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Financial time series analysis based on information categorization method.

Qiang Tian1, Pengjian Shang1, Guochen Feng1

  • 1Department of Mathematics, School of Science, Beijing Jiaotong University, No. 3 of Shangyuan Residence, Haidian District, Beijing 100044, People's Republic of China.

Physica A
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study uses information categorization to analyze financial time series, revealing increased stock market similarity after major economic crises. The method effectively differentiates global markets.

Keywords:
Dissimilarity indexFinancial time seriesInformation categorization methodRank order

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Area of Science:

  • Financial econometrics
  • Data analysis
  • Time series analysis

Background:

  • Financial time series data is complex and requires robust analytical methods.
  • Understanding stock market dynamics and interdependencies is crucial for investors and policymakers.
  • Existing methods may not fully capture the evolving relationships between global markets.

Purpose of the Study:

  • To apply the information categorization method to financial time series.
  • To quantify and compare the similarity of different stock markets over time.
  • To assess the method's effectiveness in analyzing financial data and distinguishing market behaviors.

Main Methods:

  • Information categorization method applied to financial time series.
  • Calculation of distances between sequences to determine similarity.
  • Analysis of US and Chinese stock markets across three distinct economic periods (1991-2013).
  • Comparison of 10 stock indices across three geographical areas.

Main Results:

  • Significant differences in stock market similarity were observed across different time periods.
  • The similarity between US and Chinese stock markets increased following the Asian and global financial crises.
  • The information categorization method successfully distinguished between markets in different geographical areas, visualized through phylogenetic trees.

Conclusions:

  • The information categorization method provides satisfactory insights into financial markets.
  • This approach is versatile, applicable to both physiological and financial time series.
  • The findings highlight the dynamic nature of global stock market interdependencies, particularly in response to economic shocks.