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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/23009


    Title: Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rule
    Authors: 陳樹衡;T.Yu;T.-W. Kuo
    Date: 2004-07
    Issue Date: 2009-01-09 11:21:15 (UTC+8)
    Abstract: Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold
    Relation: No 200, Computing in Economics and Finance 2004 from Society for Computational Economics
    Data Type: conference
    Appears in Collections:[Department of Economics] Proceedings

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