<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/7716" mdate="2012-12-21 00:00:00"> <author>Thi T. Nguyen and Lee N. Gordon-Brown</author> <title>Fuzzy Numbers and MCDM Methods for Portfolio Optimization</title> <pages>1593 - 1605</pages> <year>2012</year> <volume>6</volume> <number>12</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/7716</ee> <url>https://publications.waset.org/vol/72</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>A new deployment of the multiple criteria decision making (MCDM) techniques the Simple Additive Weighting (SAW), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in this paper. Rather than exclusive reference to mean and variance as in the traditional meanvariance method, the criteria used in this demonstration are the first four moments of the portfolio distribution. Each asset is evaluated based on its marginal impacts to portfolio higher moments that are characterized by trapezoidal fuzzy numbers. Then centroidbased defuzzification is applied to convert fuzzy numbers to the crisp numbers by which SAW and TOPSIS can be deployed. Experimental results suggest the similar efficiency of these MCDM approaches to selecting dominant assets for an optimal portfolio under higher moments. The proposed approaches allow investors flexibly adjust their risk preferences regarding higher moments via different schemes adapting to various (from conservative to risky) kinds of investors. The other significant advantage is that, compared to the meanvariance analysis, the portfolio weights obtained by SAW and TOPSIS are consistently welldiversified.</abstract> <index>Open Science Index 72, 2012</index> </article>