Comparison of the Characteristics of Interaction Networks Based on Pearson and Partial Correlations in the Tehran Stock Exchange

Articles in Press

Document Type : Original Article

Authors
Masoome Ramezani 1
Fraydoon Rahnamay Roodposhti 2
Ghanbar Abbaspour Esfaden 3
1 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Department Accounting and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
10.22034/jik.2026.78656.4792
Abstract
Abstract
Partial correlation is a metric used to measure the dependence between two random variables while controlling for the influence of other variables. In this study, interaction networks based on Pearson correlation and partial correlation are compared in the context of the Tehran Stock Exchange. The results show that the minimum spanning tree (MST) constructed from Pearson correlation exhibits a star-like structure with a dominant central node, whereas the MST derived from partial correlation has a more uniform structure. This contrast indicates that ignoring the effect of common variables may lead to misleading interpretations of network structure. Furthermore, the analysis of the generalized network dimension as a function of the parameter q reveals that networks based on partial correlation are more effective in distinguishing between real and random data. Finally, the relationship between network dimension and Rényi entropy is examined in both types of networks, and their distinctive characteristics are analyzed.
Interaction network, Minimum Spanning Tree, Pearson correlation, Partial correlation, Tehran Stock Exchange

Keywords
Interaction network, Minimum Spanning Tree, Pearson correlation, Partial correlation, Tehran Stock Exchange
Keywords
Interaction network
Minimum Spanning Tree
Pearson correlation
Partial correlation
Tehran Stock Exchange
1.      Aslam, F., Mohti, W., & Ferreira, P. (2020). Evidence of intraday multifractality in European stock markets during the recent coronavirus (COVID-19) outbreak. International Journal of Financial Studies, 8(2), 31. https://doi.org/10.3390/ijfs8020031
 
2.      Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535–559. https://doi.org/10.1016/j.jfineco.2011.12.010
 
3.      Cao, L., Liu, Y., & Xu, Y. (2022). Tail dependence networks for financial risk analysis. Physica A: Statistical Mechanics and its Applications, 600, 127511. https://doi.org/10.1016/j.physa.2021.127511
 
4.      Han, R., & Kristoufek, L. (2019). Can entropy-based measures quantify the diversification of financial portfolios? Physica A: Statistical Mechanics and its Applications, 531, 121759. https://doi.org/10.1016/j.physa.2019.121759
 
5.      Jiang, X., & Zheng, B. (2020). Partial correlation analysis of financial markets. Physica A: Statistical Mechanics and its Applications, 545, 123376. https://doi.org/10.1016/j.physa.2019.123376
 
6.      Kenett, D. Y., Preis, T., Gur-Gershgoren, G., & Ben-Jacob, E. (2014). Dependency network and node influence: Application to the study of financial markets. International Journal of Bifurcation and Chaos, 24(03), 1450030. https://doi.org/10.1142/S021812741450030X
 
7.      Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PLoS ONE, 5(12), e15032. https://doi.org/10.1371/journal.pone.0015032
 
8.      Liu, J., Zhao, S., & Li, W. (2023). Comparing Pearson, partial, and mutual information-based networks in cryptocurrency markets. Finance Research Letters, 57, 104123. https://doi.org/10.1016/j.frl.2023.104123
9.      Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B - Condensed Matter and Complex Systems, 11(1), 193–197.
 
10.  Millington, T., & Niranjan, M. (2020). Partial correlation financial networks. Applied Network Science, 5(1), 1–19. https://doi.org/10.1007/s41109-020-00317-7
 
11.  Morone, F., & Grassi, R. (2021). Lower tail dependence in financial markets and systemic risk assessment. Journal of Economic Dynamics and Control, 124, 104088. https://doi.org/10.1016/j.jedc.2021.104088
 
12.  Nie, C., & Song, F. (2018). Relationship between entropy and dimension of financial correlation-based network. Entropy, 20(3), 177. https://doi.org/10.3390/e20030177
 
13.  Onnela, J. P., Chakraborti, A., Kaski, K., Kertész, J., & Kanto, A. (2003). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E, 68(5), 056110. https://doi.org/10.1103/PhysRevE.68.056110
 
14.  Vidal-Tomás, D. (2021). Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis. Finance Research Letters, 43, 101981. https://doi.org/10.1016/j.frl.2021.101981
 
15.  Wiliński, M., Sienkiewicz, A., Gubiec, T., Kutner, R., & Struzik, Z. R. (2013). Structural and topological phase transitions on the German Stock Exchange. Physica A: Statistical Mechanics and its Applications, 392(23), 5963–5973.
 
16.  Yang, X., Wen, S., Zhao, X., & Huang, C. (2020). Systemic importance of financial institutions: A complex network perspective. Physica A: Statistical Mechanics and its Applications, 545, 123448. https://doi.org/10.1016/j.physa.2019.123448
 
17.  Zhang, X., Li, Y., & Wang, H. (2022). Partial correlation financial networks. Physica A: Statistical Mechanics and its Applications, 600, 127511. https://doi.org/10.1016/j.physa.2021.127511