-GS) stock data were selected for empirical testing, and to better reflect the true value of each stock for each day, we selected the ex-right price data in the experiment, a comparison with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM) is performed. The results show that the fuzzy VaR multi-objective portfolio model based on the sensitivity analysis method can effectively identify and quantitatively analyze the sensitivity of individual stocks to yield changes, i.e. that can identify which stock has more stable yields and that can calculate the degree of stability, then to obtain stable portfolio solutions. In addition, compared with the VaR-FMOPSM model, our sensitivity-based improved model with the IPSO algorithm also performs better than Genetic Algorithm and Simulate Anneal Algorithm (SA), it provides the same performance on this point. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
date: 2019
publisher: John Wiley and Sons Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074183061&doi=10.1002%2ftee.22986&partnerID=40&md5=6ade6285d9b68876e950fc1a46fef4e6
id_number: 10.1002/tee.22986
full_text_status: none
publication: IEEJ Transactions on Electrical and Electronic Engineering
volume: 14
number: 11
pagerange: 1639-1651
refereed: TRUE
issn: 19314973
citation:   Zhang, H. and Watada, J. and Wang, B.  (2019) Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk.  IEEJ Transactions on Electrical and Electronic Engineering, 14 (11).  pp. 1639-1651.  ISSN 19314973