Dynamic Local Trend Associations in Analysis of Comovements of Financial Time Series

  • Francisco Javier García-LópezEmail author
  • Ildar Batyrshin
  • Alexander Gelbukh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 648)


We show that the correlation coefficient, often used for analysis of co-movements of financial time series, can be misleading because it does not take into account the time ordering of time series values. We propose the new method of analysis of time series comovements based on dynamic local trend association measure. This measure can capture the dynamic change of the sign of association between time series. The advantage of the new method is demonstrated on examples of financial time series. The associations between time series dynamics and related events are also considered.


Time series Comovement Association measure Stock market Event Correlation 



The work was supported by the projects IPN SIP 20171344 and SEP - CONACYT 283778, Mexico.


  1. 1.
    Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Panova, A.: Moving approximation transform and local trend associations in time series data bases. In: Perception-based Data Mining and Decision Making in Economics and Finance, pp. 55–83. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Batyrshin, I., Solovyev, V., Ivanov, V.: Time series shape association measures and local trend association patterns. Neurocomputing 175, 924–934 (2016)CrossRefGoogle Scholar
  3. 3.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 12 (2012)CrossRefzbMATHGoogle Scholar
  4. 4.
    Schaefer, R., Nilsson, N.F., Guhr, T.: Power mapping with dynamical adjustment for improved portfolio optimization. Quant. Financ. 10(1), 107–119 (2010)CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Filis, G., Degiannakis, S., Floros, C.: Dynamic correlation between stock market and oil prices: the case of oil-importing and oil-exporting countries. Int. Rev. Financ. Anal. 20(3), 152–164 (2011)CrossRefGoogle Scholar
  6. 6.
    Paparrizos, J., Gravano, L.: Fast and accurate time-series clustering. ACM Trans. Database Syst. (TODS) 42(2), 8 (2017)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Peng, Y., Jiang, H.: Leverage financial news to predict stock price movements using word embeddings and deep neural networks. arXiv preprint arXiv:1506.07220 (2015)
  8. 8.
    Barberis, N., Shleifer, A., Wurgler, J.: Comovement. J. Financ. Econ. 75(2), 283–317 (2005)CrossRefGoogle Scholar
  9. 9.
    Croux, C., Forni, M., Reichlin, L.: A measure of comovement for economic variables: theory and empirics. Rev. Econ. Stat. 83(2), 232–241 (2001)CrossRefGoogle Scholar
  10. 10.
    Goodman, L.A.: Tests Based on the Movements in and the Comovements between m-Dependent Time Series. Columbia University, New York (1961)CrossRefzbMATHGoogle Scholar
  11. 11.
    Goodman, L.A., Grunfeld, Y.: Some nonparametric tests for comovements between time series. J. Am. Stat. Assoc. 56(293), 11–26 (1961)CrossRefMathSciNetzbMATHGoogle Scholar
  12. 12.
    Papadimitriou, S., Sun, J., Philip, S.Y.: Local correlation tracking in time series. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 456–465. IEEE (2006)Google Scholar
  13. 13.
    Pindyck, R.S., Rotemberg, J.J.: The excess co-movement of commodity prices. Econ. J. 100(403), 1173–1189 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Francisco Javier García-López
    • 1
    Email author
  • Ildar Batyrshin
    • 1
  • Alexander Gelbukh
    • 1
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico

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