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An evolutionary algorithm with a new operator and an adaptive strategy for large-scale portfolio problems

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Published:06 July 2018Publication History

ABSTRACT

A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz's mean-variance portfolio optimization model is studied with some practical constraints. We introduce a new operator and an adaptive strategy for improving the performance of the multi-dimensional mapping algorithm (MDM) proposed specially for the portfolio optimization. Experimental results show that the modification is efficient on tackling large-scale portfolio problems.

References

  1. T-J Chang, Nigel Meade, John E Beasley, and Yazid M Sharaiha. 2000. Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research 27, 13 (2000), 1271--1302. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Yi Chen, Aimin Zhou, Rongfang Zhou, Peng He, Yong Zhao, and Lihua Dong. 2017. An Evolutionary Algorithm with a New Coding Scheme for Multi-objective Portfolio Optimization. In Asia-Pacific Conference on Simulated Evolution and Learning. Springer, 97--109.Google ScholarGoogle ScholarCross RefCross Ref
  3. Khin Lwin, Rong Qu, and Graham Kendall. 2014. A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing 24 (2014), 757--772. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. H. Markowitz. 1952. Portfolio selection. The Journal of Finance 7, 1 (Mar 1952), 77--91.Google ScholarGoogle Scholar
  5. Antonin Ponsich, Antonio Lopez Jaimes, and Carlos A Coello Coello. 2013. A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Transactions on Evolutionary Computation 17, 3 (2013), 321--344. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Rainer Storn and Kenneth Price. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 4 (1997), 341--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Qingfu Zhang and Hui Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on evolutionary computation 11, 6 (2007), 712--731. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. An evolutionary algorithm with a new operator and an adaptive strategy for large-scale portfolio problems

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    • Published in

      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2018
      1968 pages
      ISBN:9781450357647
      DOI:10.1145/3205651

      Copyright © 2018 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 July 2018

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