Summary
It is essential not only for investors but for regulators to understand the mechanisms that govern financial markets. However, financial markets are constantly evolving and are becoming more complex and as a consequence more difficult to analyze and understand. Traditional analytical methods cannot explain some of the phenomena which are present in real markets and some of the assumptions that had to be made for the sake of tractability in such models are over-simplistic. This opens the field to alternative methods that allow us to relax some of the most unrealistic assumptions in order to gain a better understanding of such complex systems. Agent-based computational economics (ACE) offers a suitable alternative for the study of financial markets. In this chapter we develop a software platform called Co-evolutionary, Heterogeneous Artificial Stock Market (CHASM); which allows us to perform a series of experiments with the purpose of identifying the aspects that could be responsible for the statistical properties (stylized facts) of financial prices. In CHASM, we model different types of traders: technical, fundamental and noise traders. However, we focus our research on technical traders represented as genetic programming (GP) based agents which co-evolve in the market forecasting price changes on the basis of technical indicators. We perform a detailed exploration of the market’s features in order to identify the conditions under which the stylized facts emerge. Moreover, we develop a behavioral constraint inspired by the Red Queen evolutionary principle to model endogenously the competitive pressure of the market.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alexandrova-Kabadjova, B., Tsang, E., Krause, A.: Market structure and information in payment card markets. Working Paper 06, Centre Computational Finance and Economic Agents (2006)
Alfarano, S., Wagner, F., Lux, T.: A minimal noise trader model with realistic time series properties. Economics Working Paper 2003–15, University of Kiel, Germany (2003)
Alfarano, S., Wagner, F., Lux, T.: Estimation of agent-based models: the case of an asymmetric herding model. Tech. rep., University of Kiel, Germany (2004)
Angeline, P.: Genetic Programming and Emergent Intelligence. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, ch. 4, pp. 75–98. MIT Press, Cambridge (1994)
Arifovic, J.: Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics & Control 18, 3–28 (1994)
Arifovic, J.: The behavior of the exchange rate in the genetic algorithm and experimental economics. Journal of Political Economy 104, 510–541 (1996)
Arifovic, J.: Evolutionary Dynamics of Currency Substitution. Journal of Economic Dynamics & Control 25, 395–417 (2001)
Arthur, W.B.: Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality. American Economic Review 81, 353–359 (1991)
Arthur, W.B.: On learning and adaptation in the economy. Working paper 92-07-038, Santa Fe Institute (1992)
Arthur, W.B.: Inductive reasoning and bounded rationality: The El Farol problem. American Economic Review 84, 406–411 (1994), citeseer.ist.psu.edu/arthur94inductive.html
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R.G., Talyer, P.: Asset pricing under endogenous expectations in an artificial stock market. In: Arthur, W.B., Durlauf, S., Lane, D. (eds.) The Economy as an Evolving Complex System II. Addison-Wesley, Reading (1997)
Bak, P., Paczuski, M., Shubik, M.: Price variations in a stock market with many agents. Physica A 246, 430–453 (1997)
Boswijk, H.P., Hommes, C.H., Manzan, S.: Behavioral heterogeneity in stock prices. In: IFAC symposium Computational Economics, Finance and Engineering, Seattle, USA (2003)
Brenner, T.: Agent learning representation: advice in modelling economic learning. In: Judd, K., Tesfatsion, L. (eds.) Handbook of Computational Economics. Agent-Based Computational Economics, vol. 2. Elsevier Science B.V., Amsterdam (2005)
Brock, W., Hommes, C.H.: A rational route to randomness. Econometrica 65, 1059–1095 (1997)
Brock, W., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47, 1731–1764 (1992)
Caldarelli, G., Marsili, M., Zhang, Y.C.: A prototype model of stock exchange. Europhysics Letters 40, 479–484 (1997)
Campbell, J.Y., Lo, A.W., MacKinlay, A.C.: The Econometrics of Financial Markets. Princeton University Press, Princeton (1997)
Challet, D., Zhang, Y.C.: Emergence of cooperation and organization in an evolutionary game. Physica A 246, 407 (1997)
Challet, D., Marsili, M., Zhang, Y.C.: Modeling market mechanism with minority game. Physica A 276, 284–315 (2000)
Chan, N.T., Shelton, C.R.: An electronic market maker. Technical Report 200-005, MIT Artificial Intelligence Laboratory (2001)
Chan, N.T., LeBaron, B., Lo, A.W., Poggio, T.: Agent-based models of financial markets: A comparison with experimental markets. MIT Sloan Working Paper 4195-01, Massachusetts Institute of Technology (2001)
Chen, S.H., Yeh, C.H.: Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics & Control 25(3-4), 363–393 (2001)
Chen, S.H., Yeh, C.H.: On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior & Organization 49(2), 217–239 (2002), http://www.sciencedirect.com/science/article/B6V8F-45F900X-8/2/c034ae35c111ca061a11cae1df4b2cd5
Chiarella, C., He, X.Z.: Asset price and wealth dynamics under heterogeneous expectations. Quantitative Finance 1, 509–526 (2001), citeseer.ist.psu.edu/chiarella01asset.html
Cincotti, S., Ponta, L., Raberto, M.: A multi-assets artifcial stock market with zero-intelligence traders. In: WEHIA 2005, Essex, United Kingdom, June 13-15 (2005)
Cliff, D., Bruten, J.: Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets. In: First Hewlett Packard International Workshop on Interacting Software Agents, Bristol, United Kingdom (1997)
Cliff, D., Miller, G.F.: Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations. In: Proceedings of the Third European Conference on Advances in Artificial Life, pp. 200–218. Springer, London (1995)
Cont, R.: Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance 1(2), 223–236 (2001), citeseer.ist.psu.edu/cont01empirical.html
Cont, R., Bouchaud, J.P.: Herd behavior and aggregate fluctuations in financial markets. Macroeconomic Dynamics 4, 170–196 (2000)
Dempster, M.A.H., Payne, T.W., Romahi, Y., Thompson, G.W.P.: Computational learning techniques for intraday FX trading using popular technical indicators. IEEE Transactions on Neural Networks 12(4), 744–754 (2001), http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf
Edmonds, B.: Modelling bounded rationality in agent-based simulations using the evolution of mental models. In: Brenner, T. (ed.) Computational Techniques for Modelling Learning in Economics, pp. 305–332. Kluwer, Dordrecht (1999), citeseer.ist.psu.edu/edmonds99modelling.html
Edmonds, B., Moss, S.: Modelling bounded rationality using evolutionary techniques. In: Evolutionary Computing, AISB Workshop, pp. 31–42 (1997), citeseer.ist.psu.edu/edmonds97modelling.html
Edmonds, B., Moss, S.: The importance of representing cognitive processes in multi-agent models. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 759–766. Springer, Heidelberg (2001)
Fama, E.F.: Efficient capital markets: A review of theory and empirical work. The Journal of Finance 23, 383–417 (1970)
Fama, E.F.: Efficient capital markets II. The Journal of Finance 46, 1575–1617 (1991)
Fama, E.F., French, K.: Size and book-to-market factors in earnings and stock returns. The Journal of Finance 50, 131–155 (1995)
Farmer, J., Joshi, S.: The price dynamics of common trading strategies. SFI Working Paper 00-12-069, Santa Fe Institute (2000), citeseer.ist.psu.edu/farmer00price.html
Farmer, J.D.: Market force, ecology, and evolution. Industrial and Corporate Change 11, 895–953 (1998), citeseer.ist.psu.edu/farmer98market.html
Farmer, J.D., Patelli, P., Zovko II: The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences of the United States of America 102, 2254–2259 (2005)
Franci, F., Matassini, L.: Life in the stockmarket - a realistic model for trading (2000)
Garcia-Almanza, A.L., Tsang, E.P.: The repository method for chance discovery in financial forecasting. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS, vol. 4253, pp. 30–37. Springer, Heidelberg (2006)
Ghoulmie, F., Cont, R., Nadal, J.P.: Heterogeneity and feedback in an agent-based market model. Journal of Physics: Condensed Matter 17, S1259–S1268 (2005)
Giardina, I., Bouchaud, J.P.: Bubbles, crashes and intermittency in agent based market models. The European Physical Journal B - Condensed Matter 31, 421–437 (2003)
Gilli, M., Winker, P.: A global optimization heuristic for estimating agent based models. Computational Statistics & Data Analysis 42, 299–312 (2003)
Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero intelligence (z1) traders: Market as a partial substitute forindividual rationality. GSIA Working Papers 1992-16, Carnegie Mellon University, Tepper School of Business (1991)
Grothmann, R.: Multi-agent market modeling based on neural networks. PhD thesis, Faculty of Economics, University of Bremen (2002)
Hill, B.M.: A simple general approach to inference about the tail of a distribution. Annals of Statistics 3, 1163–1173 (1975)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Holland, J.H., Miller, J.H.: Artificial adaptive agents in economic theory. The American Economic Review 81, 365–370 (1991)
Jefferies, P., Hart, M., Hui, P., Johnson, N.: From market games to real-world markets. The European Physical Journal B - Condensed Matter and Complex Systems 20, 493–501 (April)
Juille, H., Pollack, J.B.: Dynamics of co-evolutionary learning. In: Maes, P., Mataric, M.J., Meyer, J.A., Pollack, J., Wilson, S.W. (eds.) Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4, pp. 526–534. MIT Press, Cambridge (1996), http://www.demo.cs.brandeis.edu/papers/sab96b.pdf
Kirman, A.P.: Whom or What Does the Representative Individual Represents? The Journal of Economic Perspectives 6, 117–136 (1992)
Kirman, A.P.: Ants, rationality and recruitment. The Quarterly Journal of Economics 108, 137–156 (1993)
Kirman, A.P., Vriend, N.J.: Evolving market structure: An ace model of price dispersion and loyalty. Journal of Economic Dynamics & Control 25, 459–502 (2001)
Koza, J.R.: Evolution and co-evolution of computer programs to control independent-acting agents. In: Meyer, J.A., Wilson, S.W. (eds.) From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, September 24-28, 1990, pp. 366–375. MIT Press, Paris (1991)
Langdon, W.B., Poli, R.: Fitness causes bloat. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer, London (1997)
LeBaron, B.: A builder’s guide to agent based financial markets. Quantitative Finance 1, 254–261 (2001)
LeBaron, B.: Evolution and time horizons in an agent-based stock market. Macroeconomic Dynamics 5, 225–254 (2001)
LeBaron, B.: Calibrating an agent-based financial market. Working paper, Graduate School of International Economics and Finance, Brandeis University (2003)
LeBaron, B.: Agent-based computational finance. In: Judd, K., Tesfatsion, L. (eds.) Handbook of Computational Economics. Agent-Based Computational Economics, vol. 2. Elsevier Science B.V., Amsterdam (2005)
LeBaron, B.: Agent-based financial markets: Matching stylized facts with style. In: Colander, D.C. (ed.) Post Walrasian Macroeconomics Beyond the Dynamic Stochastic General Equilibrium Model. Cambridge University Press, Cambridge (2006)
LeBaron, B., Arthur, W.B., Palmer, R.G.: Time series properties of an artificial stock market. Journal of Economic Dynamics & Control 23, 1487–1516 (1999)
Levy, H., Levy, M., Solomon, S.: Microscopic Simulation of Financial Markets: From Investor Behavior to Market Phenomena. Academic Press, London (2000)
Levy, M., Solomon, S.: Dynamical explanation for the emergence of power law in a stock market model. International Journal of Modern Physics C 7, 65–72 (1996)
Levy, M., Levy, H., Solomon, S.: A microscopic model of the stock market: cycles, booms and crashes. Economics Letters 45, 103–111 (1994)
Li, J., Tsang, E.P.K.: Investment decision making using FGP: A case study. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1253–1259. IEEE Press, Washington (1999), http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/CEC9%9.pdf
LiCalzi, M., Pellizzari, P.: Fundamentalists clashing over the book: a study of order-driven stock markets. Quantitative Finance 3, 1–11 (2003)
Lo, A., Mackinlay, A.C.: Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies 1, 41–66 (1988), citeseer.ist.psu.edu/lo88stock.html
Lucas, R.E.: Adaptive behavior and economic theory. In: Hogarth, R.M., Reder, M.W. (eds.) Rational Choice: The Contrast between Economics and Psychology, pp. 217–242. University of Chicago Press (1986)
Lux, T.: Herd behaviour, bubbles and crashes. The Economic Journal 105, 881–896 (1995)
Lux, T.: The socio-economic dynamics of speculative markets: Interacting agents, chaos, and the fat tails of return distributions. Journal of Economic Behavior and Organization 33, 143–165 (1998)
Lux, T.: The limiting extremal behaviour of speculative returns: an analysis of intra-daily data from the frankfurt stock exchange. Applied Financial Economics 11, 299–315 (2001)
Lux, T., Ausloos, M.: Market fluctuations i: Scaling, multiscaling and their possible origins. In: Bunde, A., Kropp, J., Schellnhuber, H.J. (eds.) Theories of Disaster - Scaling Laws Governing Weather, Body, and Stock Market Dynamics, pp. 373–409. Springer, Heidelberg (2002)
Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 498–500 (1999)
Malkiel, B.G.: A Random Walk Down Wall Street, 1st edn. W. W. Norton & Co., New York (1973)
Mandelbrot, B.B.: The variation of certain speculative prices. Journal of Business 36, 394–419 (1963)
Markose, S., Tsang, E.P.K., Martinez-Jaramillo, S.: The Red Queen principle and the emergence of efficient financial markets: an agent based approach. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) 8th Workshop on economics and heterogeneous interacting agents (WEHIA), vol. 2, pp. 1253–1259. Springer, Germany (2003)
Marsili, M.: Toy models of markets with heterogeneous interacting agents in economics with heterogeneous interacting agents. In: Kirman, A., Zimmerman, J.B. (eds.) Economics With Heterogeneous Interacting Agents, vol. 503, p. 161. Springer, Heidelberg (2001), citeseer.ist.psu.edu/marsili01toy.html
Marsili, M., Challet, D.: Trading behavior and excess volatility in toy markets. Adv. Complex Systems 1, 1–14 (2001)
Martinez-Jaramillo, S., Tsang, E.P.K.: An heterogeneous, endogenous and co-evolutionary gp-based financial market. IEEE Transactions on Evolutionary Computation (forthcomming)
Neely, C.J., Weller, P., Dittmar, R.: Is technical analysis in the foreign exchange market profitable? a genetic programming approach. Financial and Quantitative Analysis 32, 405–426 (1997)
Pagie, L., Hogeweg, P.: Information Integration and Red Queen Dynamics in Coevolutionary Optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation CEC 2000, pp. 1260–1267. IEEE Press, Los Alamitos (2000), citeseer.ist.psu.edu/pagie00information.html
Palmer, R.G., Arthur, W.B., Holland, J.H., LeBaron, B., Tyler, P.: Artificial economic life: A simple model of a stock market. Physica D 75, 264–274 (1994)
Palmer, R.G., Arthur, W.B., Holland, J.H., LeBaron, B.: An artificial stock market. Artificial Life and Robotics 3, 27–31 (1999)
Paredis, J.: Coevolving Cellular Automata: Be Aware of the Red Queen? In: Baeck, T. (ed.) Proceedings of the 7th Int. Conference on Genetic Algorithms (ICGA 1997). Morgan Kaufmann Publishers, San Francisco (1997)
Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Proceedings of the 6th European Conference, pp. 204–217. Springer, Heidelberg (2003)
Raberto, M., Cincotti, S.: Modeling and simulation of a double auction artificial financial market. Physica A: Statistical Mechanics and its Applications 355(1), 34–45 (2005)
Robson, A.J.: The evolution of rationality and the Red Queen. Journal of Economic Theory 111(1), 1–22 (2003)
Robson, A.J.: Complex Evolutionary Systems and the Red Queen. Economic Journal 115(504), F211–F224 (2005)
Schulenburg, S., Ross, P.: Strength and money: An LCS approach to increasing returns. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS, vol. 1996, p. 114. Springer, Heidelberg (2001), citeseer.ist.psu.edu/schulenburg01strength.html
Schulenburg, S., Ross, P.: Explorations in lcs models of stock trading. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS, vol. 2321, pp. 150–179. Springer, Heidelberg (2002), citeseer.ist.psu.edu/673677.html
Shiller, R.J.: From efficient market theory to behavioral finance. Journal of Economic Perspectives 17, 83–104 (2003)
Shleifer, A.: Inefficient Markets: An Introduction to Behavioral Finance. Clarendon Lectures in Economics. Oxford University Press, Oxford (2000)
Siegel, E.V.: Competitively evolving decision trees against fixed training cases for natural language processing. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, vol. 19, pp. 409–423. MIT Press, Cambridge (1994)
Simon, H.A.: A behavioral model of rational choice. The Quarterly Journal of Economics 69, 99–118 (1955)
Simon, H.A.: Models of Bounded Rationality. MIT Press, Cambridge (1982)
Simon, H.A.: A mechanism for social selection and successful altruism. Science 250(4988), 1665–1668 (1990), http://www.sciencemag.org/cgi/content/abstract/250/4988/1665 , http://www.sciencemag.org/cgi/reprint/250/4988/1665.pdf
Sunder, S.: Market as an artifact aggregate efficiency from zero intelligence traders. In: Augier, M.E., March, J.G. (eds.) Models of a Man: Essays in Memory of Herbert A. Simon, pp. 501–519. MIT Press, Cambridge (2004)
Taylor, M.P., Allen, H.: The use of technical analysis in the foreign exchange market. Journal of International Money and Finance, 304–314 (1992)
Tesfatsion, L.: Agent-based computational economics: Growing economies from the bottom up. Artificial Life 8, 55–82 (2002), citeseer.ist.psu.edu/article/tesfatsion02agentbased.html
Tsang, E.P.K., Martinez-Jaramillo, S.: Computational finance. In: IEEE Computational Intelligence Society Newsletter, pp. 3–8. IEEE Press, Los Alamitos (2004)
Tsang, E.P.K., Li, J., Butler, J.M.: EDDIE beats the bookies. Software: Practice and Experience 28(10), 1033–1043 (1998), http://www3.interscience.wiley.com/cgi-bin/abstract/10007354/%START
Tsang, E.P.K., Li, J., Markose, S., Er, H., Salhi, A., Iori, G.: EDDIE in financial decision making. Journal of Management and Economics (2000), http://privatewww.essex.ac.uk/~scher/EDDIE%20PROJ/Tsang-Eddie%-JMgtEcon2000.doc
Tsang, E.P.K., Yung, P., Li, J.: EDDIE-automation, a decision support tool for financial forecasting. Decision Support Systems, Special Issue on Data Mining for Financial Decision Making 37(4), 559–565 (2004), http://www.sciencedirect.com/science/article/B6V8S-4903GV9-1/%2/d6ba531a46ce45526ff9015e4447409a
Tsang, E.P.K., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage. In: New Mathematics and Natural Computation, vol. 1, pp. 435–447. World Scientific, Singapore (2005)
van Valen, L.: A new evolutionary law. Evolutionary Theory 1, 1–30 (1973)
Westerhoff, F.H.: Multi-asset market dynamics. Macroeconomic Dynamics 8, 596–616 (2004)
Winker, P., Gilli, M.: Indirect estimation of the parameters of agent based models of financial markets. Fame research paper, University of Geneva (2001)
Yang, J.: The efficiency of an artificial double auction stock market with neural learning agents. Evolutionary Computation in Economics and Finance, 85–106 (2002)
Zimmermann, H., Neuneier, R., Grothmann, R.: An approach of multi-agent FX-market modelling based on cognitive systems. In: Procedings of the International Conference on Artificial Neural Networks (ICANN), Viena (2001)
Zimmermann, H., Neuneier, R., Grothmann, R.: Multi-agent modeling of multiple FX-markets by neural networks. IEEE Transactions on Neural Networks, Special issue 12, 735–743 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Martinez-Jaramillo, S., Tsang, E.P.K. (2009). Evolutionary Computation and Artificial Financial Markets. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95974-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-95974-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-95973-1
Online ISBN: 978-3-540-95974-8
eBook Packages: EngineeringEngineering (R0)