IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1309.3399.html
   My bibliography  Save this paper

Grand canonical minority game as a sign predictor

Author

Listed:
  • Karol Wawrzyniak
  • Wojciech Wi'slicki

Abstract

In this paper the extended model of Minority game (MG), incorporating variable number of agents and therefore called Grand Canonical, is used for prediction. We proved that the best MG-based predictor is constituted by a tremendously degenerated system, when only one agent is involved. The prediction is the most efficient if the agent is equipped with all strategies from the Full Strategy Space. Each of these filters is evaluated and, in each step, the best one is chosen. Despite the casual simplicity of the method its usefulness is invaluable in many cases including real problems. The significant power of the method lies in its ability to fast adaptation if \lambda-GCMG modification is used. The success rate of prediction is sensitive to the properly set memory length. We considered the feasibility of prediction for the Minority and Majority games. These two games are driven by different dynamics when self-generated time series are considered. Both dynamics tend to be the same when a feedback effect is removed and an exogenous signal is applied.

Suggested Citation

  • Karol Wawrzyniak & Wojciech Wi'slicki, 2013. "Grand canonical minority game as a sign predictor," Papers 1309.3399, arXiv.org.
    • Handle: RePEc:arx:papers:1309.3399
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1309.3399
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.
    2. Austin Gerig, 2008. "A Theory for Market Impact: How Order Flow Affects Stock Price," Papers 0804.3818, arXiv.org, revised Jul 2008.
    3. Chen, Fang & Gou, Chengling & Guo, Xiaoqian & Gao, Jieping, 2008. "Prediction of stock markets by the evolutionary mix-game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3594-3604.
    4. Jean-Philippe Bouchaud & Yuval Gefen & Marc Potters & Matthieu Wyart, 2003. "Fluctuations and response in financial markets: the subtle nature of `random' price changes," Papers cond-mat/0307332, arXiv.org, revised Aug 2003.
    5. Karol Wawrzyniak & Wojciech Wislicki, 2011. "Mesoscopic approach to minority games in herd regime," Papers 1112.0210, arXiv.org.
    6. Challet, D. & Zhang, Y.-C., 1997. "Emergence of cooperation and organization in an evolutionary game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 407-418.
    7. Challet, Damien & Zhang, Yi-Cheng, 1998. "On the minority game: Analytical and numerical studies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 256(3), pages 514-532.
    8. Johnson, Neil F & Hart, Michael & Hui, P.M, 1999. "Crowd effects and volatility in markets with competing agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 1-8.
    9. Neil F. Johnson & David Lamper & Paul Jefferies & Michael L. Hart & Sam Howison, 2001. "Application of multi-agent games to the prediction of financial time-series," OFRC Working Papers Series 2001mf04, Oxford Financial Research Centre.
    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. Chen, Fang & Gou, Chengling & Guo, Xiaoqian & Gao, Jieping, 2008. "Prediction of stock markets by the evolutionary mix-game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3594-3604.
    2. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.
    3. Roberto Savona & Maxence Soumare & Jørgen Vitting Andersen, 2015. "Financial Symmetry and Moods in the Market," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.
    4. Andersen, Jørgen Vitting & de Peretti, Philippe, 2021. "Heuristics in experiments with infinitely large strategy spaces," Journal of Business Research, Elsevier, vol. 129(C), pages 612-620.
    5. Groot, Robert D. & Musters, Pieter A.D., 2005. "Minority Game of price promotions in fast moving consumer goods markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 533-547.
    6. Jørgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Post-Print hal-02435934, HAL.
    7. Guglielmo Maria Caporale & Antoaneta Serguieva & Hao Wu, 2009. "Financial contagion: evolutionary optimization of a multinational agent‐based model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 111-125, January.
    8. Jørgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02435934, HAL.
    9. Liu, Yi-Fang & Andersen, Jørgen Vitting & Frolov, Maxime & de Peretti, Philippe, 2021. "Synchronization in human decision-making," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    10. Wawrzyniak, Karol & Wiślicki, Wojciech, 2012. "Mesoscopic approach to minority games in herd regime," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2056-2082.
    11. Roberto Savona & Maxence Soumare & Jørgen Vitting Andersen, 2014. "Financial Symmetry and Moods in the Market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00983008, HAL.
    12. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    13. Yi-Fang Liu & Jørgen Vitting Andersen & Philippe de Peretti, 2016. "Onset of financial instability studied via agent-based models," Post-Print hal-01397400, HAL.
    14. J. Wiesinger & D. Sornette & J. Satinover, 2013. "Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 475-492, April.
    15. J{o}rgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Papers 2005.02337, arXiv.org.
    16. Thorsten Chmura & Thomas Pitz, 2007. "An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Experimental Congestion Games," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-1.
    17. Xin, C. & Yang, G. & Huang, J.P., 2017. "Ising game: Nonequilibrium steady states of resource-allocation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 666-673.
    18. Zhang, Yu-Xia & Liang, Wen-Yao & Liu, Xue-Mei, 2014. "Changes of behavior tendency in the evolutionary minority game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 227-232.
    19. Giorgio Fagiolo & Marco Valente, 2005. "Minority Games, Local Interactions, and Endogenous Networks," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 41-57, February.
    20. Roland Rothenstein, 2018. "Quantification of market efficiency based on informational-entropy," Papers 1812.02371, arXiv.org.

    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:1309.3399. 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.