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Cross-correlation and the predictability of financial return series

  • Duan, Wen-Qi
  • Stanley, H. Eugene
Registered author(s):

    This paper examines whether we can improve the predictability of financial return series by exploiting the effect of cross-correlations among different financial markets. We forecast financial return series based on the support vector machines (SVM) method, which can surpass the random-walk model consistently. By comparing the mean absolute errors and the root mean squared errors, we show that it is hard to improve the predictability of financial return series by incorporating correlated return series into SVM-based forecasting models, even though there are Granger causal relationships among them.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437110007934
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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 390 (2011)
    Issue (Month): 2 ()
    Pages: 290-296

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    Handle: RePEc:eee:phsmap:v:390:y:2011:i:2:p:290-296
    Contact details of provider: Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

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