The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents

The goal of this paper is to investigate the convergence property in a double auction market and test the robustness of the results. We construct an artificial equity market where agents trade a risky asset that pays a stochastic dividend each period. Artificial Neural Networks take on the role of traders, who form their expectations about the future return and place orders based on their expectations. Market prices are set endogenously by trading among agents in a double auction market. The efficiency of this artificial market is measured by the convergence of the price to the Rational Expectations Equilibrium (REE). We find that market dynamics under double auction converge to the REE in in some ex-periments. This convergence, however, is sensitive to the deviation from rationality among the agents. In the experiment where we introduce noise trading, convergence becomes unattainable. Minimal rationality is not sufficient to generate convergence in a double auction market when the market price is endogenous.

[1]  C. Plott,et al.  Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets , 1988 .

[2]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[3]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[4]  Jasmina Arifovic,et al.  The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies , 1996, Journal of Political Economy.

[5]  Halbert White,et al.  Artificial Neural Networks: Approximation and Learning Theory , 1992 .

[6]  C. Plott,et al.  Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models , 1982, Journal of Political Economy.

[7]  William N. Goetzmann,et al.  The Dow Theory: William Peter Hamilton's Track Record Re-Considered , 1998 .

[8]  A. Lo,et al.  THE ECONOMETRICS OF FINANCIAL MARKETS , 1996, Macroeconomic Dynamics.

[9]  V. Smith,et al.  Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets , 1988 .

[10]  J. Hussman.  Market efficiency and inefficiency in rational expectations equilibria: Dynamic effects of heterogeneous information and noise , 1992 .

[11]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[12]  Maureen O'Hara,et al.  Market Microstructure Theory , 1995 .

[13]  B. LeBaron,et al.  Simple Technical Trading Rules and the Stochastic Properties of Stock Returns , 1992 .

[14]  Blake LeBaron,et al.  Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders , 1998 .

[15]  R. Palmer,et al.  Time series properties of an artificial stock market , 1999 .

[16]  Keith Weigelt,et al.  Convergence in experimental double auctions for stochastically live assets , 1993 .

[17]  J. Tirole.  On the Possibility of Speculation under Rational Expectations , 1982 .

[18]  Sergio Margarita,et al.  Stock Prices and Volume in an Artificial Adaptive Stock Market , 1993, IWANN.

[19]  B. LeBaron.  Technical Trading Rules and Regime Shifts in Foreign Exchange , 1991 .

[20]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[21]  R. Palmer,et al.  Asset Pricing Under Endogenous Expectations in an Artificial Stock Market , 1996 .

[22]  Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market , 1999 .

[23]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[24]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[25]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[26]  T. Sargent.  Bounded rationality in macroeconomics , 1993 .

[27]  S. Satchell,et al.  Advanced trading rules , 2002 .

[28]  T. Palfrey,et al.  Asset Valuation in an Experimental Market , 1982 .

[29]  Blake LeBaron,et al.  Agent-based computational finance : Suggested readings and early research , 2000 .

[30]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[31]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[32]  Sanford J. Grossman.  On the Impossibility of Informationally Efficient Markets , 1980 .

[33]  M. Bray.  Learning, estimation, and the stability of rational expectations , 1982 .