An evolutionary multi-objective optimization of trading rules in call markets

Xinyang Li, Andreas Krause

Research output: Contribution to journalArticle

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 and 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 algorithm seeking to maximize the trading volume and minimize the bid–ask spread. Our results suggest that markets should choose a small tick size if concerns about the bid–ask spread are dominating and a large tick size if maximizing trading volume is the main aim. We also find that unless concerns about trading volume dominate, time priority is the optimal priority rule.
Original languageEnglish
Pages (from-to)1-14
JournalIntelligent Systems in Accounting, Finance and Management
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2011

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

Cite this

An evolutionary multi-objective optimization of trading rules in call markets. / Li, Xinyang; Krause, Andreas.

In: Intelligent Systems in Accounting, Finance and Management, Vol. 18, No. 1, 01.2011, p. 1-14.

Research output: Contribution to journalArticle

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