IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v80y2025ics1062940825001445.html

Price dynamics in artificial stock market with realistic order book mechanism

Author

Listed:
  • Çetin, Uzay
  • Demirtaş, Şükrü C.
  • Şahin, Senem Çakmak

Abstract

We analyzed the effect of the daily price margin on artificial stock markets. In our study, we have two distinct market scenarios: One designed to imitate a market akin to that of Türkiye, characterized by the presence of a daily price margin regulation, and the other reflecting a market resembling the United States, where orders are not subject to daily price margin constraints. With daily price margin regulations stock prices become more accessible, positively impacting market volume. We incorporated a realistic order book mechanism for keeping track of the bid and ask orders. Traders are classified as either fundamental or noise, according to their strategies. We have also established a dynamic risk level for each stock, based on its weekly transaction volumes. Only fundamentals are risk-aware. That is, they tend to order stocks with low risk and avoid high risk stocks. We have detected emerging patterns of price fluctuations within the market scenario governed by the daily price margin regulations. Risk-aware herd behavior, despite not being explicitly modeled as an input, emerges also spontaneously within the system. These patterns emerge because of the complex relationship among dynamic risk levels of stocks, risk-aware traders and the daily price margin regulation.

Suggested Citation

  • Çetin, Uzay & Demirtaş, Şükrü C. & Şahin, Senem Çakmak, 2025. "Price dynamics in artificial stock market with realistic order book mechanism," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:ecofin:v:80:y:2025:i:c:s1062940825001445
    DOI: 10.1016/j.najef.2025.102504
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940825001445
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2025.102504?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Liming & Sun, Xuchu & Zhu, Hongliang & Li, Tangrong, 2025. "Exploring the dynamic impact of transaction taxes on market quality in HFT and non-HFT environments: An agent-based modeling approach," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    2. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    3. Li, Jinfang, 2017. "Investor sentiment, heterogeneous agents and asset pricing model," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 504-512.
    4. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    5. Cordoni, Francesco, 2025. "Multi-asset bubbles equilibrium price dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    6. Kaizoji, Taisei & Leiss, Matthias & Saichev, Alexander & Sornette, Didier, 2015. "Super-exponential endogenous bubbles in an equilibrium model of fundamentalist and chartist traders," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 289-310.
    7. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    8. Mohammad Zare & Omid Naghshineh A. & Erfan Salavati & Adel Mohammadpour, 2021. "An agent-based model and detect price manipulation based on intraday transaction data with simulation," Applied Economics, Taylor & Francis Journals, vol. 53(43), pages 4931-4949, September.
    9. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    10. Tesfatsion, Leigh S., 2001. "Guest Editorial: Agent-Based Modeling of Evolutionary Economic Systems," Staff General Research Papers Archive 10037, Iowa State University, Department of Economics.
    11. B. LeBaron, 2001. "A builder's guide to agent-based financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 254-261.
    12. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    13. Uzay Cetin & Haluk O. Bingol, 2016. "The Dose Of The Threat Makes The Resistance For Cooperation," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-20, December.
    14. Kang Gao & Perukrishnen Vytelingum & Stephen Weston & Wayne Luk & Ce Guo, 2022. "High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach," Papers 2208.13654, arXiv.org.
    15. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    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. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    2. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    3. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    4. Xue-Zhong He & Youwei Li, 2008. "Heterogeneity, convergence, and autocorrelations," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 59-79.
    5. Pastushkov, A., 2025. "Evolutionary and agent-based computational finance: The new paradigms for asset pricing," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 196-222.
    6. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    7. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    8. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    9. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    10. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 991-1020, April.
    11. Cars Hommes & Florian Wagener, 2008. "Complex Evolutionary Systems in Behavioral Finance," Tinbergen Institute Discussion Papers 08-054/1, Tinbergen Institute.
    12. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    13. J. Doyne Farmer & John Geanakoplos, 2008. "The virtues and vices of equilibrium and the future of financial economics," Papers 0803.2996, arXiv.org.
    14. Noemi Schmitt & Ivonne Schwartz & Frank Westerhoff, 2022. "Heterogeneous speculators and stock market dynamics: a simple agent-based computational model," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1263-1282, October.
    15. Li, Y., 2006. "On microscopic simulation models of financial markets," Other publications TiSEM ec2f852d-4a7f-47b1-99b8-4, Tilburg University, School of Economics and Management.
    16. Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
    17. Gil Bazo, Javier & Moreno Muñoz, Jesús David & Tapia, Mikel, 2005. "Price dynamics, informational efficiency and wealth distribution in continuous double auction markets," DEE - Working Papers. Business Economics. WB wb057819, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    18. Bas Straatman & Danielle Marceau & Roger White, 2013. "A Generic Framework for a Combined Agent-based Market and Production Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 425-445, April.
    19. Hirshleifer, David & Lo, Andrew W. & Zhang, Ruixun, 2023. "Social contagion and the survival of diverse investment styles," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    20. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:ecofin:v:80:y:2025:i:c:s1062940825001445. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.