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Long-memory in an order-driven market

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Author Info

  • LeBaron, Blake
  • Yamamoto, Ryuichi

Abstract

This paper introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. We analyze markets with and without learning and adaptation. The simulation results show that our model with learning and adaptation successfully replicates long-memories in trading volume, stock return volatility, and signs of market orders in an informationally efficient market. We also discuss why evolutionary dynamics are important in generating these features.

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File URL: http://www.sciencedirect.com/science/article/pii/S0378437107004992
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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 383 (2007)
Issue (Month): 1 ()
Pages: 85-89

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Handle: RePEc:eee:phsmap:v:383:y:2007:i:1:p:85-89

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Microstructure; Agent-based; Long-memory; Order flow;

References

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  1. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  2. Lillo Fabrizio & Farmer J. Doyne, 2004. "The Long Memory of the Efficient Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-35, September.
  3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  4. Lo, Andrew W. (Andrew Wen-Chuan), 1989. "Long-term memory in stock market prices," Working papers 3014-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  5. Giulia Iori & Carl Chiarella, 2002. "A simple microstructure model of double auction markets," Computing in Economics and Finance 2002 44, Society for Computational Economics.
  6. J. Doyne Farmer & Paolo Patelli & Ilija I. Zovko, 2003. "The Predictive Power of Zero Intelligence in Financial Markets," Papers cond-mat/0309233, arXiv.org, revised Feb 2004.
  7. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-27, October.
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Citations

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Cited by:
  1. Blake LeBaron & Ryuichi Yamamoto, 2008. "The Impact of Imitation on Long Memory in an Order-Driven Market," Eastern Economic Journal, Palgrave Macmillan, vol. 34(4), pages 504-517.
  2. Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
  3. Bence Toth & Imon Palit & Fabrizio Lillo & J. Doyne Farmer, 2011. "Why is order flow so persistent?," Papers 1108.1632, arXiv.org.
  4. Tedeschi, Gabriele & Iori, Giulia & Gallegati, Mauro, 2012. "Herding effects in order driven markets: The rise and fall of gurus," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 82-96.
  5. Miller, Ross M., 2008. "Don't let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 68(1), pages 153-166, October.
  6. Pirino, Davide, 2009. "Jump detection and long range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1150-1156.
  7. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
  8. Ryuichi Yamamoto, 2011. "Volatility clustering and herding agents: does it matter what they observe?," Journal of Economic Interaction and Coordination, Springer, vol. 6(1), pages 41-59, May.

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