Robust-based random fuzzy mean-variance model using a fuzzy reasoning method

Takashi Hasuike, Hideki Katagiri, Hiroshi Tsuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper considers a robust-based random fuzzy mean-variance portfolio selection problem using a fuzzy reasoning method, particularly a single input type fuzzy reasoning method. Capital Asset Pricing Model is introduced as a future return of each security, and the market portfolio is assumed to be a random fuzzy variable whose mean is derived from a fuzzy reasoning method. Furthermore, under interval inputs of fuzzy reasoning method, a robust programming approach is introduced in order to minimize the worst case of the total variance. The proposed model is equivalently transformed into the deterministic nonlinear programming problem, and so the solution steps to obtain the exact optimal portfolio are developed.

Original languageEnglish
Title of host publicationInternational MultiConference of Engineers and Computer Scientists, IMECS 2012
PublisherNewswood Limited
Pages1461-1466
Number of pages6
ISBN (Print)9789881925190
Publication statusPublished - 2012
Event2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, Hong Kong
Duration: 2012 Mar 142012 Mar 16

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2196
ISSN (Print)2078-0958

Other

Other2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012
CountryHong Kong
CityKowloon
Period12/3/1412/3/16

Keywords

  • Fuzzy reasoning method
  • Portfolio selection problem
  • Random fuzzy programming
  • Robust programming

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Fingerprint Dive into the research topics of 'Robust-based random fuzzy mean-variance model using a fuzzy reasoning method'. Together they form a unique fingerprint.

  • Cite this

    Hasuike, T., Katagiri, H., & Tsuda, H. (2012). Robust-based random fuzzy mean-variance model using a fuzzy reasoning method. In International MultiConference of Engineers and Computer Scientists, IMECS 2012 (pp. 1461-1466). (Lecture Notes in Engineering and Computer Science; Vol. 2196). Newswood Limited.