IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2211.12839.html
   My bibliography  Save this paper

Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm

Author

Listed:
  • Wei-Chang Yeh
  • Yu-Hsin Hsieh
  • Chia-Ling Huang

Abstract

In modern society, the trading methods and strategies used in financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by a pre-programmed computer programs because the continuous development of network and computer computing technology. The quantitative trading, which the main purpose is to automatically formulate people's investment decisions into a fixed and quantifiable operation logic that eliminates all emotional interference and the influence of subjective thoughts and applies this logic to financial market activities in order to obtain excess profits above average returns, has led a lot of attentions in financial market. The development of self-adjustment programming algorithms for automatically trading in financial market has transformed a top priority for academic research and financial practice. Thus, a new flexible grid trading model combined with the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the fully connected neural network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model to automatically calculate and adjust the optimal trading parameters for trading after inputting the existing market situation is developed and studied in this work. The proposed model provides a self-adjust model to reduce investors' effort in the trading market, obtains outperformed investment return rate and model robustness, and can properly control the balance between risk and return.

Suggested Citation

  • Wei-Chang Yeh & Yu-Hsin Hsieh & Chia-Ling Huang, 2022. "Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm," Papers 2211.12839, arXiv.org.
    • Handle: RePEc:arx:papers:2211.12839
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2211.12839
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Franklin Allen & James McAndrews & Philip Strahan, 2002. "E-Finance: An Introduction," Journal of Financial Services Research, Springer;Western Finance Association, vol. 22(1), pages 5-27, August.
    2. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    3. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    4. Yeh, Wei-Chang, 2019. "A novel boundary swarm optimization method for reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    5. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    6. Jegadeesh, Narasimhan, 1991. "Seasonality in Stock Price Mean Reversion: Evidence from the U.S. and the U.K," Journal of Finance, American Finance Association, vol. 46(4), pages 1427-1444, September.
    7. Wei Huang & Kin Keung Lai & Yoshiteru Nakamori & Shouyang Wang & Lean Yu, 2007. "Neural Networks In Finance And Economics Forecasting," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 113-140.
    8. Meihua Wang & Wei-Chang Yeh & Ta-Chung Chu & Xianyong Zhang & Chia-Ling Huang & Jun Yang, 2018. "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," Energies, MDPI, vol. 11(9), pages 1-23, September.
    9. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).

    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. Liam Gallagher, 1999. "A multi-country analysis of the temporary and permanent components of stock prices," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 129-142.
    2. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2002. "Fractional integration and mean reversion in stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(3), pages 599-609.
    3. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
    4. Giner, Javier & Zakamulin, Valeriy, 2023. "A regime-switching model of stock returns with momentum and mean reversion," Economic Modelling, Elsevier, vol. 122(C).
    5. Nam, Kiseok & Pyun, Chong Soo & Kim, Sei-Wan, 2003. "Is asymmetric mean-reverting pattern in stock returns systematic? Evidence from Pacific-basin markets in the short-horizon," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 13(5), pages 481-502, December.
    6. Semenov, Andrei, 2021. "Measuring the stock's factor beta and identifying risk factors under market inefficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 635-649.
    7. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    8. Shlomo Zilca, 2010. "The variance ratio and trend stationary model as extensions of a constrained autoregressive model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 467-475.
    9. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, January.
    10. Tien Foo Sing & Kim Hiang Liow & Wei‐Jin Chan, 2002. "Mean reversion of Singapore property stock prices towards their fundamental values," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 20(4), pages 374-387, August.
    11. Lo, Andrew W & Wang, Jiang, 1995. "Implementing Option Pricing Models When Asset Returns Are Predictable," Journal of Finance, American Finance Association, vol. 50(1), pages 87-129, March.
    12. Boudt, Kris & Raza, Muhammad Wajid & Wauters, Marjan, 2019. "Evaluating the Shariah-compliance of equity portfolios: The weighting method matters," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 406-417.
    13. Pat Wilson & John Okunev & Guy Ta, 1994. "Are Real Estate and Securities Markets Integrated? Some Australian Evidence," Working Paper Series 42, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    14. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport," Papers 2106.12950, arXiv.org, revised Jun 2021.
    15. Balvers, Ronald J. & Wu, Yangru, 2006. "Momentum and mean reversion across national equity markets," Journal of Empirical Finance, Elsevier, vol. 13(1), pages 24-48, January.
    16. Angelovska, Julijana, 2018. "Testing Weak Form Of Stock Market Efficiency At The Macedonian Stock Exchange," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(2), pages 133-144.
    17. Liu, Chao-Shin & Ziebart, David A., 1999. "Anomalous security price behavior following management earnings forecasts," Journal of Empirical Finance, Elsevier, vol. 6(4), pages 405-429, October.
    18. Mukherji, Sandip, 2011. "Are stock returns still mean-reverting?," Review of Financial Economics, Elsevier, vol. 20(1), pages 22-27, January.
    19. Lin, Shih-Kuei & Wang, Shin-Yun & Tsai, Pei-Ling, 2009. "Application of hidden Markov switching moving average model in the stock markets: Theory and empirical evidence," International Review of Economics & Finance, Elsevier, vol. 18(2), pages 306-317, March.
    20. Kim, Hyeongwoo & Kim, Jintae, 2018. "London calling: Nonlinear mean reversion across national stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 265-277.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2211.12839. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.