Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/68721
DC Field | Value | Language |
---|---|---|
dc.contributor | 經濟系 | en_US |
dc.creator | 陳樹衡 | zh_TW |
dc.creator | Chen,Shu-Heng | en_US |
dc.date | 2006 | en_US |
dc.date.accessioned | 2014-08-14T03:48:37Z | - |
dc.date.available | 2014-08-14T03:48:37Z | - |
dc.date.issued | 2014-08-14T03:48:37Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/68721 | - |
dc.description.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. | en_US |
dc.format.extent | 370154 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en_US | - |
dc.relation | Neural Information Processing Lecture Notes in Computer Science Volume 4234, 2006, pp 450-460 | en_US |
dc.title | Pretests for Genetic - Programming Evolved Trading Programs: | en_US |
dc.type | book/chapter | en |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en_US | - |
item.openairetype | book/chapter | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | 專書/專書篇章 |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.