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From market games to real-world markets

Author

Listed:
  • P. Jefferies

    (Department of Physics, Oxford University, Parks Rd, Oxford, OX13PU, UK)

  • M.L. Hart

    (Department of Physics, Oxford University, Parks Rd, Oxford, OX13PU, UK)

  • P.M. Hui

    (Physics Department, Chinese University of Hong Kong, Shatin, Hong Kong, PR China)

  • N.F. Johnson

    (Department of Physics, Oxford University, Parks Rd, Oxford, OX13PU, UK)

Abstract

This paper uses the development of multi-agent market models to present a unified approach to the joint questions of how financial market movements may be simulated, predicted, and hedged against. We first present the results of agent-based market simulations in which traders equipped with simple buy/sell strategies and limited information compete in speculatory trading. We examine the effect of different market clearing mechanisms and show that implementation of a simple Walrasian auction leads to unstable market dynamics. We then show that a more realistic out-of-equilibrium clearing process leads to dynamics that closely resemble real financial movements, with fat-tailed price increments, clustered volatility and high volume autocorrelation. We then show that replacing the `synthetic' price history used by these simulations with data taken from real financial time-series leads to the remarkable result that the agents can collectively learn to identify moments in the market where profit is attainable. Hence on real financial data, the system as a whole can perform better than random. We then employ the formalism of Bouchaud in conjunction with agent based models to show that in general risk cannot be eliminated from trading with these models. We also show that, in the presence of transaction costs, the risk of option writing is greatly increased. This risk, and the costs, can however be reduced through the use of a delta-hedging strategy with modified, time-dependent volatility structure.

Suggested Citation

  • P. Jefferies & M.L. Hart & P.M. Hui & N.F. Johnson, 2001. "From market games to real-world markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 20(4), pages 493-501, April.
    • Handle: RePEc:spr:eurphb:v:20:y:2001:i:4:d:10.1007_s100510170228
    DOI: 10.1007/s100510170228
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    Citations

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    Cited by:

    1. Zapart, Christopher A., 2009. "On entropy, financial markets and minority games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1157-1172.
    2. Stefan Thurner & Rudolf Hanel & Stefan Pichler, 2003. "Risk trading, network topology and banking regulation," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 306-319.
    3. Kei Katahira & Yu Chen, 2019. "Heterogeneous wealth distribution, round-trip trading and the emergence of volatility clustering in Speculation Game," Papers 1909.03185, arXiv.org.
    4. Gou, Chengling, 2006. "Deduction of initial strategy distributions of agents in mix-game models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 633-640.
    5. Vee-Liem Saw & Lock Yue Chew, 2020. "No-boarding buses: Synchronisation for efficiency," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-34, March.
    6. Wei, J.R. & Huang, J.P. & Hui, P.M., 2013. "An agent-based model of stock markets incorporating momentum investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2728-2735.
    7. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    8. Matteo Ortisi & Valerio Zuccolo, 2012. "From Minority Game to Black & Scholes pricing," Papers 1205.2521, arXiv.org, revised May 2013.
    9. 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.
    10. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    11. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    12. Ohira, Toru & Sazuka, Naoya & Marumo, Kouhei & Shimizu, Tokiko & Takayasu, Misako & Takayasu, Hideki, 2002. "Predictability of currency market exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 308(1), pages 368-374.
    13. Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical regularities of order placement in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3173-3182.
    14. Damien Challet & Tobias Galla, 2005. "Price return autocorrelation and predictability in agent-based models of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 569-576.
    15. Liu, Xinghua & Gregor, Shirley & Yang, Jianmei, 2008. "The effects of behavioral and structural assumptions in artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2535-2546.
    16. 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.
    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. Elena Green & Daniel M. Heffernan, 2019. "An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns," Papers 1901.05053, arXiv.org.
    19. Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
    20. Katahira, Kei & Chen, Yu & Hashimoto, Gaku & Okuda, Hiroshi, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 503-518.
    21. Ren, F. & Zhang, Y.C., 2008. "Trading model with pair pattern strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5523-5534.
    22. Ferreira, Fernando F & Francisco, Gerson & Machado, Birajara S & Muruganandam, Paulsamy, 2003. "Time series analysis for minority game simulations of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(3), pages 619-632.
    23. Yong Shi & Bo Li & Guangle Du, 2021. "Pyramid scheme in stock market: a kind of financial market simulation," Papers 2102.02179, arXiv.org, revised Feb 2021.
    24. Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.

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