Learning Cancellation Strategies in a Continuous Double Auction Market
AbstractThis paper deals with two different issues. On one side, it tries to determine if the equilibrium order placement strategies analytically derived in Foucault et al. (2005) are learnable by no-maximizing agents that update their strategies on the only base of their own past experience (via genetic algorithm). Results state outcome (but not strategic) equivalence. On the other side, it relaxes the assumption in the original model by Foucault for which cancellation is not allowed and evaluate market performance. Results are mixed; the introduction of a cancellation option turns out to be benecial dependently on the key determinants of the market dynamic (i.e., the arrival rate and the percentage of patient traders) and an additional setup variable: the initial level of order aggressiveness in the market.
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 Applied Mathematics, Università Ca' Foscari Venezia in its series Working Papers with number 202.
Length: 36 pages
Date of creation: Sep 2010
Date of revision:
market evaluation; market design; equilibrium strategies; order cancellation; genetic algorithms.;
Find related papers by JEL classification:
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- D44 - Microeconomics - - Market Structure and Pricing - - - Auctions
- D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
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.