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    Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint

    264009.pdf (120.3Kb)
    Access Status
    Open access
    Authors
    Li, B.
    Zhu, Y.
    Sun, Y.
    Aw, Ee Ling Grace
    Teo, Kok Lay
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, B. and Zhu, Y. and Sun, Y. and Aw, E.L.G. and Teo, K.L. 2018. Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint. Applied Mathematical Modelling. 56: pp. 539-550.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.12.016
    ISSN
    0307-904X
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/65681
    Collection
    • Curtin Research Publications
    Abstract

    The complexity of financial markets leads to different types of indeterminate asset returns. For example, asset returns are considered as random variables, when the available data is enough. When the available data is too small or even no available data to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degrees of asset returns. Then, asset returns can be described as uncertain variables. In this paper, we discuss a multi-period portfolio selection problem under uncertain environment, which maximizes the final wealth and minimizes the risk of investment. Unlike the common method to describe the multi-period portfolio selection problem as a bi-objective optimization model, we formulate this uncertain multi-period portfolio selection problem by a new method in three steps with two single objective optimization models. And, we consider the influence of transaction cost and bankruptcy of investor. Then, the proposed uncertain optimization models are transformed into the corresponding crisp optimization models and we use the genetic algorithm combined with penalty function method to solve them. Finally, a numerical example is given to show the effectiveness and practicability of proposed models and method.

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