Evaluating the financial performance of companies listed on the Tehran Stock Exchange with using K-Mean and K-Medoid clustering
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Evaluating the financial performance of companies listed on the Tehran Stock Exchange with using K-Mean and K-Medoid clustering

Document Type : Original Article

Authors
Abdolrasoul Mostajeran 1
mohammad amin rafieenia 2
Peyman Pazooki 3
1 Assistant Professor, Department of Financial Mathematics, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
2 PhD student, Department of Financial Managemen, Faculty of Market and Business, tehran jonob Branch, Islamic Azad University,Tehran, Iran
3 PhD student, Department of Financial Management, Faculty of Market and Business, Tehran Jonob Branch, Islamic Azad University, Tehran, Iran
10.22034/jse.2025.12511.2323
Abstract
With the increasing volume of financial data in listed companies, choosing an accurate and efficient method for analyzing and clustering this data is of great importance. The aim of this study is to examine the financial performance of Iranian listed companies and their clustering using two clustering methods: K-Mean and K-Medoid. This study was conducted based on key financial ratios including return on assets, return on equity, gross profit margin, debt-to-equity ratio, and price-to-book value ratio. Financial data related to companies active in the Tehran Stock Exchange were collected and analyzed over a ten-year period. The K-Mean method provides fast clustering due to its high speed and appropriate efficiency in big data, while the K-Medoid method provides more accurate clustering due to its greater resistance to outliers and noisy data. Analysis of variance (ANOVA) indicates a significant difference between clusters based on the aforementioned financial criteria. The results of the research show that the K-Medoid method provides a more balanced distribution and more accurate results in separating companies, while the K-Mean method is more suitable for simpler and high-volume data. The findings of this research can help investors and financial analysts make better strategic decisions based on the identified clusters and considering their risk tolerance and expected returns.
Keywords
Stock Exchange
K-Mean Clustering
K-Medoid Clustering
Return on Assets (ROA)
Return on Equity (ROE)
Subjects

  1. Afrimayani; Devianto, D. (2021). The time series clustering of stock price in LQ45 index and its financial performance analysis. J. Phys .Conf. Ser. 1943, 1–8.

    Akhtar, M; Yusheng, K; Haris, M; Ul Ain, Q; Javaid, H.M. (2022). Impact of financial leverage on sustainable growth, market performance, and profitability. Econ. Chang. Restrict. 55, 737–774.

    Ali, S; Khan, M; Ahmed, R. (2022). Advances in K-Mean Clustering Algorithm: Enhancing Performance and Accuracy. Journal of Machine Learning Research.23, 1125-1145.

    Arora, P; Deepali, D; Varshney, S. (2016). Analysis of K-Means and K-Medoids algorithm for big data. Procedia Comput. Sci. 78, 507–512.

    Bayaraa, B. (2017).Financial performance determinants of organizations: The case of Mongolian companies.J.Compet.9, 22–33.

    Ben-Gal, I. Outlier Detection. In Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, 2nd ed; Maimon, O; Rockach, L; Eds; Springer: New York, NY, USA, 2010; pp. 117–130.

    Brealey, R. A; Myers, S. C; Allen, F. Principles of Corporate Finance; McGraw-Hill Education: 2020.

    Cai, F; Le-Khac, N.A; Kechadi, M.T. (2012). Clustering Approaches for Financial Data Analysis. In Proceedings of the 8th International Conference on Data Mining (DMIN 2012), Las Vegas, NV, USA, 15–18 December.

    De Blasio, V; Pavone, P; Migliaccio, G. Cosmetics companies: Income developments in time of crisis. J. Small Bus. Enterp. Dev.2022. ahead-of-print.

    Deloitte. Global Powers of Retailing. 2021.

    Do, D.T. A study on financial performance of transport & warehouses firms listed on the Hanoi stock exchange. Econ. Financ. Lett. 2021, 8, 44–52.

    Dsouza, S; Dsouza, J.D; Vanitha, T. Analysis of data using k-means and k-medoids algorithms. Int. J. Latest Trends Eng. Technol. Spec. Issue SACAIM 2017, 370–373.

    Dzuba, S; Krylov, D. (2021). Cluster analysis of financial strategies of companies. Mathematics. 9, 3192.

    Esfahani, R.K; Shahbazi, F; Akbarzadeh, M. (2019). Three-phase classification of an uninterrupted traffic flow: A k-means clustering study. Transp. B-Transp. Dyn. 7, 546–558.

    Etemadi, H; Hisarzadeh, R; Rahmani, A; & Azar, A. (2014). Modeling information uncertainty management using fuzzy decision tree methodology: Evidence from feasibility study of information-uncertainty-based theory. Quarterly Journal of the Tehran Stock Exchange, (25). (In Persian)

    Fenyves, V; Zsido, K.E; Bircea, I; Tarnoczi, T. (2020). Financial performance of Hungarian and Romanian retail food small businesses.Br. Food J. 122, 3451–3471.

    Goral, J; Soliwoda, M. (2021). On the profitability of Polish large agricultural holdings. Acta. Oecon. 71, 137–159.

    Güla˘gız, F.K; Sahin, S. (2017). Comparison of hierarchical and non-hierarchical clustering algorithms. Int. J. Comput. Eng. Inf. Technol. 2017, 9, 6–14.59.

    Hatem, B.S. (2014) Determinants of firm performance: A comparison of European countries. Int. J. Econ. Financ. 6, 243–249.

    Hennig, C. (2015). What are the true clusters? Pattern Recognit. Lett. 64, 53–62.

    Hepsen, A; Vatansever, M. (2012). Using hierarchical clustering algorithms for Turkish residential market. Int. J. Econ. Financ. 4, 138–150.

    Herman, E. The interplay between digital entrepreneurship and sustainable development in the context of the EU digital economy: A multivariate analysis. Mathematics 2022, 10, 1682.

    Herman, E; Zsido, K.-E; & Fenyves, V. (2022). Cluster analysis with K-Mean versus K-Medoid in financial performance evaluation. Applied Sciences, 12(12), 7985. https://doi.org/10.3390/

    Herman, E; Zsido, K.-E; Fenyves, V. (2022). Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation. Appl. Sci. 12, 7985.

    Hernant, M; Andersson, T.B; Hilmola, O.P. (2007) Managing retail chain profitability based on local competitive conditions: Preliminary analysis. Int. J. Retail Distrib. Manag. 35, 912–935.

    Hloušková, Z; Lekešová, M. (2020). Farm outcomes based on cluster analysis of compound farm evaluation. Agric. Econ.-Czech. 66, 435–443.

    Ikotun, A.M; Almutari, M.S; Ezugwu, A.E. (2021). K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions. Appl. Sci. 11, 11246.

    Imani, M. (2012). Clustering Persian texts (Master’s thesis). Department of Computer Engineering, Sharif University of Technology. (In Persian)

    Iqbal, M.Z; Riaz, M; Nasir, W. (2017). Multivariate Outlier Detection: A comparison among two clustering techniques. Pak. J. Agric. Sci. 54, 227–231.

    Izadparast, M. (2011). Customer classification in insurance using data mining. Monthly Journal of World Insurance News, (161), Payame Noor University, Tehran. (In Persian)

    1. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.

    Kamalian, A.-R; Niknafs, A.-A; Afsharizad, O; & Gholamalipour, R. (2011). Identifying the most important factors affecting the information disclosure ranking of companies listed on the Tehran Stock Exchange using a data mining approach. Quarterly Journal of the Tehran Stock Exchange, (11). (In Persian)

    Kaur, N.M; Kaur, U; Singh, D. (2014). K-medoid clustering algorithm—A review. Int. J. Comput. Appl. Technol. 1, 42–45.

    Khour, S; Elexa, L; Istok, M; Rosova, A. (2019). A Study from Slovakia on the transfer of Slovak companies to tax havens and theirimpact on the sustainability of the status of a business entity. Sustainability. 11, 2803.

    Kodinariya, T.M; Makwana, P.R. (2013). Review on determining number of cluster in K-Means clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1, 90–95.

    Kou, G; Peng, Y; Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf. Sci. 275, 1–12.

    Kramaric, T.P; Bach, M.P; Dumicic, K; Zmuk, B; Zaja, M.M. (2022). Exploratory study of insurance companies in selected post-transition countries: Non-hierarchical cluster analysis. Cent. Eur. J. Oper. Res. 2018, 26, 783–807.

    1. Breiman, Random Forests, Machine Learning, 2001.
    2. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, 1990

    Lemieux, V; Rahmdel, P.S; Walker, R; Wong, B.L.W; Flood, M. (2014). Clustering techniques and their effect on portfolio formation and risk analysis.

    Lukác, J; Teplická, K; Culková, K; Hrehová, D. (2021). Evaluation of the financial performance of the municipalities in Slovakia in the context of multidimensional statistics. J. Risk Financ. Manag. 14, 570.

    1. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise (KDD), 1996.
    2. Ester, H.-P. Kriegel, J. Sander, and X. Xu, (2010). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), 1996.

    McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media.

    Medellu, J.V.C; Nugraha, E.S. (2021). K-means and k-medoid algorithm application in clustering stock data in Indonesia. In Proceedings of the Symposium on Data Science Petra, Jordan, 5–7 April.

    Nguyena, V.C; Nguyena, T.N.L; Tranb, T.T; Nghiemc, T.T. (2019). The impact of financial leverage on the profitability of real estate companies: A study from Vietnam stock exchange. Manag. Sci. Lett. 9, 2315–2326

    Pakhira, M.K. (2012). Finding Number of Clusters before Finding Clusters. Proc. Technol. 4, 27–37.

    Park, H.S; Jun, C.H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl, 36, 3336–3341.

    Pavelkova, D; Zizka, M; Homolka, L; Knapkova, A; Pelloneova, N. (2021). Do clustered firms outperform the non-clustered? Evidence of financial performance in traditional industries. Ekon. Istraz. 34, 3270–3292.

    Pech, M; Vrchota, J. (2020). Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation. Appl. Sci; 10, 5150.

    Pelloneová, N. (2021). Are there differences in the financial performance of Czech and Slovak cluster organizations? Ekon. Cas; 69, 907–927.

    Pham, D.T; Afify, A.A. (2007). Clustering techniques and their applications in engineering. Proc. Inst. Mech. Eng. Part C-J. Eng. Mech.Eng. Sci. 221, 1445–1459.

    Popa, D.N; Bogdan, V; Sabau Popa, C.D; Belenesi, M. (2021). Performance mapping in two-step cluster analysis through ESEG disclosures and EPS. Kybernetes, 51, 98–118.

    Ren, J; Hua, K; & Cao, Y. (2022). Global Optimal K-Medoids Clustering of One Million Samples. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022).

    Sabău Popa, C.D; Popa, D.N; Bogdan, V; Simut, R. (2021). Composite financial performance index prediction—A neural networks approach. J. Bus. Econ. Manag. 22, 277–296.

    Sabau, A.S. (2012). Survey of clustering based financial fraud detection research. Inform. Econ. 16, 110–122.

    Serban, E.C; Bogeanu, A; Tudor, E. (2013). Clustering techniques in financial data analysis applications on the U.S. financial market. Ann. Constantin Brâncu¸si Univ. Târgu Jiu Econ. Ser. 4, 176–194.

    Shiry. (2006). Introduction to clustering. Lecture notes for Machine Learning course, Amirkabir University of Technology. Retrieved from http://www.aut.ac.ir/shiry/clustering.html. (In Persian).

    Simon, J. (2006). Applications of cluster analysis in marketing research. Stat. Szle. 84, 627–650.

    Soni, K.G; Patel, A. (2017). Comparative Analysis of K-means and K-medoids algorithm on IRIS Data. Int. J. Comput. Intell. Syst.13, 899–906.

    Szabo, Z.K; Herman, E. (2014). Productive entrepreneurship in the EU and its barriers in transition economies: A cluster analysis. ActaPolytech. Hung. 11, 73–94.

    Szüle, B. (2019). Comparison of cluster number determination methods. Stat. Szle. 97, 421–438.

    1. Hastie, R. Tibshirani, and J. Friedman, the Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, 2009.
    2. Hastie, R. Tibshirani, and J. Friedman, the Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009.

    Velmurugan, T. (2012). Efficiency of K-Means and K-Medoids algorithms for clustering arbitrary data points. Int. J. Comput. Appl. Technol. 3, 1758–1764

    Velmurugan, T; Santhanam, T. (2010). Computational complexity between K-Means and K-Medoids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci. 6, 363–368.

    Vishwakarma, S; Nair, P.S; Rao, D.S. (2017). A comparative study of K-means and K-medoid clustering for social media text mining. Int.J. Adv. Sci. Res. Eng. Trends. 2, 297–302.

    Wang, Y.J; Lee, H.S. (2008). A clustering method to identify representative financial ratios. Inf. Sci. 178, 1087–1097.

    Windgassen, S; Moss-Morris, R; Goldsmith, K; Chalder, T. (2018). The importance of cluster analysis for enhancing clinical practice: An example from irritable bowel syndrome. J. Ment. Health. 27, 94–96.

    Xiaojun, S; Yalin, L. (2021). Research on financial early warning of mining listed companies based on BP neural network model. Resour. Policy 73, 102223.

    Yadegari, R; Rahmani, K; Modarres Khiyabani, F. (2018). Providing a comprehensive model to measure the performance dimensions of industrial clusters using the hybrid approach of Q factor analysis and cluster analysis. Int. J. Qual. Res. 13, 235–248.

    Yen, M.-F; Huang, Y.-P; Yu, L.-C; Chen, Y.-L. (2022). A two-dimensional sentiment analysis of online public opinion and future financial performance of publicly listed companies. Comput. Econ. 59, 1677–1698.

    Zainudin, R; Mahdzan, N.S; Mohamad, N.N. (2021) Internationalisation and financial performance: In the case of global automotivefirms. Rev. Int. Bus. Strategy. 31, 80–102.

    Zhussupova, Z; Onyusheva, I; El-Hodiri, M. (2018). Corporate governance and firm value of Kazakhstani companies in the conditions of economic instability. Pol. J. Manag. Stud; 17, 235–245.

Quarterly Journal of Securities Exchange
Volume 18, Issue 70
Summer 2025
Pages 165-198