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

Xinyang Li, Andreas Krause

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)


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
Issue number1
Publication statusPublished - Jan 2011


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