Volume 11, Issue 40 (6-2020)
jemr 2020, 11(40): 123-158 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Saneifar M, Saeedi P. Comparison of Complex Networks of Stock Markets and Economic Variables in the Period Before and After the Outbreak of Coronavirus (Covid-19). jemr 2020; 11 (40) :123-158
URL: http://jemr.khu.ac.ir/article-1-2035-en.html
URL: http://jemr.khu.ac.ir/article-1-2035-en.html
1- Islamic Azad University, Aliabad Katoul branch
2- Islamic Azad University, Aliabad Katoul branch ,dr.parvizsaeedii@gmail.com
2- Islamic Azad University, Aliabad Katoul branch ,
Abstract: (5908 Views)
The corona virus has turned a health crisis into an economic crisis and its spread has led to strong negative reactions from stock markets in various countries and price fluctuations in many macroeconomic variables. On the other hand, the spread of the virus provides a basis for examining the effects of its prevalence on stock markets, economic variables and the power of influence and the speed of information dissemination in times of crisis in these markets. The aim of the present study was to investigate the effect of corona virus on the stock markets of 75 countries and the variables of oil, gold, silver and copper by comparing complex networks before and after the outbreak of the virus. Also, for the calculation section, matlab statistical software has been used and for drawing the networks, the maximum filtered flat graph method has been used with the help of daily data in the period from June 2019 to March 2020. the results show that before the outbreak of coronavirus, stock markets tended to move in small continental groups, but the outbreak of the virus led to negative group movements with high correlation for these markets, positive or negative information spreads 32% faster than before on the stock market network, also stock markets are twice as influential as they were before the outbreak. The corona virus has directly led to a 40% drop in stock markets. on the other hand, the virus has caused fluctuations in the global variables of oil, gold, silver and copper, which each respectively affected 55%, 32%, 28% and 35% of stock markets, the impact of these variables before the outbreak of the virus was 31%, 20%, 16% and 18% of stock markets, respectively. it is important to note that in crises due to the collective movements of stock markets, price stability in central stock markets and macroeconomic variables are very important to control and reduce the negative effects of the crisis on stock markets.
Type of Study: Applicable |
Subject:
تجارت و مالیه بین الملل
Received: 2020/04/5 | Accepted: 2020/08/25 | Published: 2020/09/22
Received: 2020/04/5 | Accepted: 2020/08/25 | Published: 2020/09/22
References
1. Albulescu, C. (2020a). Coronavirus and financial volatility: 40 days of fasting and fear. arXiv preprint arXiv:2003.04005. [DOI:10.2139/ssrn.3550630]
2. Albulescu, C. (2020b). Coronavirus and oil price crash. Available at SSRN 3553452. [DOI:10.2139/ssrn.3553452]
3. Birch, J., A.A. Pantelous, and K. Zuev, The maximum number of 3-and 4-cliques within a planar maximally filtered graph. Physica A: Statistical Mechanics and its Applications, 2015. 417: p. 221-229. [DOI:10.1016/j.physa.2014.09.011]
4. Barrat, A., & Weigt, M. (2000). On the properties of small-world network models. The European Physical Journal B-Condensed Matter and Complex Systems, 13(3), 547-560. [DOI:10.1007/s100510050067]
5. Bollobás, B. (1981). The diameter of random graphs. Transactions of the American Mathematical Society, 267(1), 41-52. [DOI:10.2307/1998567]
6. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163-177. [DOI:10.1080/0022250X.2001.9990249]
7. Balcı, M. A. 2020. Fractional Interaction of Financial Agents in a Stock Market Network. Applied Mathematics and Nonlinear Sciences 5 (1):317-336. [DOI:10.2478/amns.2020.1.00030]
8. Ding, G., and L. Qin. 2020. Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics 11 (6):1307-1317. [DOI:10.1007/s13042-019-01041-1]
9. George, S., & Changat, M. (2017). Network approach for stock market data mining and portfolio analysis. Paper presented at the 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). [DOI:10.1109/NETACT.2017.8076775]
10. Gormsen, N. J., & Koijen, R. S. (2020). Coronavirus: Impact on stock prices and growth expectations. University of Chicago, Becker Friedman Institute for Economics Working Paper(2020-22). [DOI:10.3386/w27387]
11. Helbing, D. 2013. Globally networked risks and how to respond. Nature, 497(7447), 51. [DOI:10.1038/nature12047]
12. Ismail Pourmoghadam, H., & Mohammadi, T., & Kashani, M., & Shakeri, A. (2018). Provide a new indicator to reflect stock market behavior using a complex network analysis approach. Financial Economics Journal, 13 Year, No. 46: p. 25-39. (In Persian)
13. Li, F., Identifying asymmetric comovements of international stock market returns. Journal of Financial Econometrics, 2013. 12(3): p. 507-543. [DOI:10.1093/jjfinec/nbt006]
14. Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social networks, 27(1), 39-54. [DOI:10.1016/j.socnet.2004.11.009]
15. Nobi, A., Lee, S., Kim, D. H., & Lee, J. W. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482-2489. [DOI:10.1016/j.physleta.2014.07.009]
16. Nie, C.-X. and F.-T. Song, Constructing financial network based on PMFG and threshold method. Physica A: Statistical Mechanics and its Applications, 2018. 495: p. 104-113. [DOI:10.1016/j.physa.2017.12.037]
17. Naderi, E., & Abbasi-Nejad, H. (2012). Chaos Analysis, Wavelet Decomposition and the Performance of Neural Network Models in Forecasting Tehran Stock Exchange Index. Journal of Research in Economic Modeling, 2(8), 119-140. (In Persian)
18. Pereira, E., Ferreira, P., & de Borges Pereira, H. B. (2020). COVID-19 in Stock Markets: A Complexity Perspective. [DOI:10.20944/preprints202005.0056.v1]
19. Mantegna, R.N., Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 1999. 11(1): p. 193-197. [DOI:10.1007/s100510050929]
20. Ramelli, S., & Wagner, A. F. (2020). Feverish stock price reactions to covid-19. [DOI:10.1093/rcfs/cfaa012]
21. Rafat, M. (2019). The Application of Complex Networks Analysis to Assess Iran's Trade and It's Most Important Trading Partners in Asia. Journal of Research in Economic Modeling, 9(34), 107-137. doi:10.29252/jemr.9.34.107(In Persian) [DOI:10.29252/jemr.9.34.107]
22. Selmi, R., & Bouoiyour, J. (2020). Global Market's Diagnosis on Coronavirus: A Tug of War between Hope and Fear.
23. Sornette, D. 2017. Why stock markets crash: critical events in complex financial systems. Princeton University Press. [DOI:10.23943/princeton/9780691175959.001.0001]
24. Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421-10426. [DOI:10.1073/pnas.0500298102]
25. Vandewalle, N., P. Boveroux, and F. Brisbois, Domino effect for world market fluctuations. The European Physical Journal B-Condensed Matter and Complex Systems, 2000. 15(3): p. 547-549. [DOI:10.1007/s100510051158]
26. West, D., Introduction to Graph Theory , Prntice-Hall. Englewood Cliffs, NJ, 2001.
27. Wagner, A. F. 2020. What the stock market tells us about the post-COVID-19 world. Nature Human Behaviour. [DOI:10.1038/s41562-020-0869-y]
28. Yan, B., Stuart, L., Tu, A., & Zhang, T. (2020). Analysis of the Effect of COVID-19 on the Stock Market and Potential Investing Strategies. Available at SSRN 3563380. [DOI:10.2139/ssrn.3563380]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
