Fuzzy dynamic portfolio selection for survival

Authors:
Jinli Zhang;Wansheng Tang;Cheng Wang;Ruiqing Zhao
Affiliations:
Institute of Systems Engineering, Tianjin University, Tianjin, China;Institute of Systems Engineering, Tianjin University, Tianjin, China;Institute of Systems Engineering, Tianjin University, Tianjin, China;Institute of Systems Engineering, Tianjin University, Tianjin, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 7
Cited 0

Estimation risk in portfolio selection: the mean variance model versus the mean absolute deviation model

Management Science
Portfolio selection based on fuzzy probabilities and possibility distributions

Fuzzy Sets and Systems
Portfolio selection under independent possibilistic information

Fuzzy Sets and Systems - Special issue on soft decision analysis
Theory and Practice of Uncertain Programming

Theory and Practice of Uncertain Programming
A possibilistic approach to selecting portfolios with highest utility score

Fuzzy Sets and Systems - Special issue: Soft decision analysis
Fuzzy portfolio selection problems based on credibility theory

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Expected value of fuzzy variable and fuzzy expected value models

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A discrete-time version of dynamic portfolio selection model for survival is proposed in fuzzy environments. The investor gains an initial wealth every period and has a given consumption requirement. The investor survives only if his wealth is large enough to meet the requirement every period over a finite time horizon. After consumption the investor allocates the rest between a risky and a risk-free asset. This paper assumes that the gross rate of return on the risky asset is a fuzzy variable, then the functional equation of dynamic programming is established. In order to get the optimal investment policy, a hybrid intelligent algorithm to solve the optimal problem is presented. Finally, an illustrative case is given to demonstrate the effectiveness of the proposed algorithm.