<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. G. Pavlidis</style></author><author><style face="normal" font="default" size="100%">E. G. Pavlidis</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">V. P. Plagianakos</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2007. CEC 2007</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational intelligence algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">differential evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">financial market</style></keyword><keyword><style  face="normal" font="default" size="100%">foreign exchange market</style></keyword><keyword><style  face="normal" font="default" size="100%">foreign exchange trading</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized moving average rule</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern detection</style></keyword><keyword><style  face="normal" font="default" size="100%">risk analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">risk-adjusted trading strategy</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical testing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Singapore</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper investigates the performance of trading strategies identified through computational intelligence techniques. We focus on trading rules derived by genetic programming, as well as, generalized moving average rules optimized through differential evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but genetic programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.</style></abstract></record></records></xml>