IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v23y2023i1d10.1007_s10660-021-09467-y.html
   My bibliography  Save this article

An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Author

Listed:
  • Chun-Hao Chen

    (National Taipei University of Technology)

  • Yu-Hsuan Chen

    (Tamkang University)

  • Vicente Garcia Diaz

    (University of Oviedo)

  • Jerry Chun-Wei Lin

    (Western Norway University of Applied Sciences)

Abstract

It is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.

Suggested Citation

  • Chun-Hao Chen & Yu-Hsuan Chen & Vicente Garcia Diaz & Jerry Chun-Wei Lin, 2023. "An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm," Electronic Commerce Research, Springer, vol. 23(1), pages 3-42, March.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-021-09467-y
    DOI: 10.1007/s10660-021-09467-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-021-09467-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-021-09467-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Yong-Jun & Zhang, Wei-Guo, 2013. "Fuzzy portfolio optimization model under real constraints," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 704-711.
    2. Rafał Dreżewski & Grzegorz Dziuban & Karol Pająk, 2018. "The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies," Sustainability, MDPI, vol. 10(5), pages 1-45, May.
    3. Faias, José Afonso & Santa-Clara, Pedro, 2017. "Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(1), pages 277-303, February.
    4. Kaminski, Kathryn M. & Lo, Andrew W., 2014. "When do stop-loss rules stop losses?," Journal of Financial Markets, Elsevier, vol. 18(C), pages 234-254.
    5. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    6. Harry M Markowitz (ed.), 2009. "Harry Markowitz:Selected Works," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6967.
    7. Yao, Haixiang & Li, Zhongfei & Li, Duan, 2016. "Multi-period mean-variance portfolio selection with stochastic interest rate and uncontrollable liability," European Journal of Operational Research, Elsevier, vol. 252(3), pages 837-851.
    8. Harry M Markowitz, 2009. "Harry Markowitz Company," World Scientific Book Chapters, in: Harry M Markowitz (ed.), Harry Markowitz Selected Works, chapter 7, pages 529-700, World Scientific Publishing Co. Pte. Ltd..
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Białkowski, Jędrzej, 2020. "Cryptocurrencies in institutional investors’ portfolios: Evidence from industry stop-loss rules," Economics Letters, Elsevier, vol. 191(C).
    2. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    3. Yong-Jun Liu & Wei-Guo Zhang, 2018. "Multiperiod Fuzzy Portfolio Selection Optimization Model Based on Possibility Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 941-968, May.
    4. Florin Turcaș & Florin Cornel Dumiter & Marius Boiță, 2022. "Econophysics Techniques and Their Applications on the Stock Market," Mathematics, MDPI, vol. 10(6), pages 1-25, March.
    5. Pun, Chi Seng & Wong, Hoi Ying, 2019. "A linear programming model for selection of sparse high-dimensional multiperiod portfolios," European Journal of Operational Research, Elsevier, vol. 273(2), pages 754-771.
    6. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    7. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
    8. Krzysztof Piasecki & Joanna Siwek, 2018. "The portfolio problem with present value modelled by a discrete trapezoidal fuzzy number," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(1), pages 57-74.
    9. Wei Chen & Yun Wang & Mukesh Kumar Mehlawat, 2018. "A hybrid FA–SA algorithm for fuzzy portfolio selection with transaction costs," Annals of Operations Research, Springer, vol. 269(1), pages 129-147, October.
    10. Wei Chen & Yuxi Gai & Pankaj Gupta, 2018. "Efficiency evaluation of fuzzy portfolio in different risk measures via DEA," Annals of Operations Research, Springer, vol. 269(1), pages 103-127, October.
    11. Pang, Xiaochuan & Zhu, Shushang & Cui, Xueting & Ma, Jiali, 2023. "Systemic risk of optioned portfolio: Controllability and optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    12. Amen Aissi Harzallah & Mouna Boujelbene Abbes, 2020. "The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory," Journal of Interdisciplinary Economics, , vol. 32(2), pages 218-236, July.
    13. Rui Wang, 2021. "Discriminating modelling approaches for Point in Time Economic Scenario Generation," Papers 2108.08818, arXiv.org.
    14. Bi, Junna & Jin, Hanqing & Meng, Qingbin, 2018. "Behavioral mean-variance portfolio selection," European Journal of Operational Research, Elsevier, vol. 271(2), pages 644-663.
    15. Pedro Barroso & Jurij-Andrei Reichenecker & Marco J. Menichetti, 2022. "Hedging with an Edge: Parametric Currency Overlay," Management Science, INFORMS, vol. 68(1), pages 669-689, January.
    16. Guo, Sini & Yu, Lean & Li, Xiang & Kar, Samarjit, 2016. "Fuzzy multi-period portfolio selection with different investment horizons," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1026-1035.
    17. Henderson, Vicky & Hobson, David & Tse, Alex S.L., 2018. "Probability weighting, stop-loss and the disposition effect," Journal of Economic Theory, Elsevier, vol. 178(C), pages 360-397.
    18. Sadoghi, Amirhossein & Vecer, Jan, 2022. "Optimal liquidation problem in illiquid markets," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1050-1066.
    19. Zhang, Ling & Zhang, Hao & Yao, Haixiang, 2018. "Optimal investment management for a defined contribution pension fund under imperfect information," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 210-224.
    20. Andrew Clare & James Seaton & Peter N Smith & Stephen Thomas, 2013. "Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500," Journal of Asset Management, Palgrave Macmillan, vol. 14(3), pages 182-194, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-021-09467-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.