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. Vol. 6283 LNCS. Springer-Verlag, pp. 78-85. (Lecture Notes in Computer Science)

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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.

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