Abstract
This paper studies the continuous double auction from the point of view of market engineering: we tweak a resampling rule often used for this exchange protocol and search for an improved design. We assume zero intelligence trading as a lower bound for more robust behavioral rules and look at allocative efficiency, as well as three subordinate performance criteria: mean spread, cancellation rate, and traders’ protection. This latter notion measures the ability of a protocol to help traders capture their share of the competitive equilibrium profits.
We consider two families of resampling rules and obtain the following results. Full resampling is not necessary to attain high allocative efficiency, but fine-tuning the resampling rate is important. The best allocative performances are similar across the two families. However, if the market designer adds any of the other three criteria as a subordinate goal, then a resampling rule based on a price band around the best quotes is superior.
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- 1.
There are negligible differences. We consider n agents who can trade at most one unit, while they have 12 traders who can exchange several units but must trade them one by one. Our setup is simpler to describe because it associates with each trader a single unit and a one-dimensional type (his cost/valuation).
- 2.
LiCalzi and Pellizzari (2008) document a similar effect over four different trading protocols.
- 3.
We consistently apply this approach to construct the graphs for this paper: a broken line joins 21 points, each of which represents a statistic over 500 distinct simulations for a fixed value of a parameter such as π.
- 4.
We start from π=0.05 because the cancellation rate at π=0 is zero by assumption.
- 5.
When the number of intramarginal traders is odd, one of them will not trade for lack of a partner.
- 6.
In a closed book, traders learn only the clearing price after each call; in an open book, they are also told the quotes processed in that call.
References
Arifovic, J., & Ledyard, J. (2007). Call market book information and efficiency. Journal of Economic Dynamics and Control, 31, 1971–2000.
Brewer, P. J., Huang, M., Nelson, B., & Plott, C. R. (2002). On the behavioral foundations of the law of supply and demand: human convergence and robot randomness. Experimental Economics, 5, 179–208.
Cliff, D., & Bruten, J. (1997). More than zero intelligence needed for continuous double-auction trading. Hewlett Packard Laboratories Paper HPL-97-157, Bristol, England.
Crockett, S., Spear, S., & Sunder, S. (2008). Learning competitive equilibrium. Journal of Mathematical Economics, 44, 651–671.
Duffy, J. (2006). Agent-based model and human subject experiments. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (pp. 950–1011). Amsterdam: Elsevier.
Gjerstad, S., & Shachat, J. M. (2007). Individual rationality and market efficiency, wp 1204, August. IRBEMS, Krannert School, Purdue University.
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.
Gode, D. K., & Sunder, S. (1993b). Lower bounds for efficiency of surplus extraction in double auctions. In D. Friedman & J. Rust (Eds.), The double auction market (pp. 199–218). Reading: Addison–Wesley.
Gode, D. K., & Sunder, S. (1997). What makes markets allocationally efficient? Quarterly Journal of Economics, 112, 603–630.
Gode, D. K., & Sunder, S. (2004). Double auction dynamics: structural effects of non-binding price controls. Journal of Economic Dynamics and Control, 28, 1707–1731.
Gul, F., & Lundholm, R. (1995). Endogenous timing and the clustering of agents’ decisions. Journal of Political Economy, 103, 1039–1066.
Hurwicz, L., Radner, R., & Reiter, S. (1975). A stochastic decentralized resource allocation process: part I. Econometrica, 43, 187–222.
LiCalzi, M., & Pellizzari, P. (2008). Zero-intelligence trading without resampling. In K. Schredelseker & F. Hauser (Eds.), Complexity and artificial markets (pp. 3–14). Berlin: Springer.
Maslov, S. (2000). Simple model of a limit order-driven market. Physica A, 278, 571–578.
Mirowski, P. (2007). Markets come to bits: evolution, computation and markomata in economic science. Journal of Economic Behavior and Organization, 63, 209–242.
Roth, A. E. (2002). The economist as engineer: game theory, experimentation, and computation as tools for design economics. Econometrica, 70, 1341–1378.
Smith, V. L. (1982). Microeconomic systems as an experimental science. American Economic Review, 72, 923–955.
Stigler, G. J. (1964). Public regulation of the securities markets. Journal of Business, 37, 117–142.
Subrahmanian, E., & Talukdar, S. N. (2004). Engineering of markets and artifacts. Electronic Commerce Research and Applications, 3, 369–380.
Sunder, S. (2004). Markets as artifacts. In M. Augier & J. March (Eds.), Models of a man: essays in memory of Herbert A. Simon (pp. 501–520). Cambridge: MIT Press.
Zhan, W., & Friedman, D. (2007). Markups in double auction markets. Journal of Economic Dynamics and Control, 31, 2984–3005.
Zhan, W., Zhang, J., Yang, J., Wang, S., & Lai, K. K. (2002). k-ZI: A general zero-intelligence model in continuous double auction. International Journal of Information Technology and Decision Making, 1, 673–691.
Acknowledgements
This paper was written while the third author was visiting the School of Finance and Economics at the University of Technology of Sidney, whose financial assistance is gratefully acknowledged. We also received financial support from MIUR under grants 2007EENEAX and 2007TKLTSR.
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LiCalzi, M., Milone, L., Pellizzari, P. (2011). Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior. In: Dawid, H., Semmler, W. (eds) Computational Methods in Economic Dynamics. Dynamic Modeling and Econometrics in Economics and Finance, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16943-4_2
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