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Heterogeneous Beliefs, Intelligent Agents, and Allocative Efficiency in an Artificial Stock Market

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  • Jing Yang

    ()
    (Concordia University)

Abstract

Various studies of asset markets have shown that traders are capable of learning. In this paper we replace human traders with artificial-intelligent software agents in a simulated stock market. They make predictions about the future, randomly submit their quotes, and transact at certain price. A simplified double auction market mechanism is employed. Three types of agents are included, value traders, momentum traders, and noise traders. Value traders form future expectation with an artificial neural network (ANN). They use ANN to predict dividend growth rate. Three computational experiments in terms of market participants are conducted. Time series from this market are analyzed from the standpoint of well-known empirical features in real markets, including GARCH, CAPM, and EMH tests. I extend earlier research in three ways. First, I employ a double auction market mechanism. I use this mechanism for two reasons. First, major real financial markets are organized as double auctions. Second, laboratory double auctions with human traders are known to yield data that approximate the equilibrium predictions of economic theory in a variety of environments. My second contribution is that I do not focus solely on equilibrium selection and convergence. I emphasize the behavior of the learning and market dynamics themselves. I analyze the portfolio returns and stock returns from this market to see whether the market exhibits characteristics cited in the empirical literature, including volatility persistence, GARCH, portfolio performance evaluation and allocative efficiency. The third extension is that I introduce the noise trader. The noise trader does not act strategically, but rather randomly posts a market order, and the resulting trade quantity is a randomly distributed variable.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 612.

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Date of creation: 01 Mar 1999
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Handle: RePEc:sce:scecf9:612

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  1. Forsythe, Robert & Palfrey, Thomas R. & Plott, Charles R., . "Asset Valuation in an Experimental Market," Working Papers 299, California Institute of Technology, Division of the Humanities and Social Sciences.
  2. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
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  4. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
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  9. Sanford Grossman, 1989. "The Informational Role of Prices," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262572141, December.
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  13. Tirole, Jean, 1982. "On the Possibility of Speculation under Rational Expectations," Econometrica, Econometric Society, vol. 50(5), pages 1163-81, September.
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  15. Blake LeBaron, . "Technical Trading Rules and Regime Shifts in Foreign Exchange," Working papers _007, University of Wisconsin - Madison.
  16. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
  17. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
  18. Kurz, Mordecai, 1994. "On Rational Belief Equilibria," Economic Theory, Springer, vol. 4(6), pages 859-76, October.
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