Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Bounded Rational Heterogeneous Agents in Artificial Stock Markets: Literature Review and Research Direction
Authors: Talal Alsulaiman, Khaldoun Khashanah
Abstract:
In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent’s attributes. Also, the influence of social networks in the developing of agents interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.Keywords: Artificial stock markets, agent based simulation, bounded rationality, behavioral finance, artificial neural network, interaction, scale-free networks.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108122
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2527References:
[1] A. Smith, “The wealth of nations (1776),” New York: Modern Library, vol. 740, 1937.
[2] B. G. Malkiel and E. F. Fama, “Efficient capital markets: A review of theory and empirical work*,” The journal of Finance, vol. 25, no. 2, pp. 383–417, 1970.
[3] P. A. Samuelson, “Proof that properly anticipated prices fluctuate randomly,” Industrial management review, vol. 6, no. 2, pp. 41–49, 1965.
[4] S. F. LeRoy, “Efficient capital markets and martingales,” Journal of Economic Literature, pp. 1583–1621, 1989.
[5] M. Allais, “Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’´ecole am´ericaine,” Econometrica: Journal of the Econometric Society, pp. 503–546, 1953.
[6] K. Khashanah, “Systems taxonomy,” in 4th International Engineering Systems Symposium, 2014.
[7] H. A. Simon, “A behavioral model of rational choice,” The quarterly journal of economics, vol. 69, no. 1, pp. 99–118, 1955.
[8] H. A. Simon, “From substantive to procedural rationality,” 25 Years of Economic Theory, pp. 65–86, 1976.
[9] H. A. Simon, “Rational choice and the structure of the environment.” Psychological review, vol. 63, no. 2, p. 129, 1956.
[10] H. A. Simon, “Theories of bounded rationality,” Decision and organization, vol. 1, pp. 161–176, 1972.
[11] H. A. Simon, “Models of bounded rationality: Empirically grounded economic reason,” 1982.
[12] D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica: Journal of the Econometric Society, pp. 263–291, 1979.
[13] K. Dehnad, “Behavioral finance and technical analysis,” One company has the precision and focus to help you redefine value in a competitive market., p. 107, 2011.
[14] N. Barberis and R. Thaler, “A survey of behavioral finance,” Handbook of the Economics of Finance, vol. 1, pp. 1053–1128, 2003.
[15] M. M. Pompian, “Behavioral finance and wealth management,” How to build optimalportfoliosforprivate clients, 2006.
[16] J. D. Farmer and A. W. Lo, “Frontiers of finance: Evolution and efficient markets,” Proceedings of the National Academy of Sciences, vol. 96, no. 18, pp. 9991–9992, 1999.
[17] A. W. Lo, “The adaptive markets hypothesis,” The Journal of Portfolio Management, vol. 30, no. 5, pp. 15–29, 2004.
[18] R. J. Shiller, “From efficient markets theory to behavioral finance,” Journal of economic perspectives, pp. 83–104, 2003.
[19] A. W. Lo, “Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis,” Journal of Investment Consulting, vol. 7, no. 2, pp. 21–44, 2005.
[20] M. Mitchell, Complexity: A guided tour. Oxford University Press, 2009.
[21] J. H. Miller and S. E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life: An Introduction to Computational Models of Social Life. Princeton University Press, 2009.
[22] B. LeBaron, “A builders guide to agent-based financial markets,” Quantitative Finance, vol. 1, no. 2, pp. 254–261, 2001.
[23] B. LeBaron, “Agent-based computational finance,” Handbook of computational economics, vol. 2, pp. 1187–1233, 2006.
[24] R. Marks, “Market design using agent-based models,” Handbook of computational economics, vol. 2, pp. 1339–1380, 2006.
[25] V. Darley and A. V. Outkin, Nasdaq Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems. World Scientific Publishing Co., Inc., 2007.
[26] S. Martinez-Jaramillo, “Artificial financial markets: an agent based approach to reproduce stylized facts and to study the red queen effect,” Ph.D. dissertation, University of Essex, 2007.
[27] C. H. Hommes, “Financial markets as nonlinear adaptive evolutionary systems,” 2001.
[28] M. Lovric, Behavioral Finance and Agent-Based Artificial Markets. Erasmus Research Institute of Management (ERIM), 2011, no. EPS-2011-229-F&A.
[29] H. Levy, M. Levy, and S. Solomon, Microscopic simulation of financial markets: from investor behavior to market phenomena. Academic Press, 2000.
[30] N. J. Vriend, “Ace models of endogenous interactions,” Handbook of computational economics, vol. 2, pp. 1047–1079, 2006.
[31] T. C. Schelling, “Dynamic models of segregation,” Journal of mathematical sociology, vol. 1, no. 2, pp. 143–186, 1971.
[32] J. M. Epstein, Growing artificial societies: social science from the bottom up. Brookings Institution Press, 1996.
[33] W. B. Arthur, “Inductive reasoning and bounded rationality,” American Economic Review, vol. 84, no. 2, pp. 406–411, 1994.
[34] N. J. Vriend, “Self-organization of markets: An example of a computational approach,” Computational Economics, vol. 8, no. 3, pp. 205–231, 1995.
[35] A. P. Kirman and N. J. Vriend, “Evolving market structure: An ace model of price dispersion and loyalty,” Journal of Economic Dynamics and Control, vol. 25, no. 3, pp. 459–502, 2001.
[36] M. O. Jackson, Social and economic networks. Princeton University Press, 2010.
[37] D. Easley and J. Kleinberg, “Networks, crowds, and markets,” Cambridge Univ Press, vol. 6, no. 1, pp. 6–1, 2010.
[38] A. Wilhite, “Economic activity on fixed networks,” Handbook of computational economics, vol. 2, pp. 1013–1045, 2006.
[39] S.-H. Chen and Y.-C. Huang, “Simulating the evolution of portfolio behavior in a multiple-asset agent-based artificial stock market,” in 9th International Conference on Computing in Economics and Finance, University of Washington, Seattle, USA, 2003.
[40] D. K. Gode and S. Sunder, “Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality,” Journal of political economy, vol. 101, no. 1, p. 119, 1993.
[41] N. T. Chan and C. Shelton, “An electronic market-maker,” 2001.
[42] J. Yang, “The efficiency of an artificial double auction stock market with neural learning agents,” in Evolutionary Computation in Economics and Finance. Springer, 2002, pp. 85–105.
[43] B. LeBaron, W. B. Arthur, and R. Palmer, “Time series properties of an artificial stock market,” Journal of Economic Dynamics and control, vol. 23, no. 9, pp. 1487–1516, 1999.
[44] S.-H. Chen and C.-H. Yeh, “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, vol. 25, no. 3, pp. 363–393, 2001.
[45] T. Brenner, “Agent learning representation: advice on modelling economic learning,” Handbook of computational economics, vol. 2, pp. 895–947, 2006.
[46] D. E. Goldberg and J. H. Holland, “Genetic algorithms and machine learning,” Machine learning, vol. 3, no. 2, pp. 95–99, 1988.
[47] Z. Michalewicz, Genetic algorithms+ data structures= evolution programs. springer, 1996.
[48] M. Mitchell, An introduction to genetic algorithms. MIT press, 1998.
[49] W.-Y. Lin, W.-Y. Lee, and T.-P. Hong, “Adapting crossover and mutation rates in genetic algorithms,” J. Inf. Sci. Eng., vol. 19, no. 5, pp. 889–903, 2003.
[50] W. Arthur, J. Holland, B. LeBaron, and R. Palmer, “P. tayler, 1997,asset pricing under endogenous expectations in an artificial stock market,” The Economy as an Evolving Complex System II, Reading, Ma: Addison-Wesley, pp. 15–44.
[51] J. R. Koza, “Genetic programming as a means for programming computers by natural selection,” Statistics and Computing, vol. 4, no. 2, pp. 87–112, 1994.
[52] R. Poli, W. B. Langdon, N. F. McPhee, and J. R. Koza, “A field guide to genetic programming,” 2008.
[53] M. Smith, Neural networks for statistical modeling. Thomson Learning, 1993.
[54] J. A. Anderson, An introduction to neural networks. MIT press, 1995.
[55] R. Rojas, Neutral Networks: A Systematic Introduction. Springer, 1996.
[56] K. Gurney, An introduction to neural networks. CRC press, 2003.
[57] P. D. McNelis, Neural networks in finance: gaining predictive edge in the market. Elsevier Acad. Press, 2005.
[58] D. T. Larose, Discovering knowledge in data: an introduction to data mining. John Wiley & Sons, 2005.
[59] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. John Wiley & Sons, 2012.
[60] A. G. Barto, “Reinforcement learning: An introduction,” 1998.
[61] S. F. Railsback and V. Grimm, Agent-based and individual-based modeling: a practical introduction. Princeton University Press, 2011.
[62] F. Kl¨ugl, “A validation methodology for agent-based simulations,” in Proceedings of the 2008 ACM symposium on Applied computing. ACM, 2008, pp. 39–43.
[63] S.-H. Chen, C.-L. Chang, and Y.-R. Du, “Agent-based economic models and econometrics,” The Knowledge Engineering Review, vol. 27, no. 02, pp. 187–219, 2012.
[64] R. Cont, “Empirical properties of asset returns: stylized facts and statistical issues,” 2001.
[65] R. S. Tsay, Analysis of financial time series. John Wiley & Sons, 2005, vol. 543.
[66] R. J. Shiller, “Market volatility,” 1992.
[67] T. C. Schelling, “Models of segregation,” The American Economic Review, pp. 488–493, 1969.
[68] T. C. Schelling, Micromotives and macrobehavior. WW Norton & Company, 2006.
[69] J. A. Frankel and K. A. Froot, “Explaining the demand for dollars: International rates of return and the expectations of chartists and fundamentalists,” Department of Economics, UCB, 1986.
[70] G.-r. Kim and H. M. Markowitz, “Investment rules, margin, and market volatility,” The Journal of Portfolio Management, vol. 16, no. 1, pp. 45–52, 1989.
[71] J. Arifovic, “The behavior of the exchange rate in the genetic algorithm and experimental economies,” Journal of Political Economy, pp. 510–541, 1996.
[72] M. Lettau, “Explaining the facts with adaptive agents: The case of mutual fund flows,” Journal of Economic Dynamics and Control, vol. 21, no. 7, pp. 1117–1147, 1997.
[73] B. R. Routledge, “Genetic algorithm learning to choose and use information,” Macroeconomic dynamics, vol. 5, no. 02, pp. 303–325, 2001.
[74] R. Palmer, W. Brian Arthur, J. H. Holland, B. LeBaron, and P. Tayler, “Artificial economic life: a simple model of a stockmarket,” Physica D: Nonlinear Phenomena, vol. 75, no. 1, pp. 264–274, 1994.
[75] S. Joshi, J. Parker, and M. A. Bedau, “Technical trading creates a prisoners dilemma: Results from an agent-based model,” Computational Finance, vol. 99, pp. 465–479, 1999.
[76] N. S. Tay and S. C. Linn, “Fuzzy inductive reasoning, expectation formation and the behavior of security prices,” Journal of Economic Dynamics and Control, vol. 25, no. 3, pp. 321–361, 2001.
[77] P. Bak, M. Paczuski, and M. Shubik, “Price variations in a stock market with many agents,” Physica A: Statistical Mechanics and its Applications, vol. 246, no. 3, pp. 430–453, 1997.
[78] N. T. Chan, B. LeBaron, A. W. Lo, T. Poggio, A. W. L. Yy, and T. P. Zz, “Agent-based models of financial markets: A comparison with experimental markets,” 1999.
[79] B. LeBaron, “Evolution and time horizons in an agent-based stock market,” Macroeconomic Dynamics, vol. 5, no. 02, pp. 225–254, 2001.
[80] M. Raberto, S. Cincotti, S. M. Focardi, and M. Marchesi, “Agent-based simulation of a financial market,” Physica A: Statistical Mechanics and its Applications, vol. 299, no. 1, pp. 319–327, 2001.
[81] J. D. Farmer and S. Joshi, “The price dynamics of common trading strategies,” Journal of Economic Behavior & Organization, vol. 49, no. 2, pp. 149–171, 2002.
[82] G. Iori, “A microsimulation of traders activity in the stock market: the role of heterogeneity, agents interactions and trade frictions,” Journal of Economic Behavior & Organization, vol. 49, no. 2, pp. 269–285, 2002.
[83] L. Neuberg and K. Bertels, “Heterogeneous trading agents,” Complexity, vol. 8, no. 5, pp. 28–35, 2003.
[84] H. Takahashi and T. Terano, “Agent-based approach to investors’ behavior and asset price fluctuation in financial markets,” Journal of artificial societies and social simulation, vol. 6, no. 3, 2003.
[85] T. Kaizoji, “Speculative bubbles and fat-tail phenomena in a heterogeneous agent model,” in International Symposia in Economic Theory and Econometrics, vol. 14. Emerald Group Publishing Limited, 2004, pp. 259–275.
[86] S.-H. Chen and C.-C. Liao, “Agent-based computational modeling of the stock price–volume relation,” Information Sciences, vol. 170, no. 1, pp. 75–100, 2005.
[87] S. Cincotti, L. Ponta, and M. Raberto, “A multi-assets artificial stock market with zero-intelligence traders,” WEHIA 2005 (13-15 June 2005), 2005.
[88] J. Derveeuw, “Market dynamics and agents behaviors: a computational approach,” in Artificial Economics. Springer, 2006, pp. 15–26.
[89] T. Shimokawa, K. Suzuki, and T. Misawa, “An agent-based approach to financial stylized facts,” Physica A: Statistical Mechanics and its Applications, vol. 379, no. 1, pp. 207–225, 2007.
[90] S.-H. Chen and Y.-C. Huang, “Risk preference, forecasting accuracy and survival dynamics: Simulations based on a multi-asset agent-based artificial stock market,” Journal of Economic Behavior & Organization, vol. 67, no. 3, pp. 702–717, 2008.
[91] S. Martinez-Jaramillo and E. P. Tsang, “An heterogeneous, endogenous and coevolutionary gp-based financial market,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 1, p. 33, 2009.
[92] B. LeBaron, “A real minsky moment in an artificial stock market,” 2012.
[93] P. Kumar, R. Rupika, S. Vennilla, K. Abinaya, and V. Mohandas, “Implementing an agent based artificial stock market model in jade-an illustration.” International Journal of Engineering & Technology (0975-4024), vol. 5, no. 3, 2013.
[94] Y.-F. Liu, C. Xu, W. Zhang, and J. V. Andersen, “Impact of information cost and switching of trading strategies in an artificial stock market,” arXiv preprint arXiv:1311.4274, 2013.
[95] D. Hirshleifer and S. Hong Teoh, “Herd behaviour and cascading in capital markets: A review and synthesis,” European Financial Management, vol. 9, no. 1, pp. 25–66, 2003.
[96] J. Ke and Y. Chen, “Modeling and simulation of the artificial stock market trading system.” Applied Mathematics & Information Sciences, vol. 7, no. 4, 2013.
[97] E. de Jong, W. F. Verschoor, and R. C. Zwinkels, “Heterogeneity of agents and exchange rate dynamics: Evidence from the ems,” Journal of International Money and Finance, vol. 29, no. 8, pp. 1652–1669, 2010.
[98] H. P. Boswijk, C. H. Hommes, and S. Manzan, “Behavioral heterogeneity in stock prices,” Journal of Economic Dynamics and Control, vol. 31, no. 6, pp. 1938–1970, 2007.
[99] H. Amilon, “Estimation of an adaptive stock market model with heterogeneous agents,” Journal of Empirical Finance, vol. 15, no. 2, pp. 342–362, 2008.
[100] S. Chen, Y. Huang, and J. Wang, “Bounded rationality and the elasticity puzzle: An analysis of agent-based computational consumption capital asset pricing models,” Routledge, New York, 2009.
[101] B. LeBaron, “Heterogeneous gain learning and long swings in asset prices,” 2010.
[102] S. Sivanandam and S. Deepa, “Introduction to genetic algorithms. 2008.”
[103] J. R. Koza, Genetic programming: on the programming of computers by means of natural selection. MIT press, 1992, vol. 1.
[104] T. Lux, “The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions,” Journal of Economic Behavior & Organization, vol. 33, no. 2, pp. 143–165, 1998.
[105] B. LeBaron and R. Yamamoto, “Long-memory in an order-driven market,” Physica A: Statistical Mechanics and its Applications, vol. 383, no. 1, pp. 85–89, 2007.
[106] D. Challet and T. Galla, “Price return autocorrelation and predictability in agent-based models of financial markets,” Quantitative Finance, vol. 5, no. 6, pp. 569–576, 2005.
[107] F. F. Ferreira, V. M. de Oliveira, A. F. Crepaldi, and P. R. Campos, “Agent-based model with heterogeneous fundamental prices,” Physica A: Statistical Mechanics and its Applications, vol. 357, no. 3, pp. 534–542, 2005.
[108] V. Manahov and R. Hudson, “Herd behaviour experimental testing in laboratory artificial stock market settings. behavioural foundations of stylised facts of financial returns,” Physica A: Statistical Mechanics and its Applications, vol. 392, no. 19, pp. 4351–4372, 2013.
[109] F. Ghoulmie, R. Cont, and J.-P. Nadal, “Heterogeneity and feedback in an agent-based market model,” Journal of Physics: condensed matter, vol. 17, no. 14, p. S1259, 2005.
[110] H. Li and J. Rosser, “Market dynamics and stock price volatility,” The European Physical Journal B-Condensed Matter and Complex Systems, vol. 39, no. 3, pp. 409–413, 2004.