Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system|
|Citation:||Proceedings of the 12th International Conference on Parallel Problem Solving from Nature, held in Taormina, Italy, 1-5 September, 2012 / C.A. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia and M. Pavone (eds.): pp.42-51|
|Series/Report no.:||Lecture Notes in Computer Science; 7492|
|Conference Name:||International Conference on Parallel Problem Solving from Nature (12th : 2012 : Taormina, Italy)|
|Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegg|
|Abstract:||This paper examines the interaction of decision model complexity and utility in a computational intelligence system for algorithmic trading. An empirical analysis is undertaken which makes use of recent developments in multiobjective evolutionary fuzzy systems (MOEFS) to produce and evaluate a Pareto set of rulebases that balance conflicting criteria. This results in strong evidence that controlling portfolio risk and return in this and other similar methodologies by selecting for interpretability is feasible. Furthermore, while investigating these properties we contribute to a growing body of evidence that stochastic systems based on natural computing techniques can deliver results that outperform the market.|
|Rights:||© Springer-Verlag Berlin Heidelberg 2012|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.