Abstract
We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by specialists and the presence of informed traders. We optimize these trading rules using a population-based incremental learning algorithm seeking to maximize the trading volume. Our results suggest markets should choose a large tick size and ensure only a small fraction of traders are informed about the order book. The effect of trading rules regarding the determination of prices, priority rules, and specialist intervention, we find to have an ambiguous effect on the outcome.
Similar content being viewed by others
References
Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical report cmu-cs-94-163, School of Computer Science, Carnegie Mellon University, Pittsburgh
Baluja S (1995) An empirical comparison of seven iterative and evolutionary function optimization heuristics. Technical reports, Carnegie Mellon University. Available via. anonymous ftp at: reports.adm.cs.cmu.edu.CMU-CS-95-193
Challet D, Stinchcombe R (2001) Analyzing and modeling /1+1d markets. Physica A 300: 285–299
Cliff D (2003) Explorations in evolutionary design of online auction market mechanisms. Electronic Commerce Res Appl 2: 162–175
Cliff D, Bruten J (2001) Minimal intelligence agents for bargaining behaviors in market-based environments. HP Lab Report HPL-97-91, HP
Domowitz I (1993) A taxonomy of automated trade execution systems. J Int Money Finance 12: 607–631
Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101: 119–137
Goldstein MA, Kavajecz KA (2000) Eighths, sixteenths, and market depth: Changes in tick size and liquidity provision on the nyse. J Financ Econ 56: 125–149
Hakansson NH, Beja A, Kale J (1985) On the feasibility of automated market making by a programmed specialist. J Finance 40: 1–20
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Ho TY, Stoll HR (1980) On dealer markets under competition. J Finance 35: 259–267
Ho TY, Stoll HR (1983) The dynamics of dealer markets under competition. J Finance 38: 1053–1074
Jones CM, Lipson ML (2001) Sixteenths: direct evidence on institutional execution costs. J Financ Econ 59(2): 253–278
Koedijk CG, Flood MD, Huisman R, Mahieu RJ (1999) Quote disclosure and price discovery in multiple dealer financial markets. Rev Financ Stud 12: 37–59
Krause A (2006) Fat tails and multi-scaling in a simple model of limit order markets. Physica A 368: 183–190
Ladley D, Schenk-Hoppe KR (2009) Do stylised facts of order book markets need strategic behaviour?. J Econ Dyn Control 33: 817–831
Li X, Krause A (2009) IDEAL 2009, Lecture notes in compter science 5788, chapter A comparison of market structure with Near-Zero-Interlligence traders. Springer, pp 703–710
Madhavan A (2000) Market microstructure: a survey. J Financ Mark 3: 205–258
Maslov S (2000) Simple model of a limit order-driven market. Physica A 278: 571–578
O’Hara M (1995) Market microstructure theory. Blackwell, Oxford
Othman A (2008) Zero-intelligence agents in prediction markets. In: Proceedings of the seventh international conference on autonomous agents and multiagent systems (AAMAS 2008). IFAAMAS, pp 879–886
Paczuski M, Shubik M, Bak P (1997) Price variations in a stock market with many agents. Physica A 246: 430–453
Pagano M, Rell A (1996) Transparency and liquidity: a comparison of auction and dealer markets with informed trading. J Finance
Schwartz RA (1988) Equity markets: structure, trading, and performance. Harper and Row, New York
Stoll HR (1978) The supply of dealer services in securities markets. J Finance 33: 1133–1151
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, X., Krause, A. Determining the optimal market structure using near-zero intelligence traders. J Econ Interact Coord 5, 155–167 (2010). https://doi.org/10.1007/s11403-010-0069-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11403-010-0069-3