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.
|Name||Lecture Notes in Computer Science|
|Conference||11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, September 1, 2010 - September 3, 2010|
|Country/Territory||UK United Kingdom|
|Period||1/01/10 → …|