The Effect of JCPOA on the Network Behavior Analysis of Tehran Stock Exchange Indexes
محورهای موضوعی : Statistical Methods in Financial Management
Salman Abbasian-Naghneh
1
(
Department of Financial Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
)
Reza Tehrani
2
(
Faculty of Management, University of Tehran, Tehran, Iran|Department of Financial Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
)
Mohammad Tamimi
3
(
Department of Accounting, Dezful Branch, Islamic Azad University, Dezful, Iran
)
کلید واژه: JCPOA, Correlation distribution, MST, Hierarchical Clustering, Network Analysis,
چکیده مقاله :
The purpose of this paper is investigating the effect of JCPOA on the network behavior analysis of Tehran Stock Exchange indexes using the minimum spanning tree (MST) and hierarchical clustering. By simplifying a complex system, network analysis allows for the extraction of important and essential information from that system. In this paper, using network analysis the simultaneous behavior of 38 industry indexes in Tehran Stock Exchange in manufacturing, service and invest-ment sectors during 2012-2017 was investigated. These analysis included identi-fying the main indexes in the direction of moving other indexes using the MST, providing a classification using hierarchical clustering for the behavioral similarity of the indexes as well as examining the degree of integration (behavioral similarity) of market indexes over time. The results showed that investment, automobile, industry and medicine indexes in the research period had a major role in guiding other indexes and indexes can be classified into six groups in terms of behavioral similarity. The market has also been moving toward integration of indexes since early 2015 and beginning the executive steps of Joint Comprehensive Plan of Action (JCPOA). This reflects the investors' hope for the promotion of all indexes.
[1]Bonanno G., Caldarelli G., Lillo F., Mantegna R. N., Topology of correlation-based minimal spanning trees in real and model markets, Physical Review E, 2003, 68(4), P. 046130, Doi: 10.1103/PhysRevE.68.046130.
[2]Coletti P., Comparing minimum spanning trees of the Italian stock market using returns and volumes, Physica a: Statistical Mechanics and its Applications, 2016, 463, P. 246-61, Doi: 10.1016/j.physa.2016.07.029.
[3]Yang C., Zhu X., Li Q., Chen Y., Deng Q., Research on the evolution of stock correlation based on maximal spanning trees, Physica A: Statistical Mechanics and its Applications, 2014, 415, P. 1-18, Doi: 10.1016/j.physa.2014.07.069.
[4]Ji Q., Fan Y., Evolution of the world crude oil market integration: A graph theory analysis, Energy Economics, 2016, 53, P. 90-100, Doi: 10.1016/j.eneco.2014.12.003.
[5]Heimo T., Saramäki J., Onnela J.-P., Kaski K., Spectral and network methods in the analysis of correlation matrices of stock returns, Physica A: Statistical Mechanics and its Applications, 2007, 383(1), P. 147-51, Doi: 10.1016/j.physa.2007.04.124.
[6]Onnela J.-P., Chakraborti A., Kaski K., Kertesz J., Kanto A., Dynamics of market correlations: Taxonomy and portfolio analysis, Physical Review E, 2003, 68(5), P. 056110, Doi: 10.1103/PhysRevE.68.056110.
[7]Gan S. L., Djauhari M. A., New York Stock Exchange performance: evidence from the forest of multidimensional minimum spanning trees, Journal of Statistical Mechanics: Theory and Experiment, 2015, 2015(12), P. P12005, Doi: 10.1088/1742-5468/2015/12/P12005.
[8]Tumminello M., Aste T., Di Matteo T., Mantegna R. N., A tool for filtering information in complex systems, Proceedings of the National Academy of Sciences, 2005, 102(30), P. 10421-6, Doi: 10.1073/pnas.0500298102.
[9]Huang W.-Q., Zhuang X.-T., Yao S., A network analysis of the Chinese stock market, Physica A: Statistical Mechanics and its Applications, 2009, 388(14), P. 2956-64, Doi: 10.1016/j.physa.2009.03.028.
[10]Tabak B. M., Serra T. R., Cajueiro D. O., Topological properties of stock market networks: The case of Brazil, Physica A: Statistical Mechanics and its Applications, 2010, 389(16), P. 3240-9, Doi: 10.1016/j.physa.2010.04.002.
[11]Wiliński M., Sienkiewicz A., Gubiec T., Kutner R., Struzik Z., Structural and topological phase transitions on the German Stock Exchange, Physica A: Statistical Mechanics and its Applications, 2013, 392(23), P. 5963-73, Doi: 10.1016/j.physa.2013.07.064.
[12]Nobi A., Maeng S. E., Ha G. G., Lee J. W., Effects of global financial crisis on network structure in a local stock market, Physica A: Statistical Mechanics and its Applications, 2014, 407, P. 135-43, Doi: 10.1016/j.physa.2014.03.083.
[13]Coronnello C., Tumminello M., Lillo F., Micciche S., Mantegna R. N., Sector identification in a set of stock return time series traded at the London Stock Exchange, Acta Phys Polon B 2005, P. 2653–79, Doi: 10.1117/12.729619.
[14]Majapa M., Gossel S. J., Topology of the South African stock market network across the 2008 financial crisis, Physica A: Statistical Mechanics and its Applications, 2016, 445, P. 35-47, Doi: 10.1016/j.physa.2015.10.108.
[15]Gałązka M., Characteristics of the polish stock market correlations, International review of financial analysis, 2011, 20(1), P. 1-5, Doi: 10.1016/j.irfa.2010.11.002.
[16]Kantar E., Deviren B., Keskin M., Hierarchical structure of Turkey’s foreign trade, Physica A: Statistical Mechanics and its Applications, 2011, 390(20), P. 3454-76, Doi: 10.1016/j.physa.2011.05.004.
[17]You T., Fiedor P., Hołda A., Network analysis of the Shanghai stock exchange based on partial mutual information, Journal of Risk and Financial Management, 2015, 8(2), P. 266-84, Doi: 10.3390/jrfm8020266.
[18]Eom C., Oh G., Jung W.-S., Jeong H., Kim S., Topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series, Physica A: Statistical Mechanics and its Applications, 2009, 388(6), P. 900-6, Doi: 10.1016/j.physa.2008.12.006.
[19]Rocach L., Maimon O. Data mining and knowledge discovery handbook, Springer, Boston, MA, 2005.
[20]Mantegna R. N., Hierarchical structure in financial markets, The European Physical Journal B-Condensed Matter and Complex Systems, 1999, 11(1), P. 193-7, Doi: 10.1007/s100510050929.
[21]Tumminello M., Lillo F., Mantegna R. N., Correlation, hierarchies, and networks in financial markets, Journal of Economic Behavior & Organization, 2010, 75(1), P. 40-58, Doi: 10.1016/j.jebo.2010.01.004.
[22]Bhattacharjee B., Shafi M., Acharjee A., Network mining based elucidation of the dynamics of cross-market clustering and connectedness in Asian region: An MST and hierarchical clustering approach, Journal of King Saud University-Computer and Information Sciences, 2017, 31(2), P. 218-28, Doi: 10.1016/j.jksuci.2017.11.002.
[23]Bollobas B. Graph Theory: An Introductory Course New York, Springer-Verlag, 1979.
[24]Dorogovtsev S. N., Mendes J. F. Evolution of networks: From biological nets to the Internet and WWW, Oxford, Oxford University Press, 2013.
[25]Dabrowski J., Pułka A., TH Cormen, CE Leiserson, and RL Rivest introduction to Algorithms. Mcgraw-hill, Mit Press, 1990. t. H. Cormen, CE Leiserson, and RL Rivest introduction to Algorithms. Mcgraw-hill, Mit Press, 1990. Discrete Approach to Pwl Analog Modeling in Vhdl Environment, Analog Integrated Circuits & Signal Processing, 1998, 16(2), P. 91-9.
[26] Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8(8), P. 1-7, Doi: 10.17485/ijst/2015/v8iS8/71505
[27]Hughes B., Trees and ultrametric spaces: a categorical equivalence, Advances in Mathematics, 2004, 189(1), P. 148-91, Doi: 10.1016/j.aim.2003.11.008.
[28] Parsa, B., Sarraf, F. Financial Statement Comparability and the Expected Crash Risk of Stock Prices. Advances in Mathematical Finance and Applications, 2018, 3(3), P. 77-93. Doi: 10.22034/amfa.2018.544951
[29] Salehi, A., Baharipour, A., Mohammadi, S. The Impact of Institutional Ownership on the Relationship between Tax and Capital Structure. Advances in Mathematical Finance and Applications, 2016, 1(2), P. 57-67. Doi: 10.22034/amfa.2016.527820
[30]Sieczka P., Hołyst J. A., Correlations in commodity markets, Physica A: Statistical Mechanics and its Applications, 2009, 388(8), P. 1621-30, Doi: 10.1016/j.physa.2009.01.004.
[31]Sabidussi G., The centrality index of a graph, Psychometrika, 1966, 31(4), P. 581-603, Doi: 10.1007/BF02289527.
[32] Zalaghi, H., Godini, M., mansouri, K. The Moderating Role of Firms characteristics on the Relationship between Working Capital Management and Financial Performance. Advances in Mathematical Finance and Applications, 2019, 4(1), P. 71-88. Doi: 10.22034/amfa.2019.581878.1158
