Journal of Applied Mathematics and Decision Sciences
Volume 2009 (2009), Article ID 897024, 20 pages
doi:10.1155/2009/897024
Research Article

A New Decision-Making Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment

1Department of Information Management, National United University, Miao-Li 36003, Taiwan
2Graduate Institute of Management, National United University, Miao-Li 36003, Taiwan

Received 30 November 2008; Revised 18 March 2009; Accepted 13 May 2009

Academic Editor: Lean Yu

Copyright © 2009 Chen-Tung Chen and Wei-Zhan Hung. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decision-makers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2-tuple linguistic variable are suitable used to express experts' opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decision-making method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.