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

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  • Yi-Fang Liu

    (College of Management and Economics - TJU - Tianjin University, 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)

  • Wei Zhang

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

  • Chao Xu

    (College of Management and Economics - TJU - 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 - TJU - 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-00983051, HAL.
    • Handle: RePEc:hal:journl:halshs-00983051
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00983051
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    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. 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.
    13. Bullard, James & Duffy, John, 2001. "Learning And Excess Volatility," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 272-302, April.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    21. 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.
    22. Weinbaum, David, 2009. "Investor heterogeneity, asset pricing and volatility dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 33(7), pages 1379-1397, July.
    23. 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.
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    Cited by:

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    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.

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    Keywords

    Agent-based model; heterogeneity; switching behavior; market volatility; Modèle agents; hétérogénéité; Comportement de commutation; volatilité du marché boursier;
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