### 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 language | English |
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Title of host publication | Lecture Notes in Engineering and Computer Science |

Pages | 1461-1466 |

Number of pages | 6 |

Volume | 2 |

Publication status | Published - 2012 |

Externally published | Yes |

Event | 2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, Hong Kong Duration: 2012 Mar 14 → 2012 Mar 16 |

### Other

Other | 2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 |
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Country | Hong Kong |

City | Kowloon |

Period | 12/3/14 → 12/3/16 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Computer Science (miscellaneous)

### Cite this

*Lecture Notes in Engineering and Computer Science*(Vol. 2, pp. 1461-1466)

**Robust-based random fuzzy mean-variance model using a fuzzy reasoning method.** / Hasuike, Takashi; Katagiri, Hideki; Tsuda, Hiroshi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Engineering and Computer Science.*vol. 2, pp. 1461-1466, 2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012, Kowloon, Hong Kong, 12/3/14.

}

TY - GEN

T1 - Robust-based random fuzzy mean-variance model using a fuzzy reasoning method

AU - Hasuike, Takashi

AU - Katagiri, Hideki

AU - Tsuda, Hiroshi

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Fuzzy reasoning method

KW - Portfolio selection problem

KW - Random fuzzy programming

KW - Robust programming

UR - http://www.scopus.com/inward/record.url?scp=84867483639&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867483639&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84867483639

SN - 9789881925190

VL - 2

SP - 1461

EP - 1466

BT - Lecture Notes in Engineering and Computer Science

ER -