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題名 Failure of Genetic Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms
作者 陳樹衡
Chen,Shu-Heng
貢獻者 經濟系
日期 2007
上傳時間 14-Aug-2014 12:05:32 (UTC+8)
摘要 Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the 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 aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends.
關聯 Computational Intelligence in Economics and Finance 2007, pp 169-182
資料類型 book/chapter
dc.contributor 經濟系en_US
dc.creator (作者) 陳樹衡zh_TW
dc.creator (作者) Chen,Shu-Hengen_US
dc.date (日期) 2007en_US
dc.date.accessioned 14-Aug-2014 12:05:32 (UTC+8)-
dc.date.available 14-Aug-2014 12:05:32 (UTC+8)-
dc.date.issued (上傳時間) 14-Aug-2014 12:05:32 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/68726-
dc.description.abstract (摘要) Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the 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 aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends.en_US
dc.format.extent 210166 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Computational Intelligence in Economics and Finance 2007, pp 169-182en_US
dc.title (題名) Failure of Genetic Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithmsen_US
dc.type (資料類型) book/chapteren