Skip to main content
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  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. 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. 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. 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. 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. 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. Christy, S.T., Levine, O.H., Pierce, E.J.: Network-based text composition, translation, and document searching. US 20020002452 A1 (2002)

  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. Russell, J.J., Gagliano, R.S.: Consultative decision engine method and system for financial transactions. US 20020194120 A1 (2002)

  10. Friel, S., Ford, L.: Systems, food security and human health. Food Secur. 7(2), 437–451 (2015)

    Google Scholar 

  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. 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. 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. 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. 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. 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. Hamza, R., Muhammad, K., Arunkumar, N., Gustavo Ramírez, G.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017). https://doi.org/10.1109/ACCESS.2017.2762405

  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). https://doi.org/10.1016/j.patrec.2017.07.002

  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). https://doi.org/10.1080/1206212X.2017.1396429

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chang Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, C., Arunkumar, N. Risk prediction and evaluation of transnational transmission of financial crisis based on complex network. Cluster Comput 22 (Suppl 2), 4307–4313 (2019). https://doi.org/10.1007/s10586-018-1870-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1870-3

Keywords

Navigation