Network analysis of Pakistan stock market during the turbulence of economic crisis
Abstract
Purpose – the purpose of this study is to analyse the impact of the recent economic crisis on the network topology structure of Pakistan stock market. Since stock market is considered a core financial market for the development of an economy, it is often used as benchmark to measure a country`s progress. Policymakers often forecast tendency of share prices, that is dependent on several foreign and local macroeconomic factors. Therefore, the aim of this study is to investigate how rising inflation, higher interest rates, and trade and budgetary deficits affect the network structure of blue-chip 96 companies listed on the Karachi stock exchange (KSE-100) index of Pakistan stock market.
Research methodology – this study follows the methodology proposed by Mantegna and Stanley and uses cross-correlation in the daily closing price of KSE 100 Index companies to compute Minimum spanning tree (MST) structures. Additionally, we also apply time-varying topological property of average tree length to extract dynamic features of the MST networks.
Findings – we construct eight monthly MSTs that show the instability of the network structure and significant differences in the topological characteristics due to economic crisis of Pakistan. Furthermore, the time-varying topological property of average tree length reveals contraction of the networks due to tight correlation among stocks.
Research limitations – this study focuses on correlation-based network construction of MST. The scope of the study can be widened by constructing partial correlation-based MSTs and comparison of different networks structures accordingly.
Practical implications – the network properties and findings of this paper will help policymakers and regulators in setting right policies, regulatory framework, and risk management for the stock market.
Originality/Value – no previous studies have performed MST based network analysis examining macroeconomic events. Therefore, we fill the research gap and thoroughly analyse structural change and dynamics of Pakistan stock market during the turbulence of current economic crisis of Pakistan.
Keyword : stock market, minimum spanning tree, network topology, macroeconomic indicators, crisis
How to Cite
Memon, B. A., Yao, H., Aslam, F., & Tahir, R. (2019). Network analysis of Pakistan stock market during the turbulence of economic crisis. Business, Management and Economics Engineering, 17(2), 269-285. https://doi.org/10.3846/bme.2019.11394
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Anagnostidis, P., Varsakelis, C., & Emmanouilides, C. J. (2016). Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone. Physica A: Statistical Mechanics and its Applications, 447, 116-128. https://doi.org/10.1016/j.physa.2015.12.017
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512. https://doi.org/10.1126/science.286.5439.509
Boginski, V., Butenko, S., & Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431-443. https://doi.org/10.1016/j.csda.2004.02.004
Brida, J. G., Matesanz, D., & Seijas, M. N. (2016). Network analysis of returns and volume trading in stock markets: The Euro Stoxx case. Physica A: Statistical Mechanics and its Applications, 444, 751764. https://doi.org/10.1016/j.physa.2015.10.078
Butt, B. Z., ur Rehman, K., Khan, M. A., & Safwan, N. (2010). Do economic factors influence stock returns? A firm and industry level analysis. African Journal of Business Management, 4(5), 583-593.
Creamer, G. G., Ren, Y., & Nickerson, J. V. (2013, September 8-14). Impact of dynamic corporate news networks on asset return and volatility. Paper presented at The 2013 International Conference on Social Computing. https://doi.org/10.1109/SocialCom.2013.121
Darrat, A. F., & Mukherjee, T. K. (1986). The behavior of the stock market in a developing economy. Economics Letters, 22(2), 273-278. https://doi.org/10.1016/0165-1765(86)90246-6
Dias, J. (2013). Spanning trees and the Eurozone crisis. Physica A: Statistical Mechanics and its Applications, 392(23), 5974-5984. https://doi.org/10.1016/j.physa.2013.08.001
Dimitrios, K., & Vasileios, O. (2015). A network analysis of the Greek Stock Market. Procedia Economics and Finance, 33, 340-349. https://doi.org/10.1016/S2212-5671(15)01718-9
Gałązka, M. (2011). Characteristics of the Polish Stock Market correlations. International Review of Financial Analysis, 20(1), 1-5. https://doi.org/10.1016/j.irfa.2010.11.002
Garber, P. M. (1990). Famous First Bubbles. Journal of Economic Perspectives, 4(2), 35-54.
https://doi.org/10.1257/jep.4.2.35
Gilmore, C. G., Lucey, B. M., & Boscia, M. (2008). An ever-closer union? Examining the evolution of linkages of European equity markets via minimum spanning trees. Physica A: Statistical Mechanics and its Applications, 387(25), 6319-6329. https://doi.org/10.1016/j.physa.2008.07.012
Horta, P., Lagoa, S., & Martins, L. (2014). The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion. International Review of Financial Analysis, 35, 140-153. https://doi.org/10.1016/j.irfa.2014.08.002
Husain, F., & Mahmood, T. (2001). The Stock Market and the Economy in Pakistan. The Pakistan Development Review, 40(2), 107-114. https://doi.org/10.30541/v40i2pp.107-114
Jang, W., Lee, J., & Chang, W. (2011). Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree. Physica A: Statistical Mechanics and its Applications, 390(4), 707-718. https://doi.org/10.1016/j.physa.2010.10.028
Jin, X. (2016). The impact of 2008 financial crisis on the efficiency and contagion of Asian stock markets: A Hurst exponent approach. Finance Research Letters, 17, 167-175. https://doi.org/10.1016/j.frl.2016.03.004
Kazemilari, M., Mohamadi, A., Mardani, A., & Streimikis, J. (2019). Network topology of renewable energy companies: minimal spanning tree and sub-dominant ultrametric for the American stock. Technological and Economic Development of Economy, 25(2), 168-187. https://doi.org/10.3846/tede.2019.7686
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem.
Proceedings of the American Mathematical Society, 7(1), 48-50. https://doi.org/10.1090/S0002-9939-1956-0078686-7
Kwon, C. S., & Shin, T. S. (1999). Cointegration and causality between macroeconomic variables and stock market returns. Global Finance Journal, 10(1), 71-81. https://doi.org/10.1016/S1044-0283(99)00006-X
Lee, J., & Nobi, A. (2018). Structural transformation of minimal spanning trees in world commodity market. Acta Physica Polonica A, 133(6), 1414-1416. https://doi.org/10.12693/APhysPolA.133.1414
Liu, X. F., & Tse, C. K. (2012). A complex network perspective of world stock markets: Synchronization and volatility. International Journal of Bifurcation and Chaos, 22(06), 1250142. https://doi.org/10.1142/S0218127412501428
Maeng, S. E., Choi, H. W., & Lee, J. W. (2012). Complex networks and minimal spanning trees in international trade network. International Journal of Modern Physics: Conference Series, 16, 51-60. https://doi.org/10.1142/S2010194512007775
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. https://doi.org/10.1016/j.physa.2015.10.108
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B – Condensed Matter and Complex Systems, 11(1), 193-197. https://doi.org/10.1007/s100510050929
Mantegna, R. N., & Stanley, H. E. (2000). An introduction to econophysics: Correlations and complexity in finance (Vol. 9). Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511755767
Memon, B. A., & Yao, H. (2019). Structural change and dynamics of Pakistan Stock Market during crisis: A complex network perspective. Entropy, 21(3), 248. https://doi.org/10.3390/e21030248
Namaki, A., Shirazi, A. H., Raei, R., & Jafari, G. R. (2011). Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390(21), 3835-3841. https://doi.org/10.1016/j.physa.2011.06.033
Nishat, M., & Shaheen, R. (2004). Macroeconomic factors and Pakistani equity market. The Pakistan Development Review, 43(4), 619-637. https://doi.org/10.30541/v43i4IIpp.619-637
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. https://doi.org/10.1016/j.physleta.2014.07.009
Nobi, A., Maeng, S. E., Ha, G. G., & Lee, J. W. (2015). Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis. Journal of the Korean Physical Society, 66(8), 1153-1159. https://doi.org/10.3938/jkps.66.1153
Omran, M., & Pointon, J. (2001). Does the inflation rate affect the performance of the stock market? The case of Egypt. Emerging Markets Review, 2(3), 263-279. https://doi.org/10.1016/S1566-0141(01)00020-6
Onnela, J.-P., Chakraborti, A., Kaski, K., & Kertiész, J. (2002). Dynamic asset trees and portfolio analysis. The European Physical Journal B, 30(3), 285-288. https://doi.org/10.1140/epjb/e2002-00380-9
Radhakrishnan, S., Duvvuru, A., Sultornsanee, S., & Kamarthi, S. (2016). Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations. Physica A: Statistical Mechanics and its Applications, 444, 259-270. https://doi.org/10.1016/j.physa.2015.09.070
Sajid Nazir, M., Younus, H., Kaleem, A., & Anwar, Z. (2014). Impact of political events on stock market returns: empirical evidence from Pakistan. Journal of Economic and Administrative Sciences, 30(1), 60-78. https://doi.org/10.1108/JEAS-03-2013-0011
Shahbaz, M. (2013). Linkages between inflation, economic growth and terrorism in Pakistan. Economic Modelling, 32, 496-506. https://doi.org/10.1016/j.econmod.2013.02.014
Sohail, N., & Zakir, H. (2010). Macroeconomic determinants of stock returns in Pakistan: The case of Karachi Stock Exchange. Journal of Advanced Studies in Finance, 1(2), 181-187.
Song, J. W., Ko, B., & Chang, W. (2018). Analyzing systemic risk using non-linear marginal expected shortfall and its minimum spanning tree. Physica A: Statistical Mechanics and its Applications, 491, 289-304. https://doi.org/10.1016/j.physa.2017.08.076
Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of Brazil. Physica A: Statistical Mechanics and its Applications, 389(16), 3240-3249. https://doi.org/10.1016/j.physa.2010.04.002
Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667. https://doi.org/10.1016/j.jempfin.2010.04.008
Tumminello, M., Aste, T., Di Matteo, T., & N Mantegna, R. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421-10426. https://doi.org/10.1073/pnas.0500298102
Wang, G.-J., Xie, C., & Stanley, H. E. (2018). Correlation structure and evolution of world stock markets: Evidence from pearson and partial correlation-based networks. Computational Economics, 51(3), 607-635. https://doi.org/10.1007/s10614-016-9627-7
Wilinski, M., Szewczak, B., Gubiec, T., Kutner, R., & Struzik, Z. R. (2014). Nucleation, condensation and lambda-transition on a real-life stock market (Papers 1311.5753). arXiv.org.
Yang, R., Li, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A: Statistical Mechanics and its Applications, 411, 12-20. https://doi.org/10.1016/j.physa.2014.05.072
Yao, H., & Memon, B. A. (2019). Network topology of FTSE 100 Index companies: From the perspective of Brexit. Physica A: Statistical Mechanics and its Applications, 523, 1248-1262. https://doi.org/10.1016/j.physa.2019.04.106
Yin, K., Liu, Z., & Liu, P. (2017). Trend analysis of global stock market linkage based on a dynamic conditional correlation network. Journal of Business Economics and Management, 18(4), 779-800. https://doi.org/10.3846/16111699.2017.1341849
Yusoff, W. S., Salleh, M. F. M., Ahmad, A., & Idris, F. (2015). Short-run Political events and stock market reactions: Evidence from companies connected to Malaysian bi-power business-political elite. Procedia – Social and Behavioral Sciences, 211, 421-428. https://doi.org/10.1016/j.sbspro.2015.11.055
Ali, I., Rehman, K. U., Yilmaz, A. K., Khan, M. A., & Afzal, H. (2010). Causal relationship between macro-economic indicators and stock exchange prices in Pakistan. African Journal of Business Management, 4(3), 312-319.
Anagnostidis, P., Varsakelis, C., & Emmanouilides, C. J. (2016). Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone. Physica A: Statistical Mechanics and its Applications, 447, 116-128. https://doi.org/10.1016/j.physa.2015.12.017
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512. https://doi.org/10.1126/science.286.5439.509
Boginski, V., Butenko, S., & Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431-443. https://doi.org/10.1016/j.csda.2004.02.004
Brida, J. G., Matesanz, D., & Seijas, M. N. (2016). Network analysis of returns and volume trading in stock markets: The Euro Stoxx case. Physica A: Statistical Mechanics and its Applications, 444, 751764. https://doi.org/10.1016/j.physa.2015.10.078
Butt, B. Z., ur Rehman, K., Khan, M. A., & Safwan, N. (2010). Do economic factors influence stock returns? A firm and industry level analysis. African Journal of Business Management, 4(5), 583-593.
Creamer, G. G., Ren, Y., & Nickerson, J. V. (2013, September 8-14). Impact of dynamic corporate news networks on asset return and volatility. Paper presented at The 2013 International Conference on Social Computing. https://doi.org/10.1109/SocialCom.2013.121
Darrat, A. F., & Mukherjee, T. K. (1986). The behavior of the stock market in a developing economy. Economics Letters, 22(2), 273-278. https://doi.org/10.1016/0165-1765(86)90246-6
Dias, J. (2013). Spanning trees and the Eurozone crisis. Physica A: Statistical Mechanics and its Applications, 392(23), 5974-5984. https://doi.org/10.1016/j.physa.2013.08.001
Dimitrios, K., & Vasileios, O. (2015). A network analysis of the Greek Stock Market. Procedia Economics and Finance, 33, 340-349. https://doi.org/10.1016/S2212-5671(15)01718-9
Gałązka, M. (2011). Characteristics of the Polish Stock Market correlations. International Review of Financial Analysis, 20(1), 1-5. https://doi.org/10.1016/j.irfa.2010.11.002
Garber, P. M. (1990). Famous First Bubbles. Journal of Economic Perspectives, 4(2), 35-54.
https://doi.org/10.1257/jep.4.2.35
Gilmore, C. G., Lucey, B. M., & Boscia, M. (2008). An ever-closer union? Examining the evolution of linkages of European equity markets via minimum spanning trees. Physica A: Statistical Mechanics and its Applications, 387(25), 6319-6329. https://doi.org/10.1016/j.physa.2008.07.012
Horta, P., Lagoa, S., & Martins, L. (2014). The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion. International Review of Financial Analysis, 35, 140-153. https://doi.org/10.1016/j.irfa.2014.08.002
Husain, F., & Mahmood, T. (2001). The Stock Market and the Economy in Pakistan. The Pakistan Development Review, 40(2), 107-114. https://doi.org/10.30541/v40i2pp.107-114
Jang, W., Lee, J., & Chang, W. (2011). Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree. Physica A: Statistical Mechanics and its Applications, 390(4), 707-718. https://doi.org/10.1016/j.physa.2010.10.028
Jin, X. (2016). The impact of 2008 financial crisis on the efficiency and contagion of Asian stock markets: A Hurst exponent approach. Finance Research Letters, 17, 167-175. https://doi.org/10.1016/j.frl.2016.03.004
Kazemilari, M., Mohamadi, A., Mardani, A., & Streimikis, J. (2019). Network topology of renewable energy companies: minimal spanning tree and sub-dominant ultrametric for the American stock. Technological and Economic Development of Economy, 25(2), 168-187. https://doi.org/10.3846/tede.2019.7686
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem.
Proceedings of the American Mathematical Society, 7(1), 48-50. https://doi.org/10.1090/S0002-9939-1956-0078686-7
Kwon, C. S., & Shin, T. S. (1999). Cointegration and causality between macroeconomic variables and stock market returns. Global Finance Journal, 10(1), 71-81. https://doi.org/10.1016/S1044-0283(99)00006-X
Lee, J., & Nobi, A. (2018). Structural transformation of minimal spanning trees in world commodity market. Acta Physica Polonica A, 133(6), 1414-1416. https://doi.org/10.12693/APhysPolA.133.1414
Liu, X. F., & Tse, C. K. (2012). A complex network perspective of world stock markets: Synchronization and volatility. International Journal of Bifurcation and Chaos, 22(06), 1250142. https://doi.org/10.1142/S0218127412501428
Maeng, S. E., Choi, H. W., & Lee, J. W. (2012). Complex networks and minimal spanning trees in international trade network. International Journal of Modern Physics: Conference Series, 16, 51-60. https://doi.org/10.1142/S2010194512007775
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. https://doi.org/10.1016/j.physa.2015.10.108
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B – Condensed Matter and Complex Systems, 11(1), 193-197. https://doi.org/10.1007/s100510050929
Mantegna, R. N., & Stanley, H. E. (2000). An introduction to econophysics: Correlations and complexity in finance (Vol. 9). Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511755767
Memon, B. A., & Yao, H. (2019). Structural change and dynamics of Pakistan Stock Market during crisis: A complex network perspective. Entropy, 21(3), 248. https://doi.org/10.3390/e21030248
Namaki, A., Shirazi, A. H., Raei, R., & Jafari, G. R. (2011). Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390(21), 3835-3841. https://doi.org/10.1016/j.physa.2011.06.033
Nishat, M., & Shaheen, R. (2004). Macroeconomic factors and Pakistani equity market. The Pakistan Development Review, 43(4), 619-637. https://doi.org/10.30541/v43i4IIpp.619-637
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. https://doi.org/10.1016/j.physleta.2014.07.009
Nobi, A., Maeng, S. E., Ha, G. G., & Lee, J. W. (2015). Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis. Journal of the Korean Physical Society, 66(8), 1153-1159. https://doi.org/10.3938/jkps.66.1153
Omran, M., & Pointon, J. (2001). Does the inflation rate affect the performance of the stock market? The case of Egypt. Emerging Markets Review, 2(3), 263-279. https://doi.org/10.1016/S1566-0141(01)00020-6
Onnela, J.-P., Chakraborti, A., Kaski, K., & Kertiész, J. (2002). Dynamic asset trees and portfolio analysis. The European Physical Journal B, 30(3), 285-288. https://doi.org/10.1140/epjb/e2002-00380-9
Radhakrishnan, S., Duvvuru, A., Sultornsanee, S., & Kamarthi, S. (2016). Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations. Physica A: Statistical Mechanics and its Applications, 444, 259-270. https://doi.org/10.1016/j.physa.2015.09.070
Sajid Nazir, M., Younus, H., Kaleem, A., & Anwar, Z. (2014). Impact of political events on stock market returns: empirical evidence from Pakistan. Journal of Economic and Administrative Sciences, 30(1), 60-78. https://doi.org/10.1108/JEAS-03-2013-0011
Shahbaz, M. (2013). Linkages between inflation, economic growth and terrorism in Pakistan. Economic Modelling, 32, 496-506. https://doi.org/10.1016/j.econmod.2013.02.014
Sohail, N., & Zakir, H. (2010). Macroeconomic determinants of stock returns in Pakistan: The case of Karachi Stock Exchange. Journal of Advanced Studies in Finance, 1(2), 181-187.
Song, J. W., Ko, B., & Chang, W. (2018). Analyzing systemic risk using non-linear marginal expected shortfall and its minimum spanning tree. Physica A: Statistical Mechanics and its Applications, 491, 289-304. https://doi.org/10.1016/j.physa.2017.08.076
Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of Brazil. Physica A: Statistical Mechanics and its Applications, 389(16), 3240-3249. https://doi.org/10.1016/j.physa.2010.04.002
Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667. https://doi.org/10.1016/j.jempfin.2010.04.008
Tumminello, M., Aste, T., Di Matteo, T., & N Mantegna, R. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421-10426. https://doi.org/10.1073/pnas.0500298102
Wang, G.-J., Xie, C., & Stanley, H. E. (2018). Correlation structure and evolution of world stock markets: Evidence from pearson and partial correlation-based networks. Computational Economics, 51(3), 607-635. https://doi.org/10.1007/s10614-016-9627-7
Wilinski, M., Szewczak, B., Gubiec, T., Kutner, R., & Struzik, Z. R. (2014). Nucleation, condensation and lambda-transition on a real-life stock market (Papers 1311.5753). arXiv.org.
Yang, R., Li, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A: Statistical Mechanics and its Applications, 411, 12-20. https://doi.org/10.1016/j.physa.2014.05.072
Yao, H., & Memon, B. A. (2019). Network topology of FTSE 100 Index companies: From the perspective of Brexit. Physica A: Statistical Mechanics and its Applications, 523, 1248-1262. https://doi.org/10.1016/j.physa.2019.04.106
Yin, K., Liu, Z., & Liu, P. (2017). Trend analysis of global stock market linkage based on a dynamic conditional correlation network. Journal of Business Economics and Management, 18(4), 779-800. https://doi.org/10.3846/16111699.2017.1341849
Yusoff, W. S., Salleh, M. F. M., Ahmad, A., & Idris, F. (2015). Short-run Political events and stock market reactions: Evidence from companies connected to Malaysian bi-power business-political elite. Procedia – Social and Behavioral Sciences, 211, 421-428. https://doi.org/10.1016/j.sbspro.2015.11.055