Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem

Abstract

On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value‐at‐risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto‐optimal‐set‐based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Set-Based Particle Swarm Optimization for Portfolio Optimization

“…In contrast, PSFGA was the worst performing algorithm. Wang and Watada made improvements to the MOPSO algorithm that were shown to be advantageous in nding a diverse POF [178].…”

“…Optimization of R and σ are the most widely considered objectives for multi-objective portfolio optimization. Additional objectives, such as liquidity [192], future risk [165], [178], and transaction costs [116] are largely under represented yet vital when considering portfolios. Cardinality, as an additional objective to be minimized, is a novel approach to obtaining a diverse POF of dierently size portfolios [2].…”

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

“…A optimization method based on particle swarm optimization is given in [26], has applied to portfolio selection. Wang et al [27] proposed a novel model for multiple objectives using fuzzy logic for solving a portfolio selection problem with alternative risk measure and the existing particle swarm optimization is also modified in the model. A multiple stage adaptive optimization model to portfolio optimization problem is introduced in [28].…”

Impact Evaluation of Exit Strategy in Fuzzy Portfolio‐based Investment

“…The triangular, trapezoidal, and Gaussian fuzzy variables are the most frequently used fuzzy variables, which can simulate several distributions under uncertainty, and they have been applied in some existing publications [7,26]. Therefore, in this paper, we use these three types of fuzzy variables to describe the security future returns, as listed in Table .…”