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Identifying Dominant Economic Sectors and Stock Markets: A Social Network Mining Approach

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7867))

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Abstract

We propose a method to identify dominant economic sectors and stock markets using a social network approach to mining stock market data. Closing price data from January 1998 through January 2011 of 2698 stocks selected from 17 major stock market indices have been used in the analysis. A Minimum Spanning Tree (MST) has been constructed using the cross-correlations between weekly returns of the stocks. The MST has been chosen to obtain a simplified but connected network having linkages among similarly behaving stocks and it constitutes a social network of stocks for our study. The macroscopic interdependence networks among economic sectors as well as among stock markets have been derived from the microscopic linkages among stocks in the MST. The analysis of these derived macroscopic networks demonstrates that the European and the North American stock markets and Financial, Industrials, Materials, and Consumer Discretionary economic sectors dominate in the global stock markets.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India, 721 302

    Ram Babu Roy

  2. Indian Institute of Management Calcutta, Kolkata, India, 700 104

    Uttam Kumar Sarkar

Authors
  1. Ram Babu Roy
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  2. Uttam Kumar Sarkar
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Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, 1 Mawson Lakes Boulevard, 5095, Adelaide, SA, Australia

    Jiuyong Li

  2. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, 2008, Sydney, NSW, Australia

    Longbing Cao  & Can Wang  & 

  3. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  4. School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Panyu District, 510006, Guangzhou, China

    Bo Liu

  5. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  6. Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, University Road, 701, Tainan, Taiwan

    Vincent S. Tseng

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Roy, R.B., Sarkar, U.K. (2013). Identifying Dominant Economic Sectors and Stock Markets: A Social Network Mining Approach. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-40319-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40318-7

  • Online ISBN: 978-3-642-40319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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