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Financial time series analysis based on information categorization method

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  • Tian, Qiang
  • Shang, Pengjian
  • Feng, Guochen

Abstract

The paper mainly applies the information categorization method to analyze the financial time series. The method is used to examine the similarity of different sequences by calculating the distances between them. We apply this method to quantify the similarity of different stock markets. And we report the results of similarity in US and Chinese stock markets in periods 1991–1998 (before the Asian currency crisis), 1999–2006 (after the Asian currency crisis and before the global financial crisis), and 2007–2013 (during and after global financial crisis) by using this method. The results show the difference of similarity between different stock markets in different time periods and the similarity of the two stock markets become larger after these two crises. Also we acquire the results of similarity of 10 stock indices in three areas; it means the method can distinguish different areas’ markets from the phylogenetic trees. The results show that we can get satisfactory information from financial markets by this method. The information categorization method can not only be used in physiologic time series, but also in financial time series.

Suggested Citation

  • Tian, Qiang & Shang, Pengjian & Feng, Guochen, 2014. "Financial time series analysis based on information categorization method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 183-191.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:183-191
    DOI: 10.1016/j.physa.2014.08.055
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    References listed on IDEAS

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    1. Yin, Yi & Shang, Pengjian, 2013. "Modified DFA and DCCA approach for quantifying the multiscale correlation structure of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6442-6457.
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    5. Xia, Jianan & Shang, Pengjian & Wang, Jing & Shi, Wenbin, 2014. "Classifying of financial time series based on multiscale entropy and multiscale time irreversibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 151-158.
    6. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.
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