Identifying the Topology of the Iranian Stock Market Network and Ranking its Groups

Authors

  • Samad Sedaghati Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Author
  • Ruhollah Farhadi Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Author
  • Mir Feyz Fallahshams Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Author

DOI:

https://doi.org/10.61841/ws6t2s26

Keywords:

complex network, graph theory, centrality indexes, stock market network, stock market topology

Abstract

 The stock market is a complex financial system with heterogeneous members which produces huge amounts of data. It is clear that analyzing this huge data and inferring practical results creates a significant competitive advantage for its participants. One method of analyzing financial market data expanded significantly after the global financial crisis is complex network-based analysis that considers the structure of interdependencies of a system's members. Therefore, the current study analyzes the Iranian stock market using the graph theory in mathematics. First, the correlation network of stock market groups is constructed in three time scales of daily, seasonal and annual, and then their topology will be compared. In the next stage, using the centrality indexes in the graph theory, the importance of each market group is calculated and the groups are ranked in the network. The results of this study have significant implications for market participants and regulators for making investment decisions, regulating and controlling risk 

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References

[1] Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks.

American Economic Review, 105(2), 564-608.

[2] Allen, F., & Babus, A. (2009). Networks in finance. The network challenge: strategy, profit, and risk in an

interlinked world, 367.

[3] Allen, F., & Gale, D. (2000). Financial contagion. Journal of political economy, 108(1), 1-33.

[4] Battiston, S., Caldarelli, G., May, R. M., Roukny, T., & Stiglitz, J. E. (2016). The price of complexity in

financial networks. Proceedings of the National Academy of Sciences, 113(36), 10031-10036.

[5] Bech, M. L., & Atalay, E. (2010). The topology of the federal funds market. Physica A: Statistical Mechanics

and its Applications, 389(22), 5223-5246.

[6] Bech, M. L., Chapman, J. T., & Garratt, R. J. (2010). Which bank is the “central” bank?. Journal of monetary

economics, 57(3), 352-363.

[7] Berndsen, R. J., Leon, C., & Renneboog, L. (2018). Financial stability in networks of financial institutions and

market infrastructures. Journal of Financial Stability, 35, 120-135.

[8] Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and

systemic risk in the finance and insurance sectors. Journal of financial economics, 104(3), 535-559.

[9] Chi, K. T., Liu, J., & Lau, F. C. (2010). A network perspective of the stock market. Journal of Empirical

Finance, 17(4), 659-667.

[10] Elliott, M., Golub, B., & Jackson, M. O. (2014). Financial networks and contagion. American Economic

Review, 104(10), 3115-53.

[11] Gabrieli, S. (2011). The microstructure of the money market before and after the financial crisis: a network

perspective. CEIS Tor Vergata Research Paper Series, 9(1), 181.

[12] Georg, C. P. (2013). The effect of the interbank network structure on contagion and common shocks. Journal

of Banking & Finance, 37(7), 2216-2228.

[13] Ismail Pourmoghadam, Hadi., Mohammadi, Teymour., Feqhi Kashani, Mohammad. & Shakeri, Abbas. (2019).

Section growth and centrality in the Iranian stock market: Application of complex network analysis. Journal of

Economic Research and Policies. 27 (90).

[14] Jackson, M. O. (2010). Social and economic networks. Princeton university press.

[15] Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY:

Springer.

[16] Krehbiel, T. C. (2004). Correlation coefficient rule of thumb. Decision Sciences Journal of Innovative

Education, 2(1), 97-100.

[17] Lee, T. K., Cho, J. H., Kwon, D. S., & Sohn, S. Y. (2019). Global stock market investment strategies based on

financial network indicators using machine learning techniques. Expert Systems with Applications, 117, 228-

242.

[18] Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008

financial crisis. Physica A: Statistical Mechanics and its Applications, 445, 35-47.

[19] Naimzada, A. K., Stefani, S., & Torriero, A. (Eds.). (2008). Networks, topology and dynamics: Theory and

applications to economics and social systems (Vol. 613). Springer Science & Business Media.

[20] Rogers, L. C., & Veraart, L. A. (2013). Failure and rescue in an interbank network. Management Science,

59(4), 882-898.

[21] Roukny, T., Battiston, S., & Stiglitz, J. E. (2018). Interconnectedness as a source of uncertainty in systemic

risk. Journal of Financial Stability, 35, 93-106.

[22] Sharifi Samani, Farshad. (2016). Topological features of the stock network in the Tehran Stock Exchange

(Case study of BARJAM effect). Master Thesis, Yazd University of Science and Art.

[23] Xu, R., Wong, W-K, Chen, G., & Huang, Sh. (2017). Topological Characteristics of the Hong Kong Stock

Market: A Test-based P-threshold Approach to Understanding Network Complexity, Scientific Reports volume

7, Article number: 41379.

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Published

28.02.2021

How to Cite

Sedaghati, S., Farhadi, R., & Feyz Fallahshams, M. (2021). Identifying the Topology of the Iranian Stock Market Network and Ranking its Groups. International Journal of Psychosocial Rehabilitation, 25(1), 466-482. https://doi.org/10.61841/ws6t2s26