Vulnerability of Contagion Network in Iran Stock Market: Complex Networks Theory Approach

Document Type : Original Article

Authors

1 Assistant Professor of Economics, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran

2 Ph.D. Candidate in Economics, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran

10.29252/jem.2022.226674.1745

Abstract

In today's world, the interconnected structures of modern economies have caused the crisis to spread from one sector or country to other countries or sectors of the economy. Empirical evidence has shown that markets are interconnected and do not move apart. In the present study, a new approach is presented to investigate the contagion effect in Iran stock market. First, with the economic analysis, a network of 36 selected industries of Iran's economy is formed in accordance with the industries active in the stock market and the weight of the edges that connect them is determined according to the input- output table of Iran. In the next step, a correlation network is formed between the stock market indices of these 36 industries by using centrality criteria, Key industries are identified and then attacked. Comparison of the results of the two networks show that only in a limited number of cases in both networks, the basic metals industry, the banking industry, investment and other financial intermediaries, and the agricultural industry have won first place in accordance with a number of centrality criteria. But in other cases, the two networks have not the same results, which shows that economic relations among industries do not dominate their stock market index relations alone. The results of this study help both policymakers and shareholders to make appropriate decisions as soon as they see a crisis in important industries of the stock market.

Keywords

References

- Adams, Z., Füss, R., & Gropp, R. (2014). Spillover effects among financial institutions: A state-dependent sensitivity value-at-risk approach. Journal of Financial and Quantitative Analysis, 49(3), 575-598.
- Azari Qaralar, A., & Rastegar, M. A. (2016). Comparison of systemic risk measures in Tehran Stock Exchange companies. Conference on Mathematics and Humanities, Shiraz, Iran (In Persian).
- Coletti, P. (2016). Comparing minimum spanning trees of the Italian stock market using returns and volumes. Physica A, 463, 246–261.
- Dimitrios, K., Vasileios, O. (2015). A network analysis of the Greek stock market. Procedia Economics and Finance, 33, 340-349.
- Dong, Y., Wang, J., & Chen, T. (2019). Price linkage rumors in the stock market and investor risk contagion on bilayer-coupled networks. Complexity, 19, 1-22.
- Eberhard, J., Lavin, J. F. & Montecinos-Pearce, A. (2017). A Network-Based Dynamic Analysis in an Equity Stock Market. Complexity, 17, 1-16.
- Garmaise, M. & Natividad, G. (2016). Spillovers in Local Banking Markets. The Review of Corporate Finance Studies, 5(2), 139–165.
- Esmailpour, H., Mohammadi, T., Kashani, M., & Shakeri, A. (2019). Provide a new indicator to reflect stock market behavior using a complex network analysis approach. Financial Economics Quarterly, (46) 13, 25-40 (In Pesian).
- George, S. & Changat, M. (2017). Network approach for stock market data mining and portfolio analysis, International Conference on Networks & Advances in Computational Technologies (NetACT), Thiruvanthapuram, 17, 251-256.
- Glattfelder, J.B. (2010). Ownership networks and corporate control: mapping economic power in a globalized world. Ph.D Thesis, Eth Zurich University.
- Gunay, S. (2020). A new form of financial contagion: COVID-19 and stock market responses. Available at SSRN 3584243.
- Hansen, D. L., Shneiderman, B. & M. A. Smith (2011). Analyzing Social Media Networks with Nodexl: Insights from a Connected World. China: Elsevier Inc.
- Holme, P., & Kim, B. J. (2002). Attack vulnerability of complex networks. Physical Review, 65, 1-23.
- Hernandez, J.A., Kang, S.H., Shahzad, S.J.H., & Yoon, S.M. (2020). Spillovers and diversification potential of bank equity returns from developed and emerging America. The North American Journal of Economics and Finance, 54, 101219.
- Kaufman, G.G. (2000). Banking and currency crises and systemic risk: A taxonomy and review (Vol. 48). Blackwell Publishers.
-Keswani, S., & Wadhwa, B. (2019). Effect of macroeconomic variables on stock market: a conceptual study. International Journal of Management, IT and Engineering, 7(10), 85-106.
- Khiabani, N., & Mohammadian Nikopi, A. (2018). Systemic risk analysis in selected industries of Tehran Stock Exchange: A multivariate regression approach. Iran Economic Research, (77) 23, 1- 36 (In Persian)
- Long, Y., Yoshida, Y., Liu, Q., Zhang, H., Wang, S., & Fang, K. (2020). Comparison of city-level carbon footprint evaluation by applying single-and multi-regional input-output tables. Journal of environmental management, 260, 110108.
- Mowat, E.M. (2010). Making Connections: Network Theory, Embodied Mathematics, and Mathematical Understanding. PhD Thesis, Economics Department, University of Alberta.
- Ortiz-Arroyo, D. (2010). Discovering Sets of Key Players in Social Networks. Computer, Communications and Networks, 1, 27-47.
- Pouyan, Sh. (2010). Evaluation of Algorithms for Identification of Vulnerable Nodes in Scale Independent Networks. Undergraduate Thesis, Sharif University of Technology, Tehran, Iran (In Persian).
- Reggiani, A., Signoretti, S., Nijkamp, P., & A. Cento. (2009). Network Measures in Civil Air Transport: A Case Study of Lufthansa. Lecture Notes In Economics And Mathematical Systems, 613, 257-282.
- Sajedian Fard, N., Hadian, E., Samadi, A.H., & Dehghan Shabani, Z. (2019). Investigating the Effect of International Sanctions on Iran's Trade Structure: A Network Theory Approach. Journal of Economics and Modeling, 10(3), 1-28.
- Sharma, K., Shah, S., Chakrabarti A.S., & Chakraborti, A. (2017). Sectoral Co-movements in the Indian Stock Market: A Mesoscopic Network Analysis. In: Aruka Y., Kirman A. (eds) Economic Foundations for Social Complexity Science. Evolutionary Economics and Social Complexity Science, Singapore: Springer.
- Taghizadeh, R., Nazemi, A. (2018). Analysis of the ownership network in the Iranian stock market. Journal of Accounting Knowledge, (3) 9, 161-194 (In Persian).
- Troug, H., & Murray, M. (2020). Crisis determination and financial contagion: an analysis of the Hong Kong and Tokyo stock markets using an MSBVAR approach. Journal of Economic Studies, 68706, 1- 84.
- Wan, X., Zhang, Z., Zhang, C., & Meng, Q. (2020). Stock market temporal complex networks construction, robustness analysis, and systematic risk identification: a case of CSI 300 index. Complexity, 15, 1-19.
- Wang, X., & Hui, X. (2017). Mutual Information Based Analysis for the Distribution of Financial Contagion in Stock Markets. Discrete Dynamics in Nature and Society. 39, 1-149.
- Wang, T., Xiao, S., Yan, J., & Zhang, P. (2021). Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input-output tables. Physica A: Statistical Mechanics and its Applications, 581, 126196.
- Yu, M., Zhao, X., & Gao, Y. (2019). Dataset of China's non-competitive constant price input-output tables for 2007 and 2012. Data in brief, 27, 104760.
- Xu, G., & Gao, W. (2019). Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects. Sustainability, 11(5), 1402.
- Zamani, Sh., Suri, D., & Sanai Alam, M. (2010). Investigating the existence of transmission between companies' shares in Tehran Stock Exchange using a multivariate dynamic model, Economic Research, 45(4), 29-54 (In Persian).

Files

Share

How to cite

Statistics