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LEARNING DYNAMICS AND NONLINEAR MISSPECIFICATION IN AN ARTIFICIAL FINANCIAL MARKET

Published online by Cambridge University Press:  30 October 2009

Christophre Georges  and
John C. Wallace
Christophre Georges*
Affiliation:
Hamilton College
John C. Wallace
Affiliation:
Hamilton College
*
Address correspondence to: Christophre Georges, Department of Economics, Hamilton College, Clinton, NY 13323, USA; e-mail: cgeorges@hamilton.edu; URL: http://academics.hamilton.edu/economics/cgeorges/.
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Abstract

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

Keywords

LearningExpectationsAgent-Based Model

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 13 , Issue 5 , November 2009 , pp. 625 - 655
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Adam, Klaus (2005) Learning to forecast and cyclical behavior of output and inflation. Macroeconomic Dynamics 9, 1–27.CrossRefGoogle Scholar
Adam, Klaus, Marcet, Albert, and Nicolini, Juan Pablo (2006) Stock Market Volatility and Learning. Manuscript, European Central Bank.Google Scholar
Alfarano, Simone and Lux, Thomas (2007) A noise trader model as a generator of apparent financial power laws and long memory. Macroeconomic Dynamics 11 (Supplement 1), 80–101.CrossRefGoogle Scholar
Allen, Todd M. and Carroll, Christopher (2001) Individual learning about consumption. Macroeconomic Dynamics 5 (4), 255–271.CrossRefGoogle Scholar
Andreoni, James and Miller, John H. (1995) Auctions with adaptive artificial agents. Journal of Games and Economic Behavior 10, 39–64.CrossRefGoogle Scholar
Aragones, Enriqueta, Gilboa, Itzhak, Postlewaite, Andrew, and Schmeidler, David (2005) Fact free learning. American Economic Review 95 (9), 1355–1368.CrossRefGoogle Scholar
Arifovic, Jasmina (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 3–28.CrossRefGoogle Scholar
Arifovic, Jasmina, Bullard, James, and Kostyshyna, Olena (2007) Social Learning and Monetary Policy Rules. Manuscript, Simon Fraser University.CrossRefGoogle Scholar
Arthur, W. Brian, Holland, John H., LeBaron, Blake, Palmer, Richard, and Taylor, Paul (1997) Asset pricing under endogenous expectations in an artificial stock market. In Arthur, W. Brian, Durlauf, Steven N., and Lane, David A. (eds.), The Economy as Evolving Complex System II, pp. 15–44. Reading, MA: Addison–Wesley.Google Scholar
Audet, Nicolas, Gravelle, Toni, and Yang, Jing (2002) Alternative Trading Systems: Does One Shoe Fit All? Bank of Canada Working Paper 2002-33.Google Scholar
Boswijk, H. Peter, Hommes, Cars H., and Manzan, Sebastiano (2007) Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control 31, 1938–1970.CrossRefGoogle Scholar
Branch, William A. and Evans, George W. (2006) Intrinsic heterogeneity in expectation formation. Journal of Economic Theory 127 (1), 264–295.CrossRefGoogle Scholar
William, A. and Hommes, Cars H. (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 1235–1274.Google Scholar
Brock, William A., Hommes, Cars H., and Wagener, Florian (2005) Evolutionary dynamics in financial markets with many trader types. Journal of Mathematical Economics 41, 7–42.CrossRefGoogle Scholar
Bullard, James and Duffie, John (1999) Using genetic algorithms to model the evolution of heterogeneous beliefs. Computational Economics 13, 41–60.CrossRefGoogle Scholar
Bullard, James and Duffie, John (1998a) Learning and the stability of cycles. Macroeconomic Dynamics 2, 22–48.CrossRefGoogle Scholar
Bullard, James and Duffie, John (1998b) Model of learning and emulation with artificial agents. Journal of Economic Dynamics and Control 22, 179–207.CrossRefGoogle Scholar
Bullard, James, Evans, George W., and Honkapohja, Seppo (2007) A Model of Near-Rational Exuberance. Manuscript, Federal Reserve Bank of St. Louis.CrossRefGoogle Scholar
Chakrabarti, Rajesh (2000) Just another day in the inter-bank foreign exchange market. Journal of Financial Economics 56, 29–64.CrossRefGoogle Scholar
Chan, Nicholas T., LeBaron, Blake, Lo, Andrew W., and Poggio, Tomaso (1999) Agent-Based Models of Financial Markets: A Comparison with Experimental Markets. Manuscript, Brandeis University.Google Scholar
Chen, Shu-Heng and Yeh, Chia-Hsuan (2001) Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25 (3–4), 363–393.CrossRefGoogle Scholar
Chen, Shu-Heng and Yeh, Chia-Hsuan (2002) On the emergent properties of artificial stock markets: The efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior and Organization 49 (1), 217–239.CrossRefGoogle Scholar
Chiarella, Carl and He, Xue-Zhong (2002) Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19, 95–132.CrossRefGoogle Scholar
Chiarella, Carl and Iori, Giulia (2002) A simulation analysis of the microstructure of double auction markets. Quantitative Finance 2, 346–353.CrossRefGoogle Scholar
Chiarella, Carl and Iori, Giulia (2004) The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows. Manuscript, University of Technology, Sydney.CrossRefGoogle Scholar
Cho, In-Koo, Williams, Noah, and Sargent, Thomas J. (2002) Escaping Nash inflation. Review of Economic Studies 69, 1–40.CrossRefGoogle Scholar
Darley, Vincent and Outkin, Alexander V. (2007) NASDAQ Market Simulation: Insights on a Major Market From the Science of Complex Adaptive Systems. Singapore: World Scientific Publishing.CrossRefGoogle Scholar
Diether, Karl B., Malloy, Christopher J., and Scherbina, Anna (2002) Differences of opinion and the cross-section of stock returns. Journal of Finance 57, 2113–2141.CrossRefGoogle Scholar
Duffie, Darrell, Garleanu, Nicolae, and Pedersen, Lasse Heje (2005) Over-the-counter-markets. Econometrica 73 (6), 1815–1847.CrossRefGoogle Scholar
Evans, George W. and Honkapohja, Seppo (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Farmer, J. Doyne and Joshi, Shareen (2002) The price dynamics of common trading strategies. Journal of Economic Behavior and Organization 49 (1), 149–171.CrossRefGoogle Scholar
Farmer, J. Doyne, Patelli, Paolo, and Zovko, Ilija I. (2005) The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences 102, 2254–2259.CrossRefGoogle ScholarPubMed
Fogel, David B. (2000) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2nd ed.New York: IEEE Press.Google Scholar
Georges, Christophre (2002) Learning Dynamics with Nonlinear Misspecification. Manuscript, Hamilton College.Google Scholar
Georges, Christophre (2006) Learning with misspecification in an artificial currency market. Journal of Economic Behavior and Organization 60, 70–84.CrossRefGoogle Scholar
Georges, Christophre (2008a) Bounded memory, overparameterized forecast rules, and instability. Economics Letters 98 (2), 129–135.CrossRefGoogle Scholar
Georges, Christophre (2008b) Staggered updating in an artificial financial market. Journal of Economic Dynamics and Control 32 (9), 2809–2825.CrossRefGoogle Scholar
Gigerenzer, Gerd and Selten, Reinhard (eds.) (2001) Bounded Rationality: The Adaptive Toolbox. Cambridge, MA: MIT Press.Google Scholar
Gode, Dhananjay K. and Sunder, Shyam (1993) Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economics 101 (1), 119–137.CrossRefGoogle Scholar
Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley.Google Scholar
Grandmont, Jean-Michel (1998) Expectations formation and stability of large socioeconomic systems. Econometrica 66 (4), 741–781.CrossRefGoogle Scholar
Harris, Larry (2003) Trading and Exchanges: Market Microstructure for Practitioners. Oxford, UK: Oxford University Press.Google Scholar
Herrera, F., Lozano, M., and Verdegay, J.L. (1998) Tackling real-coded GAs: Operators and tools for behavioural analysis. Artificial Intelligence Review 12, 265–319.CrossRefGoogle Scholar
Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press [2nd ed.: MIT Press, 1992].Google Scholar
Holland, John H. and Miller, John H. (1991) Artificial adaptive agents in economic theory. American Economic Review Papers and Proceedings 81, 365–370.Google Scholar
Hommes, Cars (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, Leigh and Judd, Kenneth L. (eds.), Handbook of Computational Economics: Vol. 2, Agent-Based Computational Economics, pp. 1109–1186. Amsterdam: Elsevier.Google Scholar
Hommes, Cars and Sorger, Gerhard (1998) Consistent expectations equilibria. Macroeconomic Dynamics 2, 287–321.CrossRefGoogle Scholar
Honkapohja, Seppo and Mitra, Kaushik (2003) Learning with bounded memory in stochastic models. Journal of Economic Dynamics and Control 27 (8), 1437–1457.CrossRefGoogle Scholar
Lagos, Ricardo and Rocheteau, Guillaume (2007) Search in asset markets: Market structure, liquidity, and welfare. American Economic Review 97 (2), 198–202.CrossRefGoogle Scholar
LeBaron, Blake (2006) Agent based computational finance. In Tesfatsion, Leigh and Judd, Kenneth L. (eds.), Handbook of Computational Economics: Vol. 2, Agent-Based Computational Economics, pp. 1187–1233. Amsterdam: Elsevier.Google Scholar
LeBaron, Blake, Arthur, W. Brian, and Palmer, Richard (1999) Time series properties of an artificial stock market. Journal of Economic Dynamics and Control 23, 1487–1516.CrossRefGoogle Scholar
LeBaron, Blake and Yamamoto, Ryuichi (2006) Long-Memory in an Order-Driven Market. Manuscript, Brandeis University.CrossRefGoogle Scholar
Lux, Thomas (1995) Herd behavior, bubbles, and crashes. Economic Journal 105, 881–896.CrossRefGoogle Scholar
Lux, Thomas and Ausloos, Marcel (2002) Market fluctuations I: Scaling, multi-scaling and their possible origins. In Bunde, A. and Schellnhuber, H.J. (eds.), Theories of Disasters: Scaling Laws Governing Weather, Body and Stock Market Dynamics, pp. 372–409. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Lux, Thomas and Marchesi, Michele (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 498–500.CrossRefGoogle Scholar
Lux, Thomas and Schornstein, Sascha (2005) Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics 41, 169–196.CrossRefGoogle Scholar
McCallum, Bennett T. (1983) On non-uniqueness in rational expectations models: An attempt at perspective. Journal of Monetary Economics 11, 139–168.CrossRefGoogle Scholar
McCallum, Bennett T. (1999) Role of the minimal state variable criterion in rational expectations models. International Tax and Public Finance 6, 621–639.CrossRefGoogle Scholar
Michalewicz, Zbigniew (1996) Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed.New York: Springer.CrossRefGoogle Scholar
Mitchell, Melanie (1996) An Introduction of Genetic Algorithms. Cambridge, MA: MIT Press.Google Scholar
Samanidou, E.E. Zschischang, Stauffer, D., and Lux, T. (2007) Agent-based models of financial markets. Reports on Progress in Physics 70, 409–450.CrossRefGoogle Scholar
Sargent, Thomas J. (1999) The Conquest of American Inflation. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Vriend, Nicholas J. (2000) An illustration of the essential difference between individual and social learning, and its consequences for computational analysis. Journal of Economic Dynamics and Control 24, 1–19.CrossRefGoogle Scholar
Yang, Jing (2002) The efficiency of an artificial double auction stock market with neural learning agents. In Chen, S.H. (ed.), Evolutionary Computation in Economics and Finance, pp. 85–105. New York: Springer-Verlag.CrossRefGoogle Scholar
Yeh, Chia-Hsuan (2003) Tick Size and Market Performance. Manuscript, Department of Information Management, Yuan Ze University.Google Scholar
Youssefmir, Michael and Huberman, Bernardo (1997) Clustered volatility in multiagent dynamics. Journal of Economic Behavior and Organization 32, 101–118.CrossRefGoogle Scholar