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Possibilistic Decision-Making Models for Portfolio Selection Problems

  • Peijun Guo
Part of the Intelligent Systems Reference Library book series (ISRL, volume 33)

Abstract

The basic assumption for using probabilistic decision-making models for portfolio selection problems, such as Markowitz’s model, is that the situation of stock markets in future can be correctly reflected by security data in the past, that is, the mean and covariance of securities in future is similar to the past one. It is hard to ensure this kind of assumption for the real ever-changing stock markets. Possibilistic decision-making models for portfolio selection problems are based on possibility distributions, which are used to characterize experts’ knowledge. A possibility distribution is identified using the returns of securities associated with possibility grades provided by experts. Based on the obtained possibility distribution, we construct a possibilistic portfolio selection decision-making model as a quadratic programming problem. Because experts’ knowledge is very valuable, it is reasonable that possibilistic decision-making models are useful in real investment environment.

Keywords

Stock Market Portfolio Selection Portfolio Return Possibility Distribution Portfolio Selection Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Peijun Guo
    • 1
  1. 1.Faculty of Business AdministrationYokohama National UniversityHodogaya-KuJapan

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