Zmijewski, Taffler, Springate dan Grover Model : Analisis Model Prediksi Kebangkrutan

Penulis

  • M Iswahyudi Universitas 17 Agustus 1945 Banyuwangi

DOI:

https://doi.org/10.23887/ekuitas.v10i1.46831

Kata Kunci:

Grover, financial distress, SOEs, springate, taffler, zmijewski

Abstrak

This study aims to analyze the differences between the Zmijewski model, the Taffler model, the Taffler model and the Grover model in predicting financial distress in SOEs. BUMNs were chosen because BUMNs that were considered not to make a big contribution to the state budget were even reported to have gone bankrupt. The object of research is BUMN that has listed its name on the Indonesia Stock Exchange from 2016 - 2020. With a total of 16 companies, 90 total observations are obtained. The variable size was measured by the cut-off value in the Zmijewski (X-Score), Taffler (T-Score), Springate (S-Score) and Grover (G-Score) models. The results of the analysis show that the x-score, t-score and s-score show the same results regarding the predictions of 2 SOEs experiencing financial difficulties (GIAA and KRAS), these three models emphasize the company's ability to fulfill obligations to third parties. In addition, Zmijewski (X-Score), Taffler (T-Score), Springate (S-Score) also show a mismatch when used as a predictor of financial failure in the banking sector. Meanwhile, the g-score is a less sensitive model in predicting bankruptcy because it emphasizes the comparison of working capital and profit.

Referensi

Altman, E. . (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ, 23(4), 589–609. http://doi.org/https://doi.org/10.2307/2978933

Altman, E. ., & Saunders. (1997). Credit risk measurement: developments over the last 20 years. J Bank and Finance, 21(11), 1721–1742. http://doi.org/https://doi.org/10.1016/ S0378-4266(97)00036-8

Aviantara, R. (2021). Scoring the financial distress and the financial statement fraud of Garuda Indonesia with “DDCC” as the financial solutions. Journal of Modelling in Management, 1(1). http://doi.org/https://doi.org/10.1108/JM2-01-2020-0017

Bai, Q., & Tian, S. (2020). Innovate or die : Corporate innovation and bankruptcy forecasts. Journal of Empirical Finance, 59(September), 88–108. http://doi.org/10.1016/j.jempfin.2020.09.002

Cattleyana, D., Iqbal, A., & Asyriana, S. (2020). Analisis Kesehatan Keuangan Perusahaan Plat Merah Tahun 2009 - 2018. Jurnal Sains Manajemen Dan Bisnis Indonesia, 10(2), 187–193.

Dasgupta, K., & Mason, B. J. (2020). The effect of interest rate caps on bankruptcy : Synthetic control evidence from recent payday lending bans. Journal of Banking and Finance, 119, 105917. http://doi.org/10.1016/j.jbankfin.2020.105917

Iswahyudi, M., & Saputra, P. E. (2020). Sebuah Analisa Fraud Triangle “Determinan Fraud Laporan Keuangan Perusahaan Plat Merah .” Jurnal Ekonomi Manajemen Bisnis Akuntansi, 8(4), 1101–1109.

Kadek, N., Pardiastuti, K., & Herawati, N. T. (2020). Penilaian Kinerja Manajemen melalui Analisis Laporan Keuangan. Ekuitas : Jurnal Pendidikan Ekonomi, 8(2), 129–136.

Kang, T. H., James, S. D., & Fabian, F. (2020). Real options and strategic bankruptcy. Journal of Business Research, 117(May), 152–162. http://doi.org/10.1016/j.jbusres.2020.05.057

Karas, M., & Srbová, P. (2019). Predicting bankruptcy in construction business : Traditional model validation and formulation of a new model, 12(201 9), 283–296. http://doi.org/10.14254/2071-8330.2019/12-1/19

Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. http://doi.org/10.1016/j.dss.2020.113429

Munawaroh. (2019). Zmijewski dan Springate : Analisis Diskriminan dalam Memprediksi Financial Distress. Akuisisi Jurnal Akuntansi, 15(1), 1–8.

Novi, R. K., & Irianto, G. (2010). Penerapan Model Beneish dan Model Altman dalam Pendeteksian Kecurangan Laporan Keuangan. Jurnal Akuntansi Multiparadigma, 1(2), 155–172.

Salehi, M., & Pour, M. D. (2016). Bankruptcy prediction of listed companies in Tehran Stock Exchange. International Journal of Law and Management, 58(5). http://doi.org/http://dx.doi.org/10.1108/IJLMA-05-2015-0023

Springate, G. . (1978). Predicting the Possibility of Failure in a Canadian Firm. Burnaby: Simon Fraser University.

Taffler, R., & Tissaw, H. (1977). Going, going, gone—Four factors which predict. Accountancy, 88, 50–54.

Tian, S., Yu. Y, & H, G. (2015). Variable selection and corporate bankruptcy forecasts,. J. Bank Finance, 52, 89–100. http://doi.org/https://doi.org/10.1016/j. jbankfin.2014.12.003

Travis, V. M. A. M. L., & Venkatesan, P. (2015). A Reference Model for Business Intelligence toPredict Bankruptcy. Journal of Enterprise Information Management, 28(2).

Vavrek, R., & Kravˇ, I. (2021). Evaluating the Financial Health of Agricultural Enterprises in the Conditions of the Slovak Republic Using Bankruptcy Models.

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2022-06-28

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