TY - CHAP
T1 - An evolutionary multi-objective optimization of market structures using PBIL
AU - Li, Xinyang
AU - Krause, Andreas
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://dx.doi.org/10.1007/978-3-642-15381-5_10
UR - http://www.scopus.com/inward/record.url?scp=78049382212&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15381-5_10
DO - 10.1007/978-3-642-15381-5_10
M3 - Chapter or section
SN - 9783642153808
T3 - Lecture Notes in Computer Science
SP - 78
EP - 85
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings
PB - Springer
T2 - 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, September 1, 2010 - September 3, 2010
Y2 - 1 January 2010
ER -