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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/iconip/ChenN06,

• title = "Pretests for Genetic-Programming Evolved Trading Programs: zero-intelligence Strategies and Lottery Trading",
• author = "Shu-Heng Chen and Nicolas Navet",
• booktitle = "Neural Information Processing, 13th International Conference, {ICONIP} 2006, Proceedings, Part {III}",
• publisher = "Springer",
• year = "2006",
• volume = "4234",
• editor = "Irwin King and Jun Wang and Laiwan Chan and DeLiang L. Wang",
• pages = "450--460",
• series = "Lecture Notes in Computer Science",
• address = "Hong Kong, China",
• month = oct # " 3-6",
• bibdate = "2006-10-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2006-3.html#ChenN06",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-46484-0",
• DOI = "doi:10.1007/11893295_50",
• 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.",

}

Genetic Programming entries for Shu-Heng Chen Nicolas Navet

Citations