Abstract
This paper studies similarities and differences between models based on Monte-Carlo method by focusing on so-called “stylized facts.” In this study, we propose a model based on evolutionary algorithm and take the other model based on statistical physics. For this purpose, first we present a genetic learning model of investor sentiment and then several ordinary time series analyses are conducted after generating sample paths. Finally, the price properties are compared to those in the Ising spin model. Our results show that both the Monte-Carlo simulations seem to lead to similar dynamics reported in real markets in that the agents are boundedly rational or have some biases towards the market. However, other time series properties are apparently different since the algorithm of price formation is different.
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
Arifovic J (2000) Evolutionary algorithms in macroeconomic models. Macroecon. Dyn. 4:373–414
Arifovic J, Gençay R (2000) Statistical properties of genetic learning in a model of exchange rate. J. Econ. Dyn. Control 24:981–1005
Barberis N, Shleifer A, Vishny R (1998) A model of investor sentiment. J. Finan. Econ. 49:307–343
Barberis N, Huang M, Santos T (2001) Prospect theory and asset prices. Q. J. E. 116:1–53
Behera L, Schweitzer F (2003) On spatial consensus formation: is the Sznajd model different from a voter model? Int. J. Mod. Phys. C 14:1331–1354
Chen S-H (2002) Evolutionary computation in economics and finance. Physica-Verlag, Heidelberg
Duffy J (2006) Agent-based models and human subject experiments. in Tesfatsion, L., Judd, K.L. (eds.) Handbook of computational economics: agent-based computational economics volume 2, North-Holland, Netherlands, 949–1012
Durlauf S N (2005) Complexity and empirical economics. Econ. J. 115:F225–F243
Hirabayashi T, Takayasu H, Miura H, Hamada K (1993) The behavior of a threshold model of market price in stock exchange. Fractals 1:29–40
Hommes C H (2001) Financial markets as nonlinear adaptive evolutionary systems. Quant. Finan. 1:149–167
Hommes C H (2006) Heterogeneous agent models in economics and finance. in Tesfatsion, L., Judd, K.L. (eds.) Handbook of computational economics: agent-based computational economics volume 2, North-Holland, Netherlands, 1109–1186
Izumi K, Ueda K (2001) Phase transition in a foreign exchange market: analysis based on an artificial market approach. IEEE Trans. Evol. Comput. 5:456–470
Izumi K, Nakamura S, Ueda K (2005) Development of an artificial market model based on a field study. Info. Sci. 170:35–63
LeBaron B, Arthur W B, Palmer R (1999) Time series properties of an artificial stock market. J. Econ. Dyn. Control 23:1487–1516
LeBaron B (2000) Agent-based computational finance: suggested readings and early research. J. Econ. Dyn. Control 24:679–702
LeBaron B (2001) A builder’s guide to agent-based financial markets. Quant. Finan. 1:254–261
LeBaron B (2006) Agent-based computational finance. in Tesfatsion, L., Judd, K.L. (eds.) Handbook of computational economics: agent-based computational economics volume 2, North-Holland, Netherlands, 1187–1234
Levy M, Levy H, Solomon S: Microscopic simulations of financial markets: from investor behaviour to market phenomena. Academic Press, San Diego.
Lux T (1998) The socio-economic dynamics of speculative markets: interacting agents, chaos, and fat tails of return distributions. J. Econ. Behav. Org. 33:143–165
Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397:498–500
Lux T, Marchesi M (2000) Volatility clustering in financial markets: a microsimulation of interacting agents. Int. J. Th. Appl. Finan. 3:675–702
Riechmann T (2001) Genetic algorithm learning and evolutionary games. J. Econ. Dyn. Control 25:1019–1037
Sznajd-Weron K, Weron R (2002) A simple model of price formation. Int. J. Mod. Phys. C 13:115–123
Tesfatsion L, Judd K L (eds.) (2006): Handbook of computational economics: agent-based computational economics volume 2. North-Holland, Netherland
Ueda K, Uchida Y, Izumi K, Ito Y (2004) How do expert dealers make profits and reduce the risk of loss in a foreign exchange market? Proc. of the 26th annual conf. of the Cognitive Science Society, Chicago, USA, 1357–1362
Yamada T, Terano T (2006) Price formations in genetic learning model of investor sentiment. Proc. of 9th Joint Conf. on Info. Sci., 285–288
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer
About this paper
Cite this paper
Yamada, T., Terano, T. (2009). From the simplest price formation models to paradigm of agent-based computational finance: a first step. In: Terano, T., Kita, H., Takahashi, S., Deguchi, H. (eds) Agent-Based Approaches in Economic and Social Complex Systems V. Agent-Based Social Systems, vol 6. Springer, Tokyo. https://doi.org/10.1007/978-4-431-87435-5_18
Download citation
DOI: https://doi.org/10.1007/978-4-431-87435-5_18
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-87433-1
Online ISBN: 978-4-431-87435-5
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)