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Zero intelligence in economics and finance

Published online by Cambridge University Press:  26 April 2012

Dan Ladley
Dan Ladley*
Affiliation:
Department of Economics, University of Leicester, Leicester LE1 7RH, United Kingdom; e-mail: d.ladley@leicester.ac.uk
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Abstract

This paper reviews the Zero Intelligence (ZI) methodology for investigating markets. This approach models individual traders, operating within a market mechanism, who behave without strategy, in order to determine the impact of the market mechanism and consequently the effect of trader behaviour. The paper considers the major contributions and models within this area from both the economics and finance communities before examining the strengths and weaknesses of this methodology.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 273 - 286
Copyright
Copyright © Cambridge University Press 2012

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References

Arthur, W. B., Holland, J. H., LeBaron, B., Palmer, R., Tayler, P. 1997. Asset pricing under endogenous expectations in an artificial stock market. In The Economy as a Complex Evolving System II, Arthur, W. B., Durlauf, S. N. & Lane, D. A. (eds). XXVII of Santa Fe Institute Studies in the Sciences of Complexity. Addison-Wesley, 15–44.Google Scholar
Bagnall, A. J., Toft, I. E. 2004. Zero Intelligence Plus and Gjerstad–Dickhaut agents for sealed bid auctions. In Workshop on Trading Agent Design and Analysis, part of International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2004), Columbia University, New York, 59–64.Google Scholar
Bagnall, A. J., Toft, I. E. 2005. Autonomous adaptive agents for single seller sealed bid auctions. Journal of Autonomous Agents and Multi-Agent Systems 12(3), May 2006.Google Scholar
Bak, P., Paczuski, M., Shubik, M. 1997. Price variations in a stock market with many agents. Physica A 246, 430–453.CrossRefGoogle Scholar
Becker, G. S. 1962. Irrational behaviour and economic theory. Journal of Political Economy 70, 1–13.CrossRefGoogle Scholar
Biais, B., Hillion, P., Spatt, C. 1995. An empirical analysis of the limit order book and the order flow in the Paris Bourse. Journal of Finance 50, 1655–1689.CrossRefGoogle Scholar
Bollerslev, T., Domowitz, I., Wang, J. 1997. Order flow and the bid–ask spread: an empirical probability model of screen-based trading. Journal of Economic Dynamics and Control 21(8–9), 1471–1491.CrossRefGoogle Scholar
Bollerslev, T., Domowitz, I. 1993. Some effects of restricting the electronic order book in an automated trade execution system. In The Double Auction Market: Institutions, Theories, and Evidence, Friedman, D. & Rust, J. (eds). Santa Fe Institute Studies in the Sciences of Complexity. Perseus Publishing, 221–252.Google Scholar
Bosch-Domenech, A., Sunder, S. 2000. Tracking the invisible hand: convergence of double auctions to competitive equilibrium. Computational Economics 16(3), 257–284.CrossRefGoogle Scholar
Bottazzi, G., Dosi, G., Rebesco, I. 2005. Institutional architectures and behavioral ecologies in the dynamics of financial markets. Journal of Mathematical Economics 41(1–2), 197–228.CrossRefGoogle Scholar
Bouchaud, J.-P., Mezard, M., Potters, M. 2002. Statistical properties of stock order books: empirical results and models. Quantitative Finance 2, 251–256.CrossRefGoogle Scholar
Cason, T. N., Friedman, D. 1996. Price formation in double auction markets. Journal of Economic Dynamics and Control 20(8), 1307–1337.CrossRefGoogle Scholar
Challet, D., Stinchcombe, R. 2001. Analyzing and modeling 1+1d markets. Physica A 300, 285–299.CrossRefGoogle Scholar
Chiarella, C., Iori, G. 2002. A simulation analysis of the microstructure of double auction markets. Quantitative Finance 2, 346–353.CrossRefGoogle Scholar
Cliff, D., Bruten, J. 1997. Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical Report HPL-97-91, HP Labs.Google Scholar
Cliff, D., Bruten, J. 1999. Animat market-trading interactions as collective social adaptive behavior. Adaptive Behavior 7(3), 385–414.CrossRefGoogle Scholar
Cliff, D. 2003. Explorations in evolutionary design of online auction market mechanism. Journal of Electronic Commerce Research and Application 2(2), 162–175.CrossRefGoogle Scholar
Cliff, D. 2006. Zip60: an enhanced variant of the zip trading algorithm. In cec-eee. Proceedings of the 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06). IEEE Computer Society, 15.Google Scholar
Cohen, K. J., Conroy, R. M., Maier, S. F. 1985. Order flow and the quality of the market. In Market Making and the Changing Structure of the Securities Industry, Amihud, Y., Ho, T. & Schwartz, R. (eds). Lexington Books, 93–110.Google Scholar
Cont, R. 2001. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance 1, 223–236.CrossRefGoogle Scholar
Crockett, S., Spear, S., Sunder, S. 2008. Learning competitive equilibrium. Journal of Mathematical Economics 7-8, 651–671.CrossRefGoogle Scholar
Crockett, S. 2008. Learning competitive equilibrium in laboratoy exchange economies. Economic Theory 34(1), 157–180.CrossRefGoogle Scholar
Daniels, M. G., Farmer, J. D., Iori, G., Smith, E. 2003. Quantitative model of price diffusion and market friction based on trading as a mechanistic random process. Physical Review Letters 90(10), 108102.CrossRefGoogle ScholarPubMed
DeLong, J. B., Shleifer, A., Summers, L. H., Waldmann, R. J. 1989. The size and incidence of losses from noise trading. Journal of Finance 44(3), 681–696.CrossRefGoogle Scholar
DeLong, J. B., Shleifer, A., Summers, L. H., Waldmann, R. J. 1990a. Noise trader risk in financial markets. Journal of Political Economy 98(4), 703–738.CrossRefGoogle Scholar
DeLong, J. B., Shleifer, A., Summers, L. H., Waldmann, R. J. 1990b. Positive-feedback investment strategies and destabilizing rational speculation. Journal of Finance 45(2), 374–397.Google Scholar
DeLong, J. B., Shleifer, A., Summers, L. H., Waldmann, R. J. 1991. The survival of noise traders in financial markets. Journal of Business 64(1), 1–20.Google Scholar
Dominguez, K. M. 1986. Are foreign exchange forecasts rational? Economic Letters 21, 277–281.CrossRefGoogle Scholar
Domowitz, I., Wang, J. 1994. Auctions as algorithms: computerized trade execution and price discovery. Journal of Economic Dynamics and Control 18(1), 29–60.CrossRefGoogle Scholar
Duffy, J., Unver, M. 2006. Asset price bubbles and crashes with near-Zero-Intelligence traders. Economic Theory 27, 537–563.CrossRefGoogle Scholar
Eliezer, D., Kogan, I. I. 1998. Scaling laws for the market microstructure of the interdealer broker markets. ArXiv Condensed Matter e-prints.Google Scholar
Farmer, J. D., Patelli, P., Zovko, I. I. 2005. The predictive power of Zero Intelligence in financial markets. Proceedings of the National Academy of Science 102, 2254–2259.CrossRefGoogle ScholarPubMed
Gode, D. K., Spear, S., Sunder, S. 2004. Convergence of Double Auctions to Pareto Optimal Allocations in the Edgeworth Box. Yale School of Management Working Papers, Yale, ICF.Google Scholar
Gode, D.K., Sunder, S. 1993a. Allocative efficiency of markets with Zero-Intelligence traders: market as a partial substitute for individual rationality. Journal of Political Economy 101, 119–137.CrossRefGoogle Scholar
Gode, D. K., Sunder, S. 1993b. Lower bounds for efficiency of surplus extraction in double auctions. In The Double Auction Market: Institutions, Theories, and Evidence, Friedman, D. & Rust, J. (eds). Santa Fe Institute Studies in the Sciences of Complexity. Perseus Publishing, 199–219.Google Scholar
Gode, D. K., Sunder, S. 1994. Human and artificially intelligent traders in a double auction market: experimental evidence. In Computational Organization Theory, Carley, K. & Prietula, M. (eds). Lawrence Erlbaum Associates, 241–262.Google Scholar
Gode, D. K., Sunder, S. 1997. What makes markets allocationally efficient? Quarterly Journal of Economics 112, 603–630.CrossRefGoogle Scholar
Gode, D. K., Sunder, S. 2004. Double auction dynamics: structural effects of non-binding price controls. Journal of Economic Dynamics and Control 28(9), 1707–1731.CrossRefGoogle Scholar
Isaac, R., Plott, C. 1981. Price controls and the behavior of auction markets: an experimental evaluation. American Economic Review 71(3), 448–459.Google Scholar
Jamal, K., Sunder, S. 1996. Bayesian equilibrium in double auctions populated by biased heuristic traders. Journal of Economic Behavior & Organization 31(2), 273–291.CrossRefGoogle Scholar
Jamal, K., Sunder, S. 2001. Why do biased heuristics approximate Bayes rule in double auctions? Journal of Economic Behavior & Organization 46(4), 431–435.CrossRefGoogle Scholar
Ladley, D., Bullock, S. 2006. Who to listen to: exploiting information quality in a zip-agent market. In Agent-Mediated Electronic Commerce, La Poutre, H., Sadeh, N. M. & Janson, S. (eds). Designing Trading Agents and Mechanisms. Springer-Verlag, 200–211.Google Scholar
Ladley, D., Schenk-Hoppé, K. R. 2009. Do stylised facts of order book markets need strategic behaviour? Journal of Economic Dynamics and Control 33(4), 817–831.CrossRefGoogle Scholar
Mandelbrot, B. 1997. Fractals and Scaling in Finance: Discontinuity, Concentration and Risk. Springer.CrossRefGoogle Scholar
Maslov, S. 2000. Simple model of a limit order-driven market. Physica A 278, 571–578.CrossRefGoogle Scholar
Mendelson, H. 1982. Market behavior in a clearing house. Econometrica 50(6), 1505–1524.CrossRefGoogle Scholar
Oberlechner, T. 2001. Importance of technical and fundamental analysis in the European foreign exchange market. International Journal of Finance & Economics 6(1), 81–93.CrossRefGoogle Scholar
Parlour, C. A. 1998. Price dynamics in limit order markets. Review of Financial Studies 11(4), 789–816.CrossRefGoogle Scholar
Slanina, F. c. v. 2001. Mean-field approximation for a limit order driven market model. Physical Review E 64, 056136.CrossRefGoogle ScholarPubMed
Smith, E., Farmer, J. D., Gillemot, L., Krishnamurthy, S. 2003. Statistical throry of the continuous double auction. Quantitative Finance 3, 481–514.CrossRefGoogle Scholar
Smith, V. L. 1962. An experimental study of competitive market behavior. Journal of Political Economy 70, 111–137.CrossRefGoogle Scholar
Smith, V., Williams, A. 1981. On nonbinding price controls in a competitive market. American Economic Review 71(3), 467–474.Google Scholar
Sunder, S. 2004. Markets as artifacts: aggregate efficiency from Zero-Intelligence traders. In Models of a Man: Essays in Memory of Herbet Simon, Augier, M. E. & March, J. G. (eds). MIT Press, 501–519.CrossRefGoogle Scholar
Sunder, S. 2006. Determinants of economic interaction: behavior or structure. Journal of Economic Interaction and Coordination 1(1), 21–32.CrossRefGoogle Scholar
Tang, L.-H., Tian, G.-S. 1999. Reaction-diffusion-branching models of stock price fluctuations. Physica A 264(3), 543–550.CrossRefGoogle Scholar
Tesfatsion, L., Judd, K. L. (eds) 2006. Handbook of Computational Economics, 2. Elsevier.Google Scholar
Tesfatsion, L. 2006. Agent-Based Computational Economics: a constructive approach to economic theory. In Handbook of Computational Economics, 2. Tesfatsion, L. & Judd, K. (eds). Elsevier, Ch. 16, 831–880.Google Scholar
Widrow, B., Hoff, M. E. 1960. Adaptive switching circuits. IRE WESCON Convention Record 4, 96–104.Google Scholar