Document Type : Original Research
Author
Department of Accounting, Universitas Muhammadiyah Surakarta, Madiun, Indonesia
Abstract
This research aims to identify the most accurate model for predicting bankruptcy in the banking industry in Indonesia. The three models used in this study are the Altman X-Score, Springate S-Score, and Zmijewski Z-Score models. The population used consists of all banks listed on the Indonesia Stock Exchange (IDX). The data used are secondary data in the form of financial reports from 2012 to 2022. The methodology employed includes hypothesis testing using tests for normality, homogeneity, and one-way ANOVA. The research findings indicate that the Z-Score model is the most suitable and accurate model for predicting bankruptcy, with an accuracy rate of 85.53%. The S-Score model achieved an accuracy rate of 14.47%, while the X-Score model did not provide significant accuracy. The implications of the findings are that if the Z-Score model can be used to evaluate the financial health of banks and provide concrete preventive actions before bankruptcy occurs.
Keywords
Main Subjects
COPYRIGHTS
©2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
Agustina, A. N. (2022). Perbandingan kebangkrutan menggunakan model altman z score, model springate dan model zmijweski. Jurnal Ilmiah Poli Bisnis, 13–28.
Agwata, J. A. (2018). Using multiple approaches in the financial distress evaluation of companies listed in the manufacturing segment of the Nairobi Securities Exchange. African Journal of Accounting, Auditing and Finance, 6(2), 130-153.
Ali, H., Aysan, A. F., & Yousef, T. (2023). From tech hub to banking failure: exploring the implications of cbdcs on the destiny of Silicon Valley Bank. Available at SSRN 4411360.
Alibabaee, G., & Khanmohammadi, M. (2022). The study of the predictive power of meta-heuristic algorithms to provide a model for bankruptcy prediction. International Journal of Finance & Managerial Accounting, 7(26), 33-51.
Altman, E. I. (2018). A fifty-year retrospective on credit risk models, the altman z-score family of models and their applications to financial markets and managerial strategies. Journal of Credit Risk, 14(4).
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.
Ameri Siahoee, S., & Kordlouie, H. R. (2018). investigating the risk of paying loans to public and private companies using the logit model and comparing it with altman z (Case Study: A Private Bank in Iran). International Journal of Finance & Managerial Accounting, 3(11), 33-41.
Ayvaz, R. N., & Erkan, M. K. (2023). A review on financial failure models-the case of Manufacturing Industry. Business & Management Studies: An International Journal, 11(1), 375-399.
Bansal, R., Kashyap, S. K., Pranav, N. S. S. P. R., Krishna, U. S., Ahmad, Z., & Chauhan, A. (2020). the analysis of altman and ohlson model in predicting distress of indian companies: A Review. Solid State Technology, 63(6), 20725-20733.
Bărbuță-Mișu, N., & Madaleno, M. (2020). assessment of bankruptcy risk of large companies: European Countries Evolution Analysis. Journal of Risk and Financial Management, 13(3), 58.
Dailibas, D. R. (2021). analysis of bunkruptcy prediction with altman z-score, springate and zmijewski models Based Engineering Science. Turkish Journal of Computer and Mathematics Education Vol, 12(4), 1530-1537.
Fredy, H. (2018). The prediction of bankruptcy in the pulp and paper industry company listed in indonesia stock exchange on 2011-2016 period using z-score altman, springate and grover model. Jurnal South East Asia Journal of Contemporary Business, Economics and Law, 15(5).
Grikietytė, G., & Mačiulytė-Šniukienė, A. (2023). Applicability of bankruptcy probability assessment models to financial sector companies. Mokslas–Lietuvos ateitis/Science–Future of Lithuania, 15.
Gupta, V. (2023). Evaluating the accuracy of prediction in accounting-based models for companies in india under IBC. Journal of Commerce & Accounting Research, 12(1).
Irawan, J. L. (2023). Model prediksi kebangkrutan. Manajemen Keuangan Perusahaan Lanjutan, 18.
Karas, M., & Srbová, P. (2019). Predicting bankruptcy in construction business: traditional model validation and formulation of a new model. Journal Of International Studies, 12(1).
Laurila, K. (2020). Accuracy comparison of accounting-based bankruptcy prediction models of Springate (1978), Ohlson (1980) and Altman (2000) to US Manufacturing Companies 1990-2018.
Leisen, D., & Swan, P. L. (2023). Bank risk governance. Available at SSRN 4385351.
Manousaridis, C. O. (2017). Z-Altman's model effectiveness in bank failure prediction-The Case Of European Banks.
Melina, M., & Kalinggo, A. G. (2023). The effect of altman, grover and zmijewski models in predicting the financial distress of coal companies listed on the Indonesia Stock Exchange. International Journal Of Social, Policy And Law, 4(1), 69-79.
Prusak, B. (2017). Predicting corporate bankruptcies in poland and lithuania–comparative analysis. Intelektinė ekonomika, 11(1), 18-31.
Putri, A. S., Badri, R. E., Pranyoto, E., Susanti, S., & Lestari, W. R. (2020). Financial distress prediction; Springate, Zmijewski, and Grover method. In Proceeding International Conference on Information Technology and Business (pp. 1-8).
Ridwan, M., Pagalung, G., & Kara, M. (2022). Methods for measuring potential bankruptcy of Sharia Banking. Al-Mashrafiyah: Jurnal Ekonomi, Keuangan, dan Perbankan Syariah, 6(2), 81-90.
Shree, N., & Selvam, D. (2023). Prediction of bankruptcy of Indian Manufacturing Companies (construction) using Zmijewski model and Altman z score. In Advances in Management Research (pp. 116-142). Routledge.
Sitorus, Y. C. T. (2023). Bankruptcy prediction analysis with the Altman z–score, Springate and Zmijewski models in fisheries sub sector companies listed on the Indonesia Stock Exchange (IDX) For The 2019-2021 Period. Dinasti International Journal of Digital Business Management, 4(2), 319-327.
Steinskog, D. J., Tjøstheim, D. B., & Kvamstø, N. G. (2007). A cautionary note on the use of the kolmogorov–smirnov test for normality. Monthly Weather Review, 135(3), 1151-1157.
Sybirtsev, V., Mazhara, V., & Moskalenko, V. (2020). Analysis of discriminant models in forecasting bankruptcy of enterprises. bulletin of the Cherkasy Bohdan Khmelnytsky National University. Economic Sciences, (2).
Tiryaki, Y. (2021). Bankruptcy prediction models: A comparative analysis with polish data. Tallin University Of Technology.
Ullah, H., Wang, Z., Abbas, M. G., Zhang, F., Shahzad, U., & Mahmood, M. R. (2021). Association of financial distress and predicted bankruptcy: the case of Pakistani Banking Sector. The Journal of Asian Finance, Economics and Business, 8(1), 573-585.
Verlekar, R. P., & Kamat, M. (2019). An application and comparison of bankruptcy models in the Indian Banking Sector. International Journal Of Financial Management, 9(4).
Vu, H. T., & Tran, L. T. T. (2023). Research on comparing z-score, o-score and x-score models in analyzing factors affecting financial risk in medical companies listed on Vietnam Stock Market. In Contemporary Economic Issues in Asian Countries: Proceeding of CEIAC 2022, Volume 2 (pp. 99-126). Singapore: Springer Nature Singapore.
Yitnosumarto, S., & O'Neill, M. E. (1986). On Levene's Tests of Variance Homogeneity. Australian Journal of Statistics, 28(2), 230-241.
Zelenkov, Y., Fedorova, E., & Chekrizov, D. (2017). Two-step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 88, 393-401.
)