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

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm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. "RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm," Electronic Commerce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. 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.
    3. 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.
    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. Harry M Markowitz (ed.), 2009. "Harry Markowitz:Selected Works," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6967, August.
    6. 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.
    7. 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.
    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. Jin’an He & Shicheng Yin & Fangping Peng, 2024. "Weak aggregating specialist algorithm for online portfolio selection," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2405-2434, June.
    2. Białkowski, Jędrzej, 2020. "Cryptocurrencies in institutional investors’ portfolios: Evidence from industry stop-loss rules," Economics Letters, Elsevier, vol. 191(C).
    3. 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.
    4. Jonathan Raimana Chan & Thomas Huckle & Antoine Jacquier & Aitor Muguruza, 2021. "Portfolio optimisation with options," Papers 2111.12658, arXiv.org, revised Sep 2024.
    5. 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.
    6. 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.
    7. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
    8. 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.
    9. David Markantonis & G.-Fivos Sargentis & Panayiotis Dimitriadis & Theano Iliopoulou & Aimilia Siganou & Konstantina Moraiti & Maria Nikolinakou & Ilias Taygetos Meletopoulos & Nikos Mamassis & Demetri, 2023. "Stochastic Evaluation of the Investment Risk by the Scale of Water Infrastructures—Case Study: The Municipality of West Mani (Greece)," World, MDPI, vol. 4(1), pages 1-20, January.
    10. Gerritsen, Dirk F., 2016. "Are chartists artists? The determinants and profitability of recommendations based on technical analysis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 179-196.
    11. Fengmin Xu & Jieao Ma, 2023. "Intelligent option portfolio model with perspective of shadow price and risk-free profit," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    12. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
    13. Mohd Azdi Maasar & Diana Roman & Paresh Date, 2022. "Risk minimisation using options and risky assets," Operational Research, Springer, vol. 22(1), pages 485-506, March.
    14. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    15. 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.
    16. 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.
    17. Faias, José Afonso, 2023. "Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation," Journal of Financial Markets, Elsevier, vol. 63(C).
    18. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    19. 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.
    20. Sadaqat, Mohsin & Butt, Hilal Anwar, 2023. "Stop-loss rules and momentum payoffs in cryptocurrencies," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).

    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.