KEY POINTS FOR REALISTIC AGENT-BASED FINANCIAL MARKET SIMULATIONS
Iryna Veryzhenko, Philippe Mathieu, Olivier Brandouy
2011
Abstract
The purpose of this paper is to define software engineering abstractions that provide a generic framework for stock market simulations. We demonstrate a series of key points and principles that has governed the development of an Agent-Based financial market in the form of an API. The simulator architecture is presented. During artificial market construction we have faced the whole variety of agent-based modeling issues and solved them : local interaction, distributed knowledge and resources, heterogeneous environments, agents autonomy, artificial intelligence, speech acts, discrete scheduling and simulation. Our study demonstrates that the choices made for agent-based modeling in this context deeply impact the resulting market dynamics and proposes a series of advances regarding the main limits the existing platforms actually meet.
References
- Arthur, B. (1994). Inductive reasoning and bounded rationality : the el-farol problem. American Economic Review, 84:406-417.
- Bouchaud, J.-P. and Potter, M. (2000). Theory of Financial Risk. Cambridge University Press.
- Brandouy, O. and Mathieu, P. (2007). A conceptual framework for the evaluation of agent-base trading and technical analysis. Artificial Markets Modeling. Methods and Applications. Lecture Notes in Economics and Mathematical Systems, 599:63-79.
- Brandouy, O., Mathieu, P., and Veryzhenko, I. (2009). Expost optimal strategy for the trading of a single financial asset. SSRN eLibrary.
- Cesa-Bianchi, N. and Lugosi, G. (2001). Worst-case bounds for the logarithmic loss of predictors. Machine Learning, 43:247264.
- Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1:223-236.
- Ferber, J. and Muller, J.-P. (1996). Influences and reaction: a model of situated multiagent systems. Second International Conference on Multiagent Systems ICMAS96, pages 72-79.
- Gode, D. K. and Sunder, S. (1993). Allocative efficiency of market with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101(1):119-137.
- Gouaich, A., Michel, F., and Guiraud, Y. (2005). Mic : A deployment environment for autonomous agents. E4MAS 2004, LNAI 3374, Springer-Verlag, pages 109-126.
- Inchiosa, M. E. and Parker, M. T. (2002). Overcoming design and development challenges in agent-based modeling using ascape. In Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling, number 3, pages 7304-7308, 317 Paseo de Peralta, Santa Fe.
- Jacobs, B. I., Levy, K. N., and Markowitz, H. M. (2004). Financial market simulation. The Journal of Portfolio Management, 30th Anniversary Issue:142-151.
- Kirman, A. (1993). Ants, rationality, and recruitment. Quarterly Journal of Economics, 108:137-156.
- Le Baron, B. (2002). Building the santa fe artificial stock market. Working Paper, Brandeis University.
- Lux, T. and Marchsi, M. (1999). Scaling and criticality in a stochastich multi-agent model of a financial market. Nature, 397:498-500.
- Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77-91.
- Michel, F. (2007). The irm4s model: The influence/reaction principle for multi-agent based simulation. AAMAS'07. Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 908- 910.
- Mitchell, T. M., editor (1997). Machine Learning. WCB/McGraw-Hill, New York, NY.
- Raberto, M., Cincotti, S., Focardi, S., and Marchesi, M. (2003). Traders' long-run wealth in an artificial financial market. Computational Economics, 22:255-272.
- Ricordel, P.-M. and Demazeau, Y. (2001). Volcano, a vowels-oriented multi-agent platform. In CEEMAS, pages 253-262.
- Tkatch, I. and Alam, Z. S. (2009). Strategic order splitting in automated markets. SSRN eLibrary.
- Veryzhenko, I., Brandouy, O., and Mathieu, P. (2010). Agent's minimal intelligence calibration for realistic market dynamics. Progress in Artificial Economics Computational and Agent-Based Models. Lecture Notes in Economics and Mathematical Systems 645, pages 3-14.
- Witkam, J. (2003). http://www.altreva.com.
- Wooldridge, M. and Jennings, N. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2):115-152.
- Woolridge, M. (2002). Introduction to Multiagent Systems. Introduction to Multiagent Systems., New York, NY, USA.
- Zambonelli, F., Jennings, N. R., and Wooldridge., M. (2003). Developing multiagent systems: The gaia methodology. In ACM Transactions on Software Engineering Methodology, 12(3)(3):317-370.
Paper Citation
in Harvard Style
Veryzhenko I., Mathieu P. and Brandouy O. (2011). KEY POINTS FOR REALISTIC AGENT-BASED FINANCIAL MARKET SIMULATIONS . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8425-41-6, pages 74-83. DOI: 10.5220/0003156200740083
in Bibtex Style
@conference{icaart11,
author={Iryna Veryzhenko and Philippe Mathieu and Olivier Brandouy},
title={KEY POINTS FOR REALISTIC AGENT-BASED FINANCIAL MARKET SIMULATIONS},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2011},
pages={74-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003156200740083},
isbn={978-989-8425-41-6},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - KEY POINTS FOR REALISTIC AGENT-BASED FINANCIAL MARKET SIMULATIONS
SN - 978-989-8425-41-6
AU - Veryzhenko I.
AU - Mathieu P.
AU - Brandouy O.
PY - 2011
SP - 74
EP - 83
DO - 10.5220/0003156200740083