Learning to trade in an unbalanced market
AbstractWe study the evolution of trading strategies in double auctions as the size of the market gets larger. When the number of buyers and sellers is balanced, Fano et al. (2011) show that the choice of the order-clearing rule (simultaneous or asynchronous) steers the emergence of fundamentally different strategic behavior. We extend their work to unbalanced markets, confirming their main result as well as that allocative inefficiency tends to zero. On the other hand, we discover that convergence to the competitive outcome takes place only when the market is large and that the long side of the market is more effective at improving its disadvantaged terms of trade under asynchronous order-clearing.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Management, Università Ca' Foscari Venezia in its series Working Papers with number 2.
Length: 23 pages
Date of creation: Apr 2011
Date of revision:
Publication status: Published in S. Osinga, G.J. Hofstede, and T. Verwaart (eds.), Emergent Results of Artificial Economics, Springer, 2011, 65-76
Trading protocols; Market design; Allocative efficiency; Genetic Programming;
Find related papers by JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-14 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Anufriev, M. & Arifovic, J. & Ledyard, D. & Panchenko, V., 2010.
"Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information,"
CeNDEF Working Papers
10-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
- Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013.
"Convergence of outcomes and evolution of strategic behavior in double auctions,"
Journal of Evolutionary Economics,
Springer, vol. 23(3), pages 513-538, July.
- Shira Fano & Marco Li Calzi & Paolo Pellizzari, 2010. "Convergence of outcomes and evolution of strategic behavior in double auctions," Working Papers 196, Department of Applied Mathematics, Università Ca' Foscari Venezia.
- Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading without Resampling," Working Papers 164, Department of Applied Mathematics, Università Ca' Foscari Venezia.
- Gode, Dhananjay K & Sunder, Shyam, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, MIT Press, vol. 112(2), pages 603-30, May.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco LiCalzi).
If references are entirely missing, you can add them using this form.