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A Multiobjective-Based Group Trading Strategy Portfolio Optimization Technique

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Genetic and Evolutionary Computing (ICGEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1107))

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Abstract

A group trading strategy optimization portfolio algorithm was presented in the literature to find out an optimal group trading strategy portfolio (GTSP) to make trading decisions. In the real situation, traders have confronted to make decision by considering multiobjective goals. This paper thus proposes a MOGA-based algorithm to find a set of Pareto solutions for investors to make more useful trading plans, where each solution is a GTSP. To encode a GTSP, the candidate trading strategies are first produced according to the chosen technical indices. Then, a subset of the candidate trading strategies is selected using the determined ranking functions. Based on the subset of the trading strategies, the population is initialized according to the encoding scheme. The two objective functions are used to evaluate the fitness values of chromosomes to discover non-dominated solutions. The first objective function is composed of the return and risk factors. The second objective function is consisted of the grouping and weight balances. The genetic operators, including crossover, mutation, and inversion are executed to generate new offspring. Experiments on a financial dataset were also made to show the effectiveness of the proposed approach.

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Acknowledgments

This research was supported by the Ministry of Science and Technology of the Republic of China under grants MOST 108-2221-E-032-037 and MOST 107-2218-E-390-004.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan

    Chun-Hao Chen & Munkhjargal Gankhuyag

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-pei Hong

  3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

    Tzung-pei Hong

  4. Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan

    Mu-En Wu

  5. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jimmy Ming-Tai Wu

Authors
  1. Chun-Hao Chen
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  2. Munkhjargal Gankhuyag
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  3. Tzung-pei Hong
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  4. Mu-En Wu
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  5. Jimmy Ming-Tai Wu
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Corresponding author

Correspondence to Jimmy Ming-Tai Wu .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jeng-Shyang Pan

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, Australia

    Shu-Chuan Chu

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Chen, CH., Gankhuyag, M., Hong, Tp., Wu, ME., Wu, J.MT. (2020). A Multiobjective-Based Group Trading Strategy Portfolio Optimization Technique. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_10

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