DYNAMIC NETWORK ANALYSIS OF THE TURKISH STOCK MARKET

Yıl 2023, , 47 - 56, 31.12.2023

Hüseyin Özdemir

https://doi.org/10.18070/erciyesiibd.1310784

Öz

This article examines the dynamic network analysis of 48 firms traded on Borsa Istanbul with a market
value of over $1 billion before and during the COVID-19 pandemic. We use daily return data, and the
data spans from January 1, 2017 to May 31, 2022. The pairwise spillover effects, which are obtained by
the standard VAR model, are used to construct a directed network graph, including nodes and edges.
According to the modular clustering method, the optimal network number is three for the pre-covid
period. However, the number of optimal clusters increases to four after COVID-19 outbreaks. Although
the stocks fall into 22 different sectoral categories, the empirical evidence shows that they exhibit
discernible movements within 3 or 4 sub-groups, indicating stock behavior is driven primarily by
financial dynamics rather than sectoral influences. For instance, the firms operating in the automotive
industry fall into different cluster groups. Moreover, the empirical findings show that the relationship
of stocks is dynamic rather than static. Some important centrality measures also show that the banking
sector plays a central role in this network structure. Last but not least, the empirical findings suggest
that the correlation between stock returns rose after the COVID-19 outbreak.

Anahtar Kelimeler

Network analysis, graph theory, Turkish stock market, return spillover, COVID-19

TÜRK HİSSE SENEDİ PİYASASINA İLİŞKİN DİNAMİK AĞ ANALİZİ

Öz

Bu makale, COVID-19’un Borsa İstanbul’da işlem gören ve piyasa değeri 1 milyar doların üzerinde olan
48 firma arasındaki dinamik ağ yapısını incelemektedir. Getiri verileri günlük frekansta olup 1 Ocak 2017
tarihi ile 31 Mayıs 2022 arası dönemi arasında yer almaktadır. Standart VAR modelinden elde edilen
net ikili bağlantılılık endeksi sonuçları kullanılarak düğümler arasındaki yönlendirilmiş ağ yapısı ortaya
çıkarılmıştır. Modüler kümeleme yöntemi kullanılarak elde edilen ampirik bulgular COVID-19 salgını
öncesinde analize konu 48 firma hisse getirisinin üç alt grup altında toplandığını göstermektedir.
Pandeminin ortaya çıkmasından sonra söz konusu hisse senedi getirileri arasındaki küme sayısı dörde
çıkmaktadır. Faaliyet alanlarına göre 22 farklı sektörde bulunan söz konusu hisse senedi getirilerinin 3
ya da 4 alt grup ile hareket etmesi hisse senetlerinin sektörel etkilerden çok finansal etkiler ile hareket
ettiği gerçeğini doğrulamaktadır. Örneğin, otomotiv sektörüne ait şirketlerin her iki dönemde de
farklı gruplar altında yer aldığı görülmektedir. Salgın sonrasında birçok hisse senedinin ait olduğu
gruplar değişmiştir. Bu da hisse senetleri arasındaki ilişkilerin statik olmayıp dinamik ve değişken bir
yapıya sahip olduğunu göstermektedir. Ayrıca, bu ağ yapısı içinde, bankacılık sektörünün merkezi bir
rol oynadığına ilişkin önemli kanıtlar elde edilmiştir. Son olarak, COVID-19 salgını sonrasında hisse
senetleri arasındaki getiri korelasyonunun arttığı gözlemlenmiştir.

Anahtar Kelimeler

Ağ analizi, grafik teorisi, Türk borsası, getiri yayılması, COVID-19

Kaynakça

• Abbasian-Naghneh, S., Tehrani, R., & Tamimi, M. (2020). The Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering. Iranian Journal of Finance, 4(2).
• Anscombe, F. J., & Glynn, W. J. (1983). Distribution of the kurtosis statistic b2 for normal samples. Biometrika, 70(1). https://doi.org/10.1093/biomet/70.1.227
• Ashraf, B. N. (2020). Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance, 54. https://doi.org/10.1016/j.ribaf.2020.101249
• Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. A. R. (2021). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters, 38. https://doi.org/10.1016/j.frl.2020.101701
• Balcilar, M., Ozdemir, H., & Agan, B. (2022). Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks. Physica A: Statistical Mechanics and Its Applications, 604. https://doi.org/10.1016/j.physa.2022.127885
• Bayraktar, A. (2020). COVID 19 Pandemisinin Finansal Etkileri: BİST İmalat Sektörü Uygulaması. Journal of Turkish Studies, Volume 15 Issue 8(Volume 15 Issue 8). https://doi.org/10.7827/turkishstudies.46807
• Bonanno, G., Caldarelli, G., Lillo, F., & Mantegna, R. N. (2003). Topology of correlation-based minimal spanning trees in real and model markets. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 68(4). https://doi.org/10.1103/PhysRevE.68.046130
• Bonanno, G., Caldarelli, G., Lillo, F., Miccichè, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. European Physical Journal B, 38(2). https://doi.org/10.1140/epjb/e2004-00129-6
• Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., & Wagner, D. (2008). On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2). https://doi.org/10.1109/TKDE.2007.190689
• Chen, L., Han, Q., Qiao, Z., & Stanley, H. E. (2020). Correlation analysis and systemic risk measurement of regional, financial and global stock indices. Physica A: Statistical Mechanics and Its Applications, 542. https://doi.org/10.1016/j.physa.2019.122653
• Chu, A. M. Y., Tiwari, A., & So, M. K. P. (2020). Detecting early signals of COVID-19 global pandemic from network density. In Journal of Travel Medicine (Vol. 27, Issue 5). https://doi.org/10.1093/JTM/TAAA084
• Chu, A. M. Y., Tsang, J. T. Y., Chan, J. N. L., Tiwari, A., & So, M. K. P. (2021). Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness. In Journal of Travel Medicine (Vol. 27, Issue 8). https://doi.org/10.1093/JTM/TAAA176
• Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2). https://doi.org/10.1080/713665670
• D’agostino, R. B. (1970). Transformation to normality of the null distribution of g1. Biometrika, 57(3). https://doi.org/10.1093/biomet/57.3.679
• Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
• Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
• Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1). https://doi.org/10.1016/j.jeconom.2014.04.012
• Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4). https://doi.org/10.2307/2171846
• Esmaeilpour Moghadam, H., Mohammadi, T., Feghhi Kashani, M., & Shakeri, A. (2019). Complex networks analysis in Iran stock market: The application of centrality. Physica A: Statistical Mechanics and Its Applications, 531. https://doi.org/10.1016/j.physa.2019.121800
• Fisher, T. J., & Gallagher, C. M. (2012). New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association, 107(498). https://doi.org/10.1080/01621459.2012.688465
• Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1). https://doi.org/10.2307/3033543
• Gan, S. L., & Djauhari, M. A. (2015). New York Stock exchange performance: Evidence from the forest of multidimensional minimum spanning trees. Journal of Statistical Mechanics: Theory and Experiment, 2015(12). https://doi.org/10.1088/1742-5468/2015/12/P12005
• Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3). https://doi.org/10.1016/0165-1765(80)90024-5
• John Wei, K. C., Liu, Y. J., Yang, C. C., & Chaung, G. S. (1995). Volatility and price change spillover effects across the developed and emerging markets. Pacific-Basin Finance Journal, 3(1). https://doi.org/10.1016/0927-538X(94)00029-7
• Keleş, E. (2020). COVID-19 VE BİST-30 ENDEKSİ ÜZERİNE KISA DÖNEMLİ ETKİLERİ. M U Iktisadi ve Idari Bilimler Dergisi, 42(1). https://doi.org/10.14780/muiibd.763962
• Khoojine, A. S., & Han, D. (2019). Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information. Physica A: Statistical Mechanics and Its Applications, 523. https://doi.org/10.1016/j.physa.2019.04.128
• Koop, G., Hashem Pesaran, M., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147.
• Kumar, S., & Deo, N. (2012). Correlation and network analysis of global financial indices. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 86(2). https://doi.org/10.1103/PhysRevE.86.026101
• Lan, W., & Zhao, G. (2010). Stocks network of coal and power sectors in China stock markets. Communications in Computer and Information Science, 105 CCIS(PART 1). https://doi.org/10.1007/978-3-642-16336-4_27
• 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(6). https://doi.org/10.1142/S0218127412501428
• Long, W., Guan, L., Shen, J., Song, L., & Cui, L. (2017). A complex network for studying the transmission mechanisms in stock market. Physica A: Statistical Mechanics and Its Applications, 484. https://doi.org/10.1016/j.physa.2017.04.043
• 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. https://doi.org/10.1016/j.physa.2015.10.108
• Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B, 11(1). https://doi.org/10.1007/s100510050929
• Marti, G., Nielsen, F., Bińkowski, M., & Donnat, P. (2021). A review of two decades of correlations, hierarchies, networks and clustering in financial markets. In Signals and Communication Technology. https://doi.org/10.1007/978-3-030-65459-7_10
• 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 Education, 17(2). https://doi.org/10.3846/bme.2019.11394
• Onnela, J. P., Chakraborti, A., Kaski, K., Kertész, J., & Kanto, A. (2003). Asset Trees and Asset Graphs in Financial Markets. Physica Scripta T, 106. https://doi.org/10.1238/physica.topical.106a00048
• Onnela, J. P., Kaski, K., & Kertész, J. (2004). Clustering and information in correlation based financial networks. European Physical Journal B, 38(2). https://doi.org/10.1140/epjb/e2004-00128-7
• Özdemir, L. (2020). COVİD-19 PANDEMİSİNİN BİST SEKTÖR ENDEKSLERİ ÜZERİNE ASİMETRİK ETKİSİ. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 5(3). https://doi.org/10.29106/fesa.797658
• Papana, A., Kyrtsou, C., Kugiumtzis, D., & Diks, C. (2017). Financial networks based on Granger causality: A case study. Physica A: Statistical Mechanics and Its Applications, 482. https://doi.org/10.1016/j.physa.2017.04.046
• Pesaran, H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
• Rakib, M. I., Hossain, M. J., & Nobi, A. (2022). Feature ranking and network analysis of global financial indices. PLoS ONE, 17(6 June). https://doi.org/10.1371/journal.pone.0269483
• Schuenemann, J. H., Ribberink, N., & Katenka, N. (2020). Japanese and Chinese Stock Market Behaviour in Comparison – an analysis of dynamic networks. Asia Pacific Management Review, 25(2). https://doi.org/10.1016/j.apmrv.2019.10.002
• Şenol, Z., & Otçeken, G. (2021). COVID-19’UN BİST SEKTÖRLERİNE ETKİSİ. Finans Ekonomi ve Sosyal Araştırmalar Dergisi. https://doi.org/10.29106/fesa.984219
• So, M. K. P., Tiwari, A., Chu, A. M. Y., Tsang, J. T. Y., & Chan, J. N. L. (2020). Visualizing COVID-19 pandemic risk through network connectedness. In International Journal of Infectious Diseases (Vol. 96). https://doi.org/10.1016/j.ijid.2020.05.011
• Şükrüoğlu, D. (2022). Effects of Covid-19 on the BIST 100 network structure. Applied Economics, 54(52). https://doi.org/10.1080/00036846.2022.2108540
• Sun, X., Wang, J., Yao, Y., Li, J., & Li, J. (2020). Spillovers among sovereign CDS, stock and commodity markets: A correlation network perspective. International Review of Financial Analysis, 68, 101271. https://doi.org/10.1016/j.irfa.2018.10.008
• Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4). https://doi.org/10.1016/j.jempfin.2010.04.008
• Tumminello, M., Lillo, F., & Mantegna, R. N. (2010). Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior and Organization, 75(1). https://doi.org/10.1016/j.jebo.2010.01.004
• Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2020.101528
• Zheng, Z., Sakurai, N., Fujiwara, T., Yoshizawa, K., & Yamasaki, K. (2012). Correlation and hierarchies in financial markets. Artificial Life and Robotics, 17(1). https://doi.org/10.1007/s10015-012-0035-3