On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming
Agent-based computational economic modeling requires demanding work on computer programming. Publications of agent-based computational economic modeling usually do not provide readers with adequate information to permit replication of the experiments reported in the papers. Such failure makes the findings from the agent-based simulations hard to verify and defies technical improvement. To facilitate the growth of this research area, it is necessary for authors to make their source codes available in a public domain. This paper is a documentation accompanying the software AIE-ASM, which is available on the website. The software is designed to simulate the agent-based artificial stock market based on a standard asset pricing model. Genetic programming, as part of the software, is used to drive the learning dynamics of traders. An example based on the version of single-population genetic programming is demonstrated in this paper.
KeywordsGenetic Programming Stock Price Business School Price Discovery Tournament Selection
Unable to display preview. Download preview PDF.
- 2.Arthur B. (1992) On Learning and Adaptation in the Economy. SFI Working Paper, 92–07-038Google Scholar
- 3.Arthur W. B., Holland J., LeBaron B., Palmer R. and Tayler P. (1997) 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, 15–44Google Scholar
- 4.Chen S. -H., Liao C. -C. (2000) Price Discovery in Agent-Based Computational Modeling of Artificial Stock Markets. In: Proceedings of the Second Asia-Pacific Conference on Genetic Algorithms and Applications. Global Link Publishing Company, Hong Kong, pp.380–387Google Scholar
- 5.Chen S. -H., Yeh C. -H. (2000a) On the Role of Intensive Search in stock Markets: Simulations Based on Agent-Based Computational Modeling of Artificial Stock Markets. In: Proceedings of the Second Asia-Pacific Conference on Genetic Algorithms and Applications. Global Link Publishing Company, Hong Kong, 397–402Google Scholar
- 6.Chen S. -H., Yeh C. -H. (2000b) On the Consequence of “Following the Herd”: Evidence from the Artificial Stock Market. In: Arabnia H. R. (Ed.) Proceedings of the International Conference on Artificial Intelligence, Vol. II, CSREA Press, 388–394Google Scholar
- 8.Chen S. -H., Yeh C. -H. (2001b) On the Emergent Properties of Artificial Stock Markets: The Efficient Market Hypothesis and the Rational Expectations Hypothesis. Forthcoming in Journal of Economic Behavior and Organization. Google Scholar
- 9.Chen S. -H., Yeh C. -H., Liao C. -C. (2000), Testing for Granger Causality in the Stock-Price Volume Relation: A Perspective from the Agent-Based Model of Stock Markets. In: Wang P. (Ed.) Proceedings of the Fifth Joint Conference on Information Sciences, Vol. II, 950–956Google Scholar
- 10.Harrald P. (1998) Economics and Evolution. Panel Paper Given at the Seventh International Conference on Evolutionary Programming, March 25–27, San Diego, U.S.A.Google Scholar
- 11.Holland J. H., Miller J. H. (1991) Artificial Adaptive Agents in Economic Theory. American Economic Review: Papers and Proceedings 81(2), 365–370Google Scholar
- 14.Palmer R. G., Arthur W. B., Holland J. H., LeBaron B., Tayler P. (1994) Artificial Economic Life: A Simple Model of a Stockmarket. Physica D 75, 264–274Google Scholar
- 16.Sargent T. J. (1993) Bounded Rationality in Macroeconomics, Oxford.Google Scholar