Research

An evolutionary multi-objective optimization of market structures using PBIL


Reference:

Li, X. and Krause, A., 2010. An evolutionary multi-objective optimization of market structures using PBIL. In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Springer-Verlag, pp. 78-85. (Lecture Notes in Computer Science; 6283)

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.

Official URL:

http://dx.doi.org/10.1007/978-3-642-15381-5_10

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 market makers and the presence of informed traders. We optimize these trading rules using a multi-objective population-based incremental learning (PIBL) algorithm seeking to maximize the trading price and minimize the bid-ask spread. Our results suggest that markets should choose a relatively large tick size unless concerns about either the bid-ask spread or the trading price are dominating. We also find that in contrast to trading rules in actual markets, reverse time priority is an optimal priority rule.

Details

Item Type Book Sections
CreatorsLi, X.and Krause, A.
DOI10.1007/978-3-642-15381-5_10
DepartmentsSchool of Management
StatusPublished
ID Code21634
Additional Information11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010. 1-3 September 2010. Paisley, UK.

Export

Actions (login required)

View Item