Fuzzy portfolio optimization wang shouyang lai kin keung fang yong
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We discusses an interval portfolio selection model with liquidity constraints. The chapter is organized as follows. Konno and Yamazaki Konno, H. This chapter is organized as follows. Contents: Literature Review: Survey for Portfolio Selection Under Fuzzy Uncertain Circumstances -- Portfolio Selection Models Based on Fuzzy Decision Making -- Fuzzy Decision Making and Maximization Decision Making -- Portfolio Selection Model with Fuzzy Liquidity Constraints -- Ramaswamy's Model -- LeĚon-Liern-Vercher's Model -- Fuzzy Semi-absolute Deviation Portfolio Rebalancing Model -- Fuzzy Mixed Projects and Securities Portfolio Selection Model -- Portfolio Selection Models with Interval Coefficients: Linear Programming Model with Interval Coefficients -- Quadratic Programming Model with Interval Coefficients -- Portfolio Selection Models with Possibility Distribution: Tanaka and Guo's Model with Exponential Possibility Distributions -- Carlsson-FulleĚr-Majlender's Trapezoidal Possibility Model -- Center Spread Model in Fractional Financial Market -- Fuzzy Passive Portfolio Selection Models -- Fuzzy Index Tracking Portfolio Selection Model -- References. To solve the problem, we rely on a specially developed algorithm that hybridizes fuzzy simulation and real-coded genetic algorithm.

Some fundamental issues and problems of portfolio selection have been studied systematically and extensively by the authors to apply fuzzy systems theory and optimization methods a new framework for investment analysis is presented in this book. A new framework for investment analysis is presented in this book. Portfolio construction based only on risk and return results in major exposure to extreme returns and unsatisfactory portfolio out of sample results. Logical clustering method for portfolio selection employs interpolative Boolean algebra dissimilarity within a hierarchical clustering algorithm to group companies according to investment ratios. In some cases, for instance, historical data of stocks are not available. The return is measured by the mean, and the risk is measured by the variance, however, there are many different ways kurtosis, skewness of measuring risk. We assume that the transaction costs are paid on the basis of incremental discounts and are adjusted in the net return of the portfolio.

Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. It also allows you to accept potential citations to this item that we are uncertain about. In addition, the diversity of the underlying copulas facilitates the challenge of the diversity of investors with different beliefs for expectations. Considering the stochastic exchange rate, a four-factor futures model with the underling asset, convenience yield, instantaneous risk free interest rate and exchange rate, is established. It is based on a combination of soft computing techniques for dealing with practical and computation aspects of this problem. KeywordsEnhanced indexationâSoft computingâCardinality constraintsâEvolutionary optimisationâSimulated annealingâGenetic algorithmsâParticle swarm optimisationâStochastic convergence In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem.

In Section 2, we present a bi-objective programming model for the index tracking portfolio selection problem. We study a static portfolio selection problem, in which future returns of securities are given as fuzzy sets. Logical clustering and logical DuPont methods have proven to be valuable fuzzy tools for an automated stock selection. This is the first monograph on fuzzy portfolio optimization by using fuzzy mathematical approaches quantitative analysis qualitative analysis the experts knowledge and the investors subjective opinions can be better integrated into portfolio selection models. Despite many proposed alternatives, the predominant model in portfolio selection is still mean-variance. Then, we put forward a two-stage optimization algorithm to solve the multi-objective portfolio optimization model.

Most of the existing portfolio selection models are based on the probability theory. If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. In the absence of reported work for a complex problem of such a nature and scale, two metaheuristic strategies chosen from two different genres of evolutionary algorithms, viz. This paper presents linear programming problems with all the parameters are fuzzy. Logical DuPont method for portfolio selection is applied to give insight into the structure of companyĂs earnings and further identify profit drivers to recommend investment actions. We have no references for this item. The proposed hybrid optimization technique offers significant improvements in managing investments in a stock portfolio under volatile and uncertainty stock market without the need for human intervention, with diversification procedure, and thus provides acceptable returns with minimal risks.

The investors want to obtain a return similar to that of the benchmark, through index tracking investment. To find whether it is available, there are three options: 1. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. The fuzzy set theory has been widely used to solve many practical problems, including? In some cases, investors may consider other factors such as liquidity. Both methods are extended to address portfolio selection problems. .

It also better reflects the preferences of risk-averse investors. You can help correct errors and omissions. Check below whether another version of this item is available online. Some concluding remarks are given in Section 5. Portfolio selection approaches using possibilistic programming and interval programming are reviewed in Sections 3 and 4, respectively.

A series of portfolio selection models are given and some of them are more efficient for practical applications. The contents of this book mainly comprise of the authors' research results for fuzzy portfolio selection problems in recent years. In this paper, we propose a bi-objective mixed asset portfolio selection model involving projects as well as securi-ties. These considerations are illustrated by means of extensive numerical case study This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. As a second stage, expected return of this model is assumed to be fuzzy.