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A network perspective of the stock market

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  • Tse, Chi K.
  • Liu, Jing
  • Lau, Francis C.M.

Abstract

Complex networks are constructed to study correlations between the closing prices for all US stocks that were traded over two periods of time (from July 2005 to August 2007; and from June 2007 to May 2009). The nodes are the stocks, and the connections are determined by cross correlations of the variations of the stock prices, price returns and trading volumes within a chosen period of time. Specifically, a winner-take-all approach is used to determine if two nodes are connected by an edge. So far, no previous work has attempted to construct a full network of US stock prices that gives full information about their interdependence. We report that all networks based on connecting stocks of highly correlated stock prices, price returns and trading volumes, display a scalefree degree distribution. The results from this work clearly suggest that the variation of stock prices are strongly influenced by a relatively small number of stocks. We propose a new approach for selecting stocks for inclusion in a stock index and compare it with existing indexes. From the composition of the highly connected stocks, it can be concluded that the market is heavily dominated by stocks in the financial sector.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 17 (2010)
Issue (Month): 4 (September)
Pages: 659-667

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Handle: RePEc:eee:empfin:v:17:y:2010:i:4:p:659-667

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Web page: http://www.elsevier.com/locate/jempfin

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Keywords: Stock market Complex network Degree distribution Stock indexes;

References

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  1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 11(1), pages 193-197, September.
  2. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, 04.
  3. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
  4. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 38(2), pages 353-362, 03.
  5. G. Bonanno & G. Caldarelli & F. Lillo & S. Micciche` & N. Vandewalle & R. N. Mantegna, 2004. "Networks of equities in financial markets," Papers cond-mat/0401300, arXiv.org.
  6. Giovanni Bonanno & Fabrizio Lillo & Rosario N. Mantegna, 2000. "High-frequency Cross-correlation in a Set of Stocks," Papers cond-mat/0009350, arXiv.org, revised Nov 2000.
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Citations

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Cited by:
  1. Hu, Sen & Yang, Hualei & Cai, Boliang & Yang, Chunxia, 2013. "Research on spatial economic structure for different economic sectors from a perspective of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3682-3697.
  2. Fang, Yiwei & Francis, Bill & Hasan, Iftekhar & Wang, Haizhi, 2012. "Product market relationships and cost of bank loans: Evidence from strategic alliances," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 653-674.
  3. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2011. "The instability of the correlation structure of the S&P 500," MPRA Paper 34160, University Library of Munich, Germany.
  4. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.
  5. Heiberger, Raphael H., 2014. "Stock network stability in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 376-381.
  6. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
  7. Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4136-4146.
  8. Chunxia, Yang & Bingying, Xia & Sen, Hu & Rui, Wang, 2012. "A study of the interplay between the structure variation and fluctuations of the Shanghai stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3198-3205.
  9. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.

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