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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/23057


    Title: Are Efficient Markets Really Efficient?: Can Financial Econometric Tests Convince Machine-Learning People?
    Authors: 陳樹衡;T.-W. Kuo
    Chen,Shu-Heng
    Date: 2004
    Issue Date: 2009-01-09 11:26:22 (UTC+8)
    Abstract: Using Quinlan’s Cubist, this paper examines whether there is a consistent, interpretation of the efficient market hypothesis between financial econometrics and machine learning. In particular, we ask whether machine learning can be useful only in the case when the market is not efficient. Based on the forecasting performance of Cubist in our artificial returns, some evidences seems to support this consistent interpretation. However, there are a few cases whereby Cubist can beat the random walk even though the series is independent. As a result, we do not consider that the evidence is strong enough to convince one to give up his reliance on machine learning even though the efficient market hypothesis is sustained.
    Relation: Computational Intelligence in Economics and Finance
    Advanced Information Processing 2004, pp 288-296
    Data Type: conference
    Appears in Collections:[經濟學系] 會議論文

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