Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading Export

@incollection{citeulike:5911315, abstract = {Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.}, author = {Chen, Shu-Heng and Navet, Nicolas}, citeulike-article-id = {5911315}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11893295_50}, citeulike-linkout-1 = {http://www.springerlink.com/content/840urg84012826r3}, doi = {10.1007/11893295_50}, journal = {Neural Information Processing}, keywords = {geneticprogramming, tradingstrategy}, pages = {450--460}, posted-at = {2009-10-08 21:00:19}, priority = {2}, title = {Pretests for Genetic-Programming Evolved Trading Programs: ” zero-intelligence” Strategies and Lottery Trading}, url = {http://dx.doi.org/10.1007/11893295_50}, year = {2006} }

TY - CHAP ID - citeulike:5911315 L3 - citeulike-article-id:5911315 N2 - Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends. JF - Neural Information Processing EP - 460 TI - Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading SP - 450 KW - geneticprogramming KW - tradingstrategy AU - Chen, Shu-Heng AU - Navet, Nicolas PY - 2006/// UR - http://dx.doi.org/10.1007/11893295_50 ER -