Cluster Computing

, Volume 22, Supplement 2, pp 4307–4313 | Cite as

Risk prediction and evaluation of transnational transmission of financial crisis based on complex network

  • Chang LiuEmail author
  • N. Arunkumar


In this paper, the transmission characteristics of financial crisis in stock market are studied based on complex network. The influences factor and propagation model of financial crisis in international stock market are qualitatively analyzed through comprehensive application of the qualitative and quantitative analysis method by taking complex network and communication theory of financial crisis as theoretical basis. Meanwhile, the transmission mechanism, measurement of transmission effect, transmission path and immunization strategy of financial crisis in stock market network are empirically analyzed.


Complex network Financial crisis Transnational transmission Risk prediction 


  1. 1.
    Wu, F., Li, J., Li, J., et al.: Research on risk evaluation of transnational power networking projects based on the matter-element extension theory and granular computing. Energies 10(10), 1523 (2017)Google Scholar
  2. 2.
    Huang, R.L.A., Huang, Z.P.: The impact of financial crisis on real economy: crisis spillover and transnational transmission. J. Jiangsu Admin. Inst. 3, 009 (2011)Google Scholar
  3. 3.
    Mahnkopf, B., Altvater, E.: Transmission belts of transnational competition? Trade unions and collective bargaining in the context of European integration. Eur. J. Ind. Relat. 1(1), 101–117 (1995)Google Scholar
  4. 4.
    Roda, J.M., Kamaruddin, N., Tobias, R.P.: Deciphering corporate governance and environmental commitments among Southeast Asian transnationals: uptake of sustainability certification. Forests 6(5), 1454–1475 (2014)Google Scholar
  5. 5.
    Chalaby, J.K.: Broadcasting in a post-national environment: the rise of transnational TV groups. Crit. Stud. Telev. Int. J. Telev. Stud. 4(1), 39–64 (2009)Google Scholar
  6. 6.
    Bevans, P.G., Mckay, J.: The association of transnational law schools’ agora: an experiment in graduate legal pedagogy. German Law J. 10, 929–958 (2009)Google Scholar
  7. 7.
    Christy, S.T., Levine, O.H., Pierce, E.J.: Network-based text composition, translation, and document searching. US 20020002452 A1 (2002)Google Scholar
  8. 8.
    Taylor, J.B.: The monetary transmission mechanism. In: Taylor, J.B. (ed.) The Evaluation of Monetary Policy Rules, pp. 121–130. University of Chicago Press, Chicago (2010)Google Scholar
  9. 9.
    Russell, J.J., Gagliano, R.S.: Consultative decision engine method and system for financial transactions. US 20020194120 A1 (2002)Google Scholar
  10. 10.
    Friel, S., Ford, L.: Systems, food security and human health. Food Secur. 7(2), 437–451 (2015)Google Scholar
  11. 11.
    Zio, E., Golea, L.R.: Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms. Reliab. Eng. Syst. Saf. 99(99), 172–177 (2012)Google Scholar
  12. 12.
    Kayser, G.L., Patrick, M., Catarina, F., et al.: Domestic water service delivery indicators and frameworks for monitoring, evaluation, policy and planning: a review. Int. J. Environ. Res. Public Health 10(10), 4812–35 (2013)Google Scholar
  13. 13.
    Costa, I.D., Pulignano, V., Rehfeldt, U., et al.: Transnational negotiations and the Europeanization of industrial relations: potential and obstacles. Eur. J. Ind. Relat. 18(2), 123–137 (2012)Google Scholar
  14. 14.
    Boie, I., Fernandes, C., Frías, P., et al.: Efficient strategies for the integration of renewable energy into future energy infrastructures in Europe—an analysis based on transnational modeling and case studies for nine European regions. Energy Policy 67(10), 170–185 (2014)Google Scholar
  15. 15.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)Google Scholar
  16. 16.
    Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inf. 6(3), 724–730 (2016)Google Scholar
  17. 17.
    Hamza, R., Muhammad, K., Arunkumar, N., Gustavo Ramírez, G.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017).
  18. 18.
    Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017).
  19. 19.
    Liu, S., Cai, C., Zhu, Q., Arunkumar, N.: A study of software pools for seismogeology-related software based on the Docker technique. Int. J. Comput. Appl. (2017).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Master of Science in Global Finance, Gabelli School of BusinessFordham UniversityNew YorkUSA
  2. 2.SASTRA UniversityThanjavurIndia

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