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Computational study of the US stock market evolution: a rank correlation-based network model

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Abstract

This paper presents a computational study of global characteristics of the US stock market using a network-based model referred to as the market graph. The market graph reflects similarity patterns between stock return fluctuations via linking pairs of stocks that exhibit “coordinated” behavior over a specified period of time. We utilized Spearman rank correlation as a measure of similarity between stocks and considered the evolution of the market graph over the recent decade between 2001–2011. The observed market graph characteristics reveal interesting trends in the stock market over time, as well as allow one to use this model to identify cohesive clusters of stocks in the market.

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References

  • Aiello W, Chung F, Lu L (2001) Random evolution in massive graphs. Annu Symp Found Comput Sci 42:510–521

    Google Scholar 

  • Barabasi A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  • Barabasi A, Albert R, Jeong H (1999) Scale-free characteristics of random networks: the topology of the world wide web. Physica A272:173–187

    Google Scholar 

  • Batagelj V, Zaversnik M (2002) Generalized cores.

  • Batagelj V, Zaversnik M (2003) An \(O(m)\) algorithm for cores decomposition of networks.

  • Boginski V, Butenko S, Pardalos PM (2003) On structural properties of the market graph. In: Nagurney A (ed) Innovation in financial and economic networks. Edward Elgar Publishers, London, pp 29–45

    Google Scholar 

  • Boginski V, Butenko S, Pardalos PM (2005) Statistical analysis of financial networks. Comput Stat Data Anal 48:431–443

    Article  Google Scholar 

  • Boginski V, Butenko S, Pardalos PM (2006) Mining market data: a network approach. Comput Oper Res 33:3171–3184

    Article  Google Scholar 

  • Bollerslev T (1986) Generalized autoregresssive conditional heteroskedasticity. J Econ 31:307–327

    Google Scholar 

  • Bollobás B, Thomason A (1985) Random graphs of small order. In: Karonski M, Rucinski A (eds) Random graphs. volume 118 of North-Holland Mathematics Studies, North-Holland, pp 47–97.

  • Bomze IM, Budinich M, Pardalos PM, Pelillo M (1999) The maximum clique problem. In: Du DZ, Pardalos PM (eds) Handbook of combinatorial optimization. Kluwer Academic Publishers, Dordrecht, pp 1–74

    Chapter  Google Scholar 

  • Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tompkins A, Wiener J (2000) Graph structure in the web. Comput Netw 33:309–321

    Article  Google Scholar 

  • Campbell J, Lo A, MacKinlay C (1997) The econometrics of financial markets. Princeton University Press, Princeton

    Google Scholar 

  • Grane A, Veiga H (2010) Wavelet-based detection of outliers in financial time series. Comput Stat Data Anal 54:2580–2593

    Article  Google Scholar 

  • Huang WQ, Zhuang XT, Yao S (2009) A network analysis of the chinese stock market. Phys A Stat Mech Appl 388:2956–2964

    Article  Google Scholar 

  • Kim T, White H (2004) On more robust estimation of skewness and kurtosis. Financ Res Lett 1:56–73

    Article  Google Scholar 

  • Nagurney A (ed) (2003) Innovations in financial and economic networks. Edward Elgar Publishers, London

    Google Scholar 

  • Nagurney A, Siokos S (1997) Financial networks: statics and dynamics. Springer, Berlin

    Book  Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  Google Scholar 

  • Pattillo J, Veremyev A, Butenko S, Boginski V (2013) On the maximum quasi-clique problem. Discret Appl Math 161:244–257

    Article  Google Scholar 

  • Pena D (2001) Outliers, influential observations and missing data. In: Pena D, Tiao G, Tsay R (eds) A course in time series. Wiley, New York, pp 136–170

    Google Scholar 

  • Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42:59–66

    Article  Google Scholar 

  • Seidman SB (1983) Network structure and minimum degree. Soc Netw 5:269–287

    Article  Google Scholar 

  • Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101

    Article  Google Scholar 

  • Tarjan R (1972) Depth-first search and linear graph algorithms. SIAM J Comput 2:146–160

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, USA

    Oleg Shirokikh & Grigory Pastukhov

  2. Department of Industrial and Systems Engineering, Research and Engineering Education Facility (REEF), University of Florida, Shalimar, FL, 32579, USA

    Vladimir Boginski

  3. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA

    Sergiy Butenko

Authors
  1. Oleg Shirokikh
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  2. Grigory Pastukhov
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  3. Vladimir Boginski
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  4. Sergiy Butenko
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Corresponding author

Correspondence to Vladimir Boginski.

Appendix: 25 highest degree stocks for each time period

The sector “Funds, Trusts and Tracking Stocks” is denoted by “FTTS”.

Appendix: 25 highest degree stocks for each time period

See Tables  4,  5,  6,  7,  8,  9,  10,  11,  12 and  13.

Table 4 1st period (November 2001–November 2002)
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Table 5 2nd period (November 2002–November 2003)
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Table 6 3rd period (November 2003–November 2004)
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Table 7 4th period (November 2004–November 2005)
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Table 8 5th period (November 2005–November 2006)
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Table 9 6th period (November 2006–November 2007)
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Table 10 7th period (November 2007–November 2008)
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Table 11 8th period (November 2008–November 2009)
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Table 12 9th period (November 2009–November 2010)
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Table 13 10th period (November 2010–November 2011)
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Shirokikh, O., Pastukhov, G., Boginski, V. et al. Computational study of the US stock market evolution: a rank correlation-based network model. Comput Manag Sci 10, 81–103 (2013). https://doi.org/10.1007/s10287-012-0160-4

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  • DOI: https://doi.org/10.1007/s10287-012-0160-4

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