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

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  • Oleg Shirokikh
  • Grigory Pastukhov
  • Vladimir Boginski
  • Sergiy Butenko

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. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
    • Handle: RePEc:spr:comgts:v:10:y:2013:i:2:p:81-103
    DOI: 10.1007/s10287-012-0160-4
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    References listed on IDEAS

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    1. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
    4. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March.
    5. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
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    Citations

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    Cited by:

    1. Alina Zaharia, 2021. "Estimation of Correlation between Capital Markets. Analysing the case of Central and Eastern European markets in the context of the COVID-19 pandemic," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 13(1), pages 61-78, June.
    2. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A., 2022. "Reliability of maximum spanning tree identification in correlation-based market networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    3. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos, 2018. "Optimal decision for the market graph identification problem in a sign similarity network," Annals of Operations Research, Springer, vol. 266(1), pages 313-327, July.
    4. Neto, José de Paula Neves & Figueiredo, Daniel Ratton, 2023. "Ranking influential and influenced stocks over time using transfer entropy networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. Halkos, George & Tsilika, Kyriaki, 2016. "Measures of correlation and computer algebra," MPRA Paper 70200, University Library of Munich, Germany.
    6. Koldanov, A. & Koldanov, P. & Semenov, D., 2021. "Confidence set for connected stocks of stock market," Journal of the New Economic Association, New Economic Association, vol. 50(2), pages 12-34.
    7. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    8. Vladimir Boginski & Sergiy Butenko & Oleg Shirokikh & Svyatoslav Trukhanov & Jaime Gil Lafuente, 2014. "A network-based data mining approach to portfolio selection via weighted clique relaxations," Annals of Operations Research, Springer, vol. 216(1), pages 23-34, May.
    9. Dong, Zhiliang & An, Haizhong & Liu, Sen & Li, Zhengyang & Yuan, Meng, 2020. "Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 63-74.
    10. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    11. V. A. Kalyagin & P. A. Koldanov & P. M. Pardalos, 2015. "Optimal decision for the market graph identification problem in sign similarity network," Papers 1512.06449, arXiv.org.
    12. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2015. "An autocatalytic network model for stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 122-127.

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