Phân tích phân cụm các doanh nghiệp ở Việt Nam, Thái Lan và Indonesia trong giai đoạn 2018–2022: Ứng dụng phương pháp phân tích dữ liệu đa hợp
DOI:
https://doi.org/10.24311/jabes/2025.36.3.04Keywords:
Compositional data analysis, Cluster analysis, Financial ratios, Business classification, COVID-19Abstract
The study focuses on using financial indicators to analyze the clustering of businesses, employing a compositional data analysis method. With data taken from the financial reports of 300 large enterprises in Thailand, Indonesia, and Vietnam, the study aims to cluster the enterprises and thereby understand the characteristics of each cluster as well as the movement of enterprises between clusters during the period 2018-2022. The results obtained show no significant difference when using both CLR and ALR methods, the financial indicators are effective in classifying companies, dividing them into three clusters based on business efficiency and financial risk levels. Analysis in the pre-COVID-19 and post-COVID-19 periods also shows signs of recovery in business results through the clustering shifts of some companies. Notably, Vietnamese businesses demonstrate good operational performance and maintain a significant presence in the group with the best performance results and high payment safety.
References
Aitchison, J. (1983). Principal component analysis of compositional data. Biometrika, 70(1), 57-65. https://doi.org/10.1093/biomet/70.1.57
Aitchison, J. (1986). The Statistical Analysis of Compositional Data (Monographs on Statistics and Applied Probability) (1st ed.). Springer.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/J.1540-6261.1968.TB00843.X
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-Score model. Journal of International Financial Management & Accounting, 28(2), 131-171. https://doi.org/10.1111/JIFM.12053
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
Carper, W. B., & Snizek, W. E. (1980). The nature and types of organizational taxonomies: An overview. The Academy of Management Review, 5(1), 65-75. https://doi.org/10.2307/257805
Chen, M.-Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261-11272. https://doi.org/10.1016/j.eswa.2011.02.173
Coenders, G., & Serrat, N. A. (2023). Accounting statement analysis at industry level. A gentle introduction to the compositional approach. https://arxiv.org/abs/2305.16842v5
Cortés, A. E., Martínez, M. G., & Rubio, N. G. (2007). Multiclass corporate failure prediction by Adaboost.M1. International Advances in Economic Research, 13(3), 301-312. https://doi.org/10.1007/s11294-007-9090-2
Creixans-Tenas, J., Coenders, G., & Arimany-Serrat, N. (2019). Corporate social responsibility and financial profile of Spanish private hospitals. Heliyon, 5(10), e02623. https://doi.org/10.1016/j.heliyon.2019.e02623
Bailey, K. (1994). Typologies and Taxonomies. SAGE Publications, Inc. https://doi.org/10.4135/9781412986397
du Jardin, P., Veganzones, D., & Séverin, E. (2019). Forecasting corporate bankruptcy using accrual-based models. Computational Economics, 54(1), 7-43. https://doi.org/10.1007/s10614-017-9681-9
Glassman, D. A., & Riddick, L. A. (1996). Why empirical international portfolio models fail: evidence that model misspecification creates home asset bias. Journal of International Money and Finance, 15(2), 275-312. https://doi.org/10.1016/0261-5606(95)00046-1
Goldstein, H., & Healy, M. J. R. (1995). The graphical presentation of a collection of means. Journal of the Royal Statistical Society. Series A (Statistics in Society), 158(1), 175-177. https://doi.org/10.2307/2983411
Greenacre, M. (2018). Compositional Data Analysis in Practice (1st ed.). New York: Chapman and Hall/CRC. https://doi.org/10.1201/9780429455537
Greenacre, M. (2019). Variable selection in compositional data analysis using pairwise logratios. Mathematical Geosciences, 51(5), 649-682. https://doi.org/10.1007/s11004-018-9754-x
Greenacre, M., & Lewi, P. (2009). Distributional equivalence and subcompositional coherence in the analysis of compositional data, contingency tables and ratio-scale measurements. Journal of Classification, 26(1), 29-54. https://doi.org/10.1007/s00357-009-9027-y
Idson, T. L., & Oi, W. Y. (1999). Workers are more productive in large firms. American Economic Review, 89(2), 104-108. https://doi.org/10.1257/AER.89.2.104
Jofre-Campuzano, P., & Coenders, G. (2022). Compositional classification of financial statement profiles: The weighted case. Journal of Risk and Financial Management, 15(12), 546. https://doi.org/10.3390/JRFM15120546
Jones, S., & Wang, T. (2019). Predicting private company failure: A multi-class analysis. Journal of International Financial Markets, Institutions and Money, 61, 161-188. https://doi.org/10.1016/j.intfin.2019.03.004
Kaplan, A. D. H. (1950). The influence of size of firms on the functioning of the economy. The American Economic Review, 40(2), 74-84. http://www.jstor.org/stable/1818026
Kokoszka, P., Miao, H., Petersen, A., & Shang, H. L. (2019). Forecasting of density functions with an application to cross-sectional and intraday returns. International Journal of Forecasting, 35(4), 1304-1317. https://doi.org/10.1016/J.IJFORECAST.2019.05.007
Laitinen, E. K., & Laitinen, T. (2000). Bankruptcy prediction: Application of the Taylor’s expansion in logistic regression. International Review of Financial Analysis, 9(4), 327-349. https://doi.org/10.1016/S1057-5219(00)00039-9
Lev, B., & Sunder, S. (1979). Methodological issues in the use of financial ratios. Journal of Accounting and Economics, 1(3), 187-210. https://doi.org/10.1016/0165-4101(79)90007-7
Linares-Mustarós, S., Coenders, G., & Vives-Mestres, M. (2018). Financial performance and distress profiles. From classification according to financial ratios to compositional classification. Advances in Accounting, 40, 1-10. https://doi.org/10.1016/J.ADIAC.2017.10.003
Linares-Mustarós, S., Farreras-Noguer, M. À., Arimany-Serrat, N., & Coenders, G. (2022). New financial ratios based on the compositional data methodology. Axioms, 11(12), 694. https://doi.org/10.3390/AXIOMS11120694
Martín-Ferńandez, J. A., Palarea-Albaladejo, J., & Olea, R. A. (2011). Dealing with Zeros. In V. Pawlowsky-Glahn, & A. Buccianti (eds), Compositional Data Analysis: Theory and Applications (pp. 43-58). https://doi.org/10.1002/9781119976462.CH4
Martín-Oliver, A., Ruano, S., & Salas-Fumás, V. (2017). The fall of Spanish cajas: Lessons of ownership and governance for banks. Journal of Financial Stability, 33, 244-260. https://doi.org/10.1016/J.JFS.2017.02.004
McCann, B. T., & Folta, T. B. (2008). Location matters: Where we have been and where we might go in agglomeration research. Journal of Management, 34(3), 532-565. https://doi.org/10.1177/0149206308316057
Mselmi, N., Lahiani, A., & Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis, 50, 67-80. https://doi.org/10.1016/j.irfa.2017.02.004
Palarea-Albaladejo, J., & Martín-Fernández, J. A. (2008). A modified EM alr-algorithm for replacing rounded zeros in compositional data sets. Computers & Geosciences, 34(8), 902-917. https://doi.org/10.1016/J.CAGEO.2007.09.015
Peek, J., & Rosengren, E. S. (2005). Unnatural selection: Perverse incentives and the misallocation of credit in Japan. American Economic Review, 95(4), 1144-1166. https://doi.org/10.1257/0002828054825691
Rich, P. (1992). The organizational taxonomy: Definition and design. The Academy of Management Review, 17(4), 758-781. https://doi.org/10.2307/258807
Saus-Sala, E., Farreras-Noguer, À., Arimany-Serrat, N., & Coenders, G. (2021). Compositional DuPont analysis. A visual tool for strategic financial performance assessment. In Filzmoser, P., Hron, K., Martín-Fernández, J.A., Palarea-Albaladejo, J. (eds), Advances in Compositional Data Analysis: Festschrift in Honour of Vera Pawlowsky-Glahn (pp. 189-206). Springer. https://doi.org/10.1007/978-3-030-71175-7_10
Downloads
Published
Issue
Section
License
Copyright (c) 2025 JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
