An Analysis of Capital Market Using Network Approach

Document Type : Research Paper

Authors

1 Assistant Prof., Department of Accounting, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.

2 Ph.D., Department of Accounting, Faculty of Accounting and Financial Sciences, College of Management, University of Tehran, Tehran, Iran.

Abstract

Objective
Financial markets are considered to be a kind of complex network due to the interaction and interrelationship of its various actors. Therefore, the development of stock communication networks is an important issue for discovering potential connections between different companies. Therefore, the purpose of this research is to investigate and analyze the communication structure of companies in the capital market based on price, return, and trading volume of stock with a network approach.
 
Methods
This study employs a quantitative research methodology, specifically focusing on network analysis. The data were collected in the period between 2017 and 2021 and analyzed using the network analysis method and with the help of Excel 2021, SPSS 26, UCINET 6, and its complementary package NetDraw. In addition to the general analysis and review of the price, returns, and trading volume of the stock correlation network, the performance of each node in the network was also investigated using micro-indices. Centrality is one of the most important micro-concepts of network analysis, which examines the influence and importance of people in the network. Also, it was studied with three indices of degree, betweenness, and closeness of centrality of network nodes.
 
Results
The results indicated that as the correlation basis expands, there is a decrease in communication between different companies along with an increase in irregularity. The higher the correlation basis, the less likely it is to be affected by a common variable or interaction, which will reduce the correlation between stocks. In addition, there are companies in the communicative network that are in a better position than the rest. They may have higher connections, faster access to information, and greater influence over other units' price changes. As a result, these companies can play a decisive and leading role, or in other words, the role of key and influential actors in the governing structure. Also, the results showed that in the second period of 2021 compared to the first period, communication networks were much more erratic and scattered.
 
Conclusion
Considering the importance of capital market communication networks and the decisive role of key companies, relationship pattern analysis helps to increase transparency, reduce risk, and, consequently, improve decision-making, policy-making, portfolio optimization, etc by revealing the network structure of the capital market. The results of this research can lead to a better understanding of the forces that create the communication structure of companies and enable policymakers to make better decisions. Therefore, all users are suggested to pay attention to the communication structure governing the companies in the capital market and the role and impact of their position on the advancement of the company's goals in their decisions and to consider this communication structure and the way power is distributed.

Keywords

Main Subjects

References

 
References
Abbasi, A., Hossain, L., Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(3), 403-412.
Acemoglu, D., Ozdaglar, A. & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564-608.
Babu Roy, R. & Kumar Sarkar, U. (2011). Network Approach to Capture Co-movements of Global Stock Returns. Indian Institute of Management Calcutta, Working Paper. WPS NO.676, Available online at: www.iimcal.ac.in/sites/all/files/pdfs/wps_676.pdf.
Baltakiene, M., Kanniainen, J., Baltakys, K. (2021). Identification of information networks in stock markets. Journal of Economic Dynamics & Control, 131, 104217.
Billio, M., Caporin, M., Panzica, R. & Pelizzon, L. (2023). The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification. International Review of Economics & Finance, 84, 196-223.
Boginski, V., Butenko, S., Pardalos, P.M. (2006). Mining Market Data: A Network Approach. Computers & Operations Research, 33, 3171–3184.
Chen, W., Jiang, M. & Jiang, C. (2020). Constructing a multilayer network for stock market. Soft Computing, 24, 6345-6361.
Chen, W., Qu, S., Jian, M. & Jiang, C. (2021). The construction of multilayer stock network model. Physica A: Statistical Mechanics and its Applications, 565, 125608.
Chen, Y. J., Zenou, Y. & Zhou, J. (2022). The impact of network topology and market structure on pricing. Journal of Economic Theory, 204, 105491.
Constantine, L. S. (2014). Understanding the Linkages in Organisational and Human Relation: A Review of Social Network Analysis. The Qualitative Report, 19(1), 1-22.
Dimitrios, K. & Vasileios, O. (2015). A Network Analysis of the Greek Stock Market Procedia Economics and Finance, 33, 340 – 349.
Fallahpour, S. & Ghahramani, A. (2021). An Analysis of Centrality’s Features as a New Measure for Network Analysis, Risk Measurement & Portfolio Selection. Financial Research Journal, 23(2), 158-171. (in Persian)
Freeman, L.C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science, Publisher: BookSurge, LLC, North Charleston, South Carolina, Printed in the United States of America.
Freitas, W. B. & Junior, J. R. B. (2023). Random walk through a stock network and predictive analysis for portfolio optimization. Expert Systems with Applications, 119597.
George, S. & Changat, M. (2017, July). Network approach for stock market data mining and portfolio analysis. In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT) (pp. 251-256). IEEE.
Ghasemi, H. R., Najafi, A. A. (2014). Portfolio Optimization in terms of Justifiability Short Selling and Some Market Practical Constraints. Financial Research Journal, 14(2), 117-132. (in Persian)
Godigbe, B. G., Chui, C. M. & Liu, C. L. (2018). Directors network centrality and earnings quality, Applied Economics, 5381-5400.
Gong, Ch., Tang, P., Wang, Y. (2019). Measuring the network connectedness of global stock markets. Physica A: Statistical Mechanics and its Applications, 535, 122351.
Gua, X., Li, W., Zhang, H., Tianhai, T. (2022). Multi-likelihood methods for developing relationship networks using stock market data. Physica A: Statistical Mechanics and its Applications, 585, 126421.
He, C., Wen, Z., Huang, K. & Ji, X. (2022). Sudden shock and stock market network structure characteristics: A comparison of past crisis events. Technological Forecasting and Social Change, 180, 121732.
Hosseini, S., Wormald, N., Tian, T. (2021). A Weight-based Information Filtration Algorithm for Stock-Correlation Networks. Physica A: Statistical Mechanics and its Applications, 563, 125489.
Huang, Q., Zhao, J., Wu, X. (2021). Financial risk propagation between Chinese and American stock markets based on multilayer networks. Physica A: Statistical Mechanics and its Applications, 586, 126445.
Huang, W.Q., Zhuang, X.-T., S, Yao (2009). A Network Analysis of the Chinese Stock Market. Physica A, 388, 2956-2964.
Kumar, S. & Jan, J. M. (2012). Discovering Knowledge Landscapes: An Epistemic Analysis of Business and Management Field in Malaysia. Procedia-Social and Behavioral Sciences, 65, 1027-1032.
Luo, R., Zhao, S. & Zhou, J. (2023). Information network, public disclosure and asset prices. Pacific-Basin Finance Journal, 77, 101882.
Raie, R., Bajalan, S., Ajam, A. (2021). Investigating the Efficiency of the 1/N Model in Portfolio Selection. Financial Research Journal. 23(1), 1-16. (in Persian)
Raie, R., Jaafari, GH., Namaki, A. (2011). Analysis of Tehran Stock Exchange using sophisticated networks based on threshold method. Journal of Accounting and Auditing Review, 17(62), 33-48. (in Persian)
Taghizadeh, R. & Nazemi, A. (2018). Analysis of Ownership Network in the Iranian Stock Markets. Journal of Accounting Knowledge, 9(3), 115-144. (in Persian)
Taghizadeh, R. & Ruhani, A. (2021). Investigating the inequality of iommunication structure in the Iranian Stock Market through social network analysis approach. Strategic Research on Social Problems in Iran, 9(3), 31-48. (in Persian)
Taghizadeh, R., Nazemi, A. & Sadeghzadeh Maharluie, M. (2019). Analyzing shareholder network in the Tehran Stock Exchange. Iranian Journal of Finance, 3(4), 113-134.
Taghizadeh, R., Nazemi, A. & Sadeghzadeh Maharluie, M. (2021). Network analysis of interpersonal relationships in Tehran Stock Exchange. Advances in Mathematical Finance & Applications, 6(1), 49-61.
Tse, C.K., Liu, J. & Lau, F.C.M. (2010). A Network Perspective of the Stock Market. Journal of Empirical Finance, 17(2), 659-667.
Validi, J., Najafi, A. A. & Validi, A. (2020). Online Portfolio Selection Based on Follow-the-Loser Algorithms. Financial Research Journal, 22(3), 408-427. (in Persian)
Wang, Z., Liu, S., Yang, H. (2019). The influence of social network structure on stock price disclosure. Physica A: Statistical Mechanics and its Applications, 533, 122064.
Wu, S., Tuo, M., Xiong, D, (2018). Effects of fundamentals acquisition and strategy switch on stock price dynamics. Physica A: Statistical Mechanics and its Applications, 491, 799–809.
Wu, S., Tuo, M., Xiong, D. (2015). Network Structure Detection and Analysis of Shanghai Stock Marke. Journal of Industrial Engineering and Management, 8(2), 383–398.
Yang, X., Jin, C., Huang, C. & Yang, X. (2022). Network characteristics and stock liquidity: Evidence from the UK. Finance Research Letters, 103625.
Zhang, Y., Chu, G. & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, 101484.
Financial Research Journal
Volume 25, Issue 3
2023
Pages 369-386

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