Bounded Rational Heterogeneous Agents in Artificial Stock Markets: Literature Review and Research Direction
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.
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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.
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bottom up. Brookings Institution Press, 1996.
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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.
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[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.
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zero-intelligence traders: Market as a partial substitute for individual
rationality,” Journal of political economy, vol. 101, no. 1, p. 119, 1993.
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[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.
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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.
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to genetic programming,” 2008.
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@article{"International Journal of Business, Human and Social Sciences:70634", author = "Talal Alsulaiman and Khaldoun Khashanah", title = "Bounded Rational Heterogeneous Agents in Artificial Stock Markets: Literature Review and Research Direction", 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.", volume = "9", number = "6", pages = "2108-20", }
