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Cross-correlation and the predictability of financial return series

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  • Duan, Wen-Qi
  • Stanley, H. Eugene

Abstract

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.

Suggested Citation

  • Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:2:p:290-296
    DOI: 10.1016/j.physa.2010.09.013
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    Cited by:

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    3. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
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    5. Aki-Hiro Sato & Takaki Hayashi & Janusz Hołyst, 2012. "Comprehensive analysis of market conditions in the foreign exchange market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 167-179, October.
    6. Lin, Chiun-Sin & Chiu, Sheng-Hsiung & Lin, Tzu-Yu, 2012. "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2583-2590.

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