The Art of Investment Portfolio Curation through Centrality Metrics (An Enchanting Network Analysis of Tehran Stock Exchange's Top 50 Companies)

Document Type : Original Article

Authors

1 Ph.D. in Financial Engineering, Faculty of Economic, Management , and Accounting, Yazd University, Yazd, Iran.

2 Ph D Candidate. Department of Management, Faculty of Social and Economic Sciences, Al-Zahra University, Tehran, Iran

3 Associate Professor .Department of Accounting and Finance, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran

Abstract

The stock market, as a prominent financial domain, presents a formidable challenge in comprehending and evaluating an extensive array of stocks employing centrality measurements to portray the key variables within a network, companies' stocks can be visualized and comprehended. The utilization of stock network analysis facilitates a comprehensive understanding of the entire network through diverse visualization techniques. This study delves into the data of the top 50 companies listed on the Tehran Securities Exchange during the period from January 1, 2019, to July 6, 2021. Employing unsupervised machine learning tools such as Community Detection algorithms and network analysis methods like Louvain and Girvan-Newman, we construct a stock network. Subsequently, we compute five Centrality Metrics, including Degree Centrality, Closeness Centrality, Eigen Centrality, Betweenness Centrality, and PageRank, for these companies. By formulating a similarity matrix based on these criteria for the remaining stocks in the network, we determine a portfolio of 25 stocks suitable for investment, derived from the ranking of stocks according to the Centrality Metrics

Keywords

Main Subjects

Smiley face

References

Allen, F., & Gale, D. (2000). Financial contagion. Journal of political economy, 108(1), 1-33.
Al-Taie, M. Z., & Kadry, S. (2017). Python for graph and network analysis. Springer.
Bhattacharjee, B., Shafi, M., & Acharjee, A. (2017). Investigating the evolution of linkage dynamics among equity markets using network models and measures: The case of asian equity market integration. Data, 2(4), 41.
Bechis, L. (2020). Machine learning portfolio optimization: hierarchical risk parity and modern portfolio theory
Chen, H., Xiao, K., Sun, J., & Wu, S. (2017). A double-layer neural network framework for high-frequency forecasting. ACM Transactions on Management Information Systems (TMIS), 7(4), 1-17.
Chi, K. T., Liu, J., & Lau, F. C. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667.
de Pontes, L. S., & Rêgo, L. C. (2022). Impact of macroeconomic variables on the topological structure of the Brazilian stock market: A complex network approach. Physica A: Statistical Mechanics and its Applications, 604, 127660.
George, Susan, and Manoj Changat. 2017. “Network Approach for Stock Market Data Mining and Portfolio Analysis.” In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), IEEE, 251–56.
Huang, Wei-Qiang, Xin-Tian Zhuang, and Shuang Yao. 2009. “A Network Analysis of the Chinese Stock Market.” Physica A: Statistical Mechanics and its Applications 388(14): 2956–64.
Hüttner, Amelie, Jan-Frederik Mai, and Stefano Mineo. 2018. “Portfolio Selection Based on Graphs: Does It Align with Markowitz-Optimal Portfolios? ” Dependence Modeling 6(1): 63–87.
Kullmann, L, J Kertesz, and R N Mantegna. 2000. “Identification of Clusters of Companies in Stock Indices via Potts Super-Paramagnetic Transitions.” Physica A: Statistical Mechanics and its Applications 287(3–4): 412–19.
Kumar, Sunil, and Nivedita Deo. 2012. “Correlation and Network Analysis of Global Financial Indices.” Physical Review E 86(2): 26101.
Liu, Jing, Chi K Tse, and Keqing He. 2011. “Fierce Stock Market Fluctuation Disrupts Scalefree Distribution.” Quantitative Finance 11(6): 817–23.
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.
Nourahmadi, M., & Sadeghi, H. (2023). Application of Threshold-based Filtered Networks in Stock Portfolio Selection and Performance Evaluation. Financial Economics, 17(64), 1-26. doi: 10.30495/fed.2023.705588(in persian)
 Nourahmadi, M., & Sadeqi, H. (2022). The Application of the Main Components in Investment Basket Management: A Case Study of Fifty Stock Exchange Companies. Scientific Journal of Budget and Finance Strategic Research, 3(1), 95-71. (in persian)
Onnela, J-P et al. 2003. “Dynamics of Market Correlations: Taxonomy and Portfolio Analysis.” Physical Review E 68(5): 56110.
Rasti, F., & Sadeqi, H. (2021). Development of Financial Networks Based on Cointegration Concept (A Study on Tehran Stock Exchange). Financial Engineering and Portfolio Management, 12(46), 235-254. (in persian)
Vizgunov, Arsenii et al. 2014. “Network Approach for the Russian Stock Market.” Computational Management Science 11(1–2): 45–55.
Wei, K C John, Yu-Jane Liu, Chau-Chen Yang, and Guey-Shiang Chaung. 1995. “Volatility and Price Change Spillover Effects across the Developed and Emerging Markets.” Pacific-Basin Finance Journal 3(1): 113–36.
Zhang, P., Wang, T., & Yan, J. (2022). PageRank centrality and algorithms for weighted, directed networks. Physica A: Statistical Mechanics and its Applications, 586, 126438.
Budget and Finance Strategic Research
Volume 4, Issue 4 - Serial Number 16
Winter Quarterly
January 2024
Pages 35-61

Files

History

  • Receive Date: 09 December 2023
  • Revise Date: 20 December 2023
  • Accept Date: 20 December 2023

Share

How to cite

Statistics