An evolutionary multi-objective optimization of market structures using PBIL

Xinyang Li, Andreas Krause

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings
PublisherSpringer
Pages78-85
Number of pages8
ISBN (Print)9783642153808
DOIs
Publication statusPublished - 2010
Event11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, September 1, 2010 - September 3, 2010 - Paisley, UK United Kingdom
Duration: 1 Jan 2010 → …

Publication series

NameLecture Notes in Computer Science
Volume6283

Conference

Conference11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, September 1, 2010 - September 3, 2010
CountryUK United Kingdom
CityPaisley
Period1/01/10 → …

Fingerprint

Trading rules
Market structure
Evolutionary
Multi-objective optimization
Bid/ask spread
Call markets
Learning algorithm
Priority rules
Incremental
Agent-based model
Market makers
Informed traders
Market intervention
Tick size
Traders

Cite this

Li, X., & 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 (pp. 78-85). (Lecture Notes in Computer Science; Vol. 6283). Springer. https://doi.org/10.1007/978-3-642-15381-5_10

An evolutionary multi-objective optimization of market structures using PBIL. / Li, Xinyang; Krause, Andreas.

Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Springer, 2010. p. 78-85 (Lecture Notes in Computer Science; Vol. 6283).

Research output: Chapter in Book/Report/Conference proceedingChapter

Li, X & 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. Lecture Notes in Computer Science, vol. 6283, Springer, pp. 78-85, 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, September 1, 2010 - September 3, 2010, Paisley, UK United Kingdom, 1/01/10. https://doi.org/10.1007/978-3-642-15381-5_10
Li X, Krause A. 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. 2010. p. 78-85. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-15381-5_10
Li, Xinyang ; Krause, Andreas. / An evolutionary multi-objective optimization of market structures using PBIL. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Springer, 2010. pp. 78-85 (Lecture Notes in Computer Science).
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