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Fundamentalists clashing over the book: a study of order-driven stock markets

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  • Marco Licalzi
  • Paolo Pellizzari

Abstract

Agent-based models of market dynamics must strike a compromise between the structural assumptions that represent the trading mechanism and the behavioural assumptions that describe the rules by which traders make their decisions. We present a structurally detailed model of an order-driven stock market and show that a minimal set of behavioural assumptions suffices to generate a leptokurtic distribution of short-term log-returns. This result supports the conjecture that the emergence of some statistical properties of financial time series is due to the microstructure of stock markets.

Suggested Citation

  • Marco Licalzi & Paolo Pellizzari, 2003. "Fundamentalists clashing over the book: a study of order-driven stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 3(6), pages 470-480.
    • Handle: RePEc:taf:quantf:v:3:y:2003:i:6:p:470-480
    DOI: 10.1088/1469-7688/3/6/306
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    1. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    2. Milgrom, Paul & Stokey, Nancy, 1982. "Information, trade and common knowledge," Journal of Economic Theory, Elsevier, vol. 26(1), pages 17-27, February.
    3. 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.
    4. Matassini, Lorenzo & Franci, Fabio, 2001. "On financial markets trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 526-542.
    5. 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.
    6. 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.
    7. Welch, Ivo, 2000. "Herding among security analysts," Journal of Financial Economics, Elsevier, vol. 58(3), pages 369-396, December.
    8. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    9. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    10. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    11. Bak, P. & Paczuski, M. & Shubik, M., 1997. "Price variations in a stock market with many agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 430-453.
    12. Steiglitz, Ken & Shapiro, Daniel, 1998. "Simulating the Madness of Crowds: Price Bubbles in an Auction-Mediated Robot Market," Computational Economics, Springer;Society for Computational Economics, vol. 12(1), pages 35-59, August.
    13. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    14. Cohen, Kalman J, et al, 1978. "Limit Orders, Market Structure, and the Returns Generation Process," Journal of Finance, American Finance Association, vol. 33(3), pages 723-736, June.
    15. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    16. Mendelson, Haim, 1982. "Market Behavior in a Clearing House," Econometrica, Econometric Society, vol. 50(6), pages 1505-1524, November.
    17. Maslov, Sergei, 2000. "Simple model of a limit order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(3), pages 571-578.
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    More about this item

    JEL classification:

    • G19 - Financial Economics - - General Financial Markets - - - Other
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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