ملخص المقالة :
هدف این پژوهش ایجاد و معرفی یک شبکه مالی جدید و بررسی معیارهای مرکزیت سهام برای بهینه سازی سبد سهام سرمایه گذاران و همچنین شناسایی رهبران بازار سهام می باشد. در این پژوهش 100 شرکت برتر بورس اوراق بهادار که دارای بیشترین سرمایه ثبت شده اند در بازه زمانی دی ماه 1388 تا دی ماه 1398 انتخاب شدند. شبکه مالی با استفاده از قیمت پایانی تعدیل شده به بازده لگاریتمی تبدیل و ضریب همبستگی پیرسون بازده های سهام محاسبه گردید. از مفاهیم تئوری گراف و الگوریتم پریم برای کشف روابط و فاصله های موجود بین سهام برای ساخت حداقل درخت پویا استفاده گردید. نتایج نشان داد بر اساس معیار مرکزیت درجه، سهام مخابرات ایران و بانک آینده، بر اساس معیار مرکزیت نزدیکی، سهام سرمایه گذاری بهمن، تامین سرمایه امید و بانک گردشگری، بر اساس معیار مرکزیت بینابینی، سهام تامین سرمایه امید، سرمایه گذاری بهمن و بیمه آسیا و بر اساس معیار مرکزیت تنگنا، سهام بیمه آسیا، بانک گردشگری و تامین سرمایه امید بیشترین تاثیر را بر شبکه مالی و بازار سهام دارند. همچنین در نهایت شبکه مالی به 9 خوشه تقسیم شد که هر خوشه نشان دهنده ارتباط قوی تر اجزای آن با یکدیگر می باشد.
المصادر:
منابع
1, 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), P12005.
2, Coletti, P. (2016). Comparing minimum spanning trees of the Italian stock market using returns and volumes. Physica A: Statistical Mechanics and its Applications, 463, 246-261.
3, Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
4, Fama,E. F., & French, K. R.(1997). Industry costs of equity.Journal of financial economics, 43(2), 153-193.
5, Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.
6, Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38(2), 363-371.
7, Tumminello, M., Lillo, F., & Mantegna, R. N. (2010). Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior & Organization, 75(1), 40-58.
8, Graham, R. L., & Hell, P. (1985). On the history of the minimum spanning tree problem. Annals of the History of Computing, 7(1), 43-57.
9, Al-Taie, M. Z., & Kadry, S. (2017). Python for graph and network analysis (pp. 1-184). Cham: Springer International Publishing.
10, Gower, J. C., & Ross, G. J. (1969). Minimum spanning trees and single linkage cluster analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 18(1), 54-64.
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.
12. Li, Y., Jiang, X. F., Tian, Y., Li, S. P., & Zheng, B. (2019). Portfolio optimization based on network topology. Physica a-Statistical Mechanics and Its Applications, 515, 671-681. doi:10.1016/j.physa.2018.10.014
13. 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, 1091-1109. doi:10.1016/j.physa.2019.04.128.
14. Wegner, D. L. B. (2019). A note on liquidity policies and financial networks. Journal of Financial Economic Policy, 11(3), 451-456. doi:10.1108/jfep-10-2018-0148.
15. Inekwe, J. N., Jin, Y., & Valenzuela, M. R. (2018). Global financial network and liquidity risk. Australian Journal of Management, 43(4), 593-613. doi:10.1177/0312896218766219
16. Eng-Uthaiwat, H. (2018). Stock market return predictability: Does network topology matter? Review of Quantitative Finance and Accounting, 51(2), 433-460. doi:10.1007/s11156-017-0676-3
17. 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, 751-764.
18. Wang, Y., Li, H., Guan, J., & Liu, N. (2019). Similarities between stock price correlation networks and co-main product networks: Threshold scenarios. Physica A: Statistical Mechanics and its Applications, 516, 66-77.
19. Iori, G., & Mantegna, R. N. (2018). Empirical analyses of networks in finance. In Handbook of Computational Economics (Vol. 4, pp. 637-685). Elsevier.
20. Tumminello, M., Coronnello, C., Lillo, F., Micciche, S., & Mantegna, R. N. (2007). Spanning trees and bootstrap reliability estimation in correlation-based networks. International Journal of Bifurcation and Chaos, 17(07), 2319-2329.
21. Brida, J. G., & Risso, W. A. (2009). Dynamic and Structure of the Italian Stock Market based on returns and volume trading. Economics Bulletin, 29(3), 2420-2426.
22. Newman, M. (2018). Networks. Oxford university press.
23. Rochat, Y. (2009). Closeness centrality extended to unconnected graphs: The harmonic centrality index (No. CONF).
24. Cheng, B. (2006). Using social network analysis to investigate potential bias in editorial peer review in core journals of comparative/international education.
25. Scott, J. (2000). Social network analysis: A handbook. 2nd edn sage publications.
Dangalchev, C. (2006). Residual closeness in networks. Physica A: Statistical Mechanics and its Applications, 365(2), 556-564.
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منابع
1, 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), P12005.
2, Coletti, P. (2016). Comparing minimum spanning trees of the Italian stock market using returns and volumes. Physica A: Statistical Mechanics and its Applications, 463, 246-261.
3, Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
4, Fama,E. F., & French, K. R.(1997). Industry costs of equity.Journal of financial economics, 43(2), 153-193.
5, Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.
6, Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38(2), 363-371.
7, Tumminello, M., Lillo, F., & Mantegna, R. N. (2010). Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior & Organization, 75(1), 40-58.
8, Graham, R. L., & Hell, P. (1985). On the history of the minimum spanning tree problem. Annals of the History of Computing, 7(1), 43-57.
9, Al-Taie, M. Z., & Kadry, S. (2017). Python for graph and network analysis (pp. 1-184). Cham: Springer International Publishing.
10, Gower, J. C., & Ross, G. J. (1969). Minimum spanning trees and single linkage cluster analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 18(1), 54-64.
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.
12. Li, Y., Jiang, X. F., Tian, Y., Li, S. P., & Zheng, B. (2019). Portfolio optimization based on network topology. Physica a-Statistical Mechanics and Its Applications, 515, 671-681. doi:10.1016/j.physa.2018.10.014
13. 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, 1091-1109. doi:10.1016/j.physa.2019.04.128.
14. Wegner, D. L. B. (2019). A note on liquidity policies and financial networks. Journal of Financial Economic Policy, 11(3), 451-456. doi:10.1108/jfep-10-2018-0148.
15. Inekwe, J. N., Jin, Y., & Valenzuela, M. R. (2018). Global financial network and liquidity risk. Australian Journal of Management, 43(4), 593-613. doi:10.1177/0312896218766219
16. Eng-Uthaiwat, H. (2018). Stock market return predictability: Does network topology matter? Review of Quantitative Finance and Accounting, 51(2), 433-460. doi:10.1007/s11156-017-0676-3
17. 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, 751-764.
18. Wang, Y., Li, H., Guan, J., & Liu, N. (2019). Similarities between stock price correlation networks and co-main product networks: Threshold scenarios. Physica A: Statistical Mechanics and its Applications, 516, 66-77.
19. Iori, G., & Mantegna, R. N. (2018). Empirical analyses of networks in finance. In Handbook of Computational Economics (Vol. 4, pp. 637-685). Elsevier.
20. Tumminello, M., Coronnello, C., Lillo, F., Micciche, S., & Mantegna, R. N. (2007). Spanning trees and bootstrap reliability estimation in correlation-based networks. International Journal of Bifurcation and Chaos, 17(07), 2319-2329.
21. Brida, J. G., & Risso, W. A. (2009). Dynamic and Structure of the Italian Stock Market based on returns and volume trading. Economics Bulletin, 29(3), 2420-2426.
22. Newman, M. (2018). Networks. Oxford university press.
23. Rochat, Y. (2009). Closeness centrality extended to unconnected graphs: The harmonic centrality index (No. CONF).
24. Cheng, B. (2006). Using social network analysis to investigate potential bias in editorial peer review in core journals of comparative/international education.
25. Scott, J. (2000). Social network analysis: A handbook. 2nd edn sage publications.
Dangalchev, C. (2006). Residual closeness in networks. Physica A: Statistical Mechanics and its Applications, 365(2), 556-564.
