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Sensitivity‐based fuzzy multi‐objective portfolio model with Value‐at‐Risk
Author(s) -
Zhang Huiming,
Watada Junzo,
Wang Bo
Publication year - 2019
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22986
Subject(s) - portfolio , fuzzy logic , portfolio optimization , econometrics , stock exchange , vector autoregression , computer science , fuzzy set , value at risk , sensitivity (control systems) , particle swarm optimization , mathematical optimization , mathematics , economics , risk management , financial economics , artificial intelligence , engineering , finance , electronic engineering
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.