IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-01215947.html
   My bibliography  Save this paper

Impact of information cost and switching of trading strategies in an artificial stock market

Author

Listed:
  • Yi-Fang Liu

    (China Center for Social Computing and Analytics - TJU - Tianjin University, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, College of Management and Economics - Tianjin University)

  • Wei Zhang

    (College of Management and Economics - Tianjin University, China Center for Social Computing and Analytics - TJU - Tianjin University)

  • Chao Xu

    (College of Management and Economics - Tianjin University, China Center for Social Computing and Analytics - TJU - Tianjin University)

  • Jørgen Vitting Andersen

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Hai-Chuan Xu

    (College of Management and Economics - Tianjin University, China Center for Social Computing and Analytics - TJU - Tianjin University)

Abstract

This paper studies the switching of trading strategies and its effect on the market volatility in a continuous double auction market. We describe the behavior when some uninformed agents, who we call switchers, decide whether or not to pay for information before they trade. By paying for the information they behave as informed traders. First we verify that our model is able to reproduce some of the stylized facts in real financial markets. Next we consider the relationship between switching and the market volatility under different structures of investors. We find that there exists a positive relationship between the market volatility and the percentage of switchers. We therefore conclude that the switchers are a destabilizing factor in the market. However, for a given fixed percentage of switchers, the proportion of switchers that decide to buy information at a given moment of time is negatively related to the current market volatility. In other words, if more agents pay for information to know the fundamental value at some time, the market volatility will be lower. This is because the market price is closer to the fundamental value due to information diffusion between switchers.

Suggested Citation

  • Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-01215947, HAL.
    • Handle: RePEc:hal:journl:halshs-01215947
    DOI: 10.1016/j.physa.2014.04.004
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01215947
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-01215947/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.physa.2014.04.004?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
    ---><---

    References listed on IDEAS

    as
    1. Di Guilmi, Corrado & He, Xue-Zhong & Li, Kai, 2014. "Herding, trend chasing and market volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 349-373.
    2. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    3. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    4. Kirchler, Michael & Huber, Jurgen, 2007. "Fat tails and volatility clustering in experimental asset markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1844-1874, June.
    5. Bulkley, George & Tonks, Ian, 1989. "Are U.K. Stock Prices Excessively Volatile? Trading Rules and Variance Bounds Tests," Economic Journal, Royal Economic Society, vol. 99(398), pages 1083-1098, December.
    6. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    7. Chiarella, Carl & He, Xue-Zhong, 2002. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 95-132, February.
    8. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    9. Luisa Corrado & Marcus Miller & Lei Zhang, 2007. "Bulls, bears and excess volatility: can currency intervention help?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 261-272.
    10. Youssefmir, Michael & Huberman, Bernardo A., 1997. "Clustered volatility in multiagent dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 32(1), pages 101-118, January.
    11. Grundy, Bruce D. & Kim, Youngsoo, 2002. "Stock Market Volatility in a Heterogeneous Information Economy," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(1), pages 1-27, March.
    12. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
    13. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February.
    14. Bullard, James & Duffy, John, 2001. "Learning And Excess Volatility," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 272-302, April.
    15. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    16. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    17. William A. Brock & Cars H. Hommes, 2001. "A Rational Route to Randomness," Chapters, in: W. D. Dechert (ed.), Growth Theory, Nonlinear Dynamics and Economic Modelling, chapter 16, pages 402-438, Edward Elgar Publishing.
    18. Gil Bazo, Javier & Moreno Muñoz, Jesús David, 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.
    19. Wohlmuth, Johannes & Andersen, Jørgen Vitting, 2006. "Modelling financial markets with agents competing on different time scales and with different amount of information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 459-468.
    20. Franke, Reiner & Westerhoff, Frank, 2012. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
    21. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.
    22. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    23. LeRoy, Stephen F & Porter, Richard D, 1981. "The Present-Value Relation: Tests Based on Implied Variance Bounds," Econometrica, Econometric Society, vol. 49(3), pages 555-574, May.
    24. Weinbaum, David, 2009. "Investor heterogeneity, asset pricing and volatility dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 33(7), pages 1379-1397, July.
    25. Allan Timmermann, 1996. "Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(4), pages 523-557.
    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. Basaure, Arturo & Suomi, Henna & Hämmäinen, Heikki, 2016. "Transaction vs. switching costs—Comparison of three core mechanisms for mobile markets," Telecommunications Policy, Elsevier, vol. 40(6), pages 545-566.
    2. Basaure, Arturo & Suomi, Henna & Hämmäinen, Heikki, 2014. "Effects of transaction and switching costs on mobile market performance," 20th ITS Biennial Conference, Rio de Janeiro 2014: The Net and the Internet - Emerging Markets and Policies 106830, International Telecommunications Society (ITS).
    3. Roberto Mota Navarro & Hernán Larralde, 2017. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-27, February.
    4. Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2016. "Understanding Financial Market States Using an Artificial Double Auction Market," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.

    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. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01215947, HAL.
    2. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-00983051, HAL.
    3. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00983051, HAL.
    4. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Documents de travail du Centre d'Economie de la Sorbonne 14031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01011701, HAL.
    6. Liu, Yi-Fang & Zhang, Wei & Xu, Chao & Vitting Andersen, Jørgen & Xu, Hai-Chuan, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 204-215.
    7. Yi-Fang Liu & Wei Zhang & Chao Xu & J{o}rgen Vitting Andersen & Hai-Chuan Xu, 2013. "Impact of information cost and switching of trading strategies in an artificial stock market," Papers 1311.4274, arXiv.org, revised Jul 2014.
    8. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    9. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    10. Raquel Almeida Ramos & Federico Bassi & Dany Lang, 2020. "Bet against the trend and cash in profits," CEPN Working Papers halshs-02956879, HAL.
    11. Federico Bassi & Raquel Ramos & Dany Lang, 2023. "Bet against the trend and cash in profits: An agent-based model of endogenous fluctuations of exchange rates," Journal of Evolutionary Economics, Springer, vol. 33(2), pages 429-472, April.
    12. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    13. He, Xue-Zhong & Li, Youwei & Zheng, Min, 2019. "Heterogeneous agent models in financial markets: A nonlinear dynamics approach," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 135-149.
    14. Qi Nan Zhai, 2015. "Asset Pricing Under Ambiguity and Heterogeneity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2015, January-A.
    15. Hommes, C.H., 2005. "Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006," CeNDEF Working Papers 05-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    16. Diks, Cees & Dindo, Pietro, 2008. "Informational differences and learning in an asset market with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1432-1465, May.
    17. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    18. Zhao, Dongxu & Li, Kai, 2022. "Bounded rationality, adaptive behaviour, and asset prices," International Review of Financial Analysis, Elsevier, vol. 80(C).
    19. Kai Li, 2014. "Asset Price Dynamics with Heterogeneous Beliefs and Time Delays," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 13, July-Dece.
    20. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.

    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:hal:journl:halshs-01215947. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.