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Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods

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  • Kantar, Ersin
  • Keskin, Mustafa
  • Deviren, Bayram

Abstract

We have analyzed the topology of 50 important Turkish companies for the period 2006–2010 using the concept of hierarchical methods (the minimal spanning tree (MST) and hierarchical tree (HT)). We investigated the statistical reliability of links between companies in the MST by using the bootstrap technique. We also used the average linkage cluster analysis (ALCA) technique to observe the cluster structures much better. The MST and HT are known as useful tools to perceive and detect global structure, taxonomy, and hierarchy in financial data. We obtained four clusters of companies according to their proximity. We also observed that the Banks and Holdings cluster always forms in the centre of the MSTs for the periods 2006–2007, 2008, and 2009–2010. The clusters match nicely with their common production activities or their strong interrelationship. The effects of the Automobile sector increased after the global financial crisis due to the temporary incentives provided by the Turkish government. We find that Turkish companies were not very affected by the global financial crisis.

Suggested Citation

  • Kantar, Ersin & Keskin, Mustafa & Deviren, Bayram, 2012. "Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2342-2352.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:7:p:2342-2352
    DOI: 10.1016/j.physa.2011.12.014
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    Cited by:

    1. Baumöhl, Eduard & Kočenda, Evžen & Lyócsa, Štefan & Výrost, Tomáš, 2018. "Networks of volatility spillovers among stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1555-1574.
    2. Nobi, Ashadun & Alam, Shafiqul & Lee, Jae Woo, 2017. "Dynamic of consumer groups and response of commodity markets by principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 337-344.
    3. Bing Li, 2017. "Network Evolution of the Chinese Stock Market: A Study based on the CSI 300 Index," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(3), pages 1-5.
    4. Bentian Li & Dechang Pi, 2018. "Analysis of global stock index data during crisis period via complex network approach," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    5. Yusuf Yargı BAYDİLLİ & Şafak BAYIR & İlker TÜRKER, 2017. "A Hierarchical View of a National Stock Market as a Complex Network," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 205-222.
    6. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.
    7. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    8. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    9. Kantar, Ersin & Aslan, Alper & Deviren, Bayram & Keskin, Mustafa, 2016. "Hierarchical structure of the countries based on electricity consumption and economic growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 1-10.
    10. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    11. Zeitsch, Peter J., 2019. "A jump model for credit default swaps with hierarchical clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 737-775.
    12. Haishu Qiao & Yue Xia & Ying Li, 2016. "Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    13. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    14. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    15. Stefania Vignini & Tiziana De Cristofaro, 2018. "Impatto della crisi economica su redditivit? e rischio finanziario delle imprese romagnole. Una cluster analysis," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 157-181.
    16. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    17. Matesanz, David & Ortega, Guillermo J., 2015. "Sovereign public debt crisis in Europe. A network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 756-766.
    18. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets Around the Global Financial Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 195-210, February.
    19. Lahmiri, Salim & Uddin, Gazi Salah & Bekiros, Stelios, 2017. "Clustering of short and long-term co-movements in international financial and commodity markets in wavelet domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 947-955.
    20. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.

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