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Multi-period cardinality constrained portfolio selection models with interval coefficients

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Abstract

In this paper, we discuss a multi-period portfolio selection problem in emerging markets. To provide investors with more choices, we propose four multi-period cardinality constrained portfolio selection models with interval coefficients in both objective functions and constraints. The proposed models can be equivalently represented as the parameter programming problems with interval coefficients in constraints. We utilize the definition of the possibility degree for interval inequality to handle the interval inequality constraints in the proposed models and express investors’ different risk attitudes. Then, the proposed models are transformed into deterministic models. After that, we design a new dynamic differential evolution algorithm with self-adapting control parameter to solve the transformed deterministic models. Finally, we provide a numerical example to illustrate the applications of the proposed models and demonstrate the effectiveness of the designed algorithm.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (No. 71501076), the Natural Science Foundation of Guangdong Province of China (No. 2014A030310454), the Fundamental Research Funds for the Central Universities (2015ZM084, 2014ZP0005) and Guangzhou Financial Services Innovation and Risk Management Research Base.

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Authors and Affiliations

  1. School of Business Administration, South China University of Technology, Guangzhou, 510641, China

    Yong-Jun Liu & Wei-Guo Zhang

  2. Department of Economics and Finance, City University of Hong Kong, 88 Tat Chee Avenue, Kowloon, Hong Kong

    Jun-Bo Wang

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  1. Yong-Jun Liu
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  2. Wei-Guo Zhang
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  3. Jun-Bo Wang
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Correspondence to Yong-Jun Liu or Wei-Guo Zhang.

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Liu, YJ., Zhang, WG. & Wang, JB. Multi-period cardinality constrained portfolio selection models with interval coefficients. Ann Oper Res 244, 545–569 (2016). https://doi.org/10.1007/s10479-016-2117-4

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