Membandingkan Akurasi Financial Distress Berbasis Model Peramalan Kebangkrutan

  • Langgeng Prasetyo Utomo
https://doi.org/10.26533/jad.v3i1.548
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Abstract

This study aims to determine the differences between the Altman Model, the Zmijewski Model, the Springate Model and the Grover Model in predicting bankruptcy in companies that experienced losses in a row from 2013 to 2018. In this study, the sample technique used was purposive sampling by taking 24 samples of companies that suffered losses for two years in a row in the observation year were obtained. The analysis method used is One Way Anova. The results of this study indicate that the Altman Model, the Zmijewski Model, the Springate Model and the Grover Model differ in predicting company bankruptcy. Based on the Post-Hoc test and manual accuracy calculation shows that among the four models in this study

References

Aminian, H. & Olin, R., 2016. Investigate the ability of bankruptcy prediction models of Altman and Springate and Zmijewski and Grover in Tehran Stock Exchange. Mediterranean Journal of Social Sciences, 7(4), pp. 208-211.

Anon., n.d. Knowledge-Based Systems.

Chen, J., 2016. Financial distress prediction in China.". Review of Pacific Basin Financial Markets and Policies, 9(2), pp. 317-337.

Chen, W. S. & Yin, K. D., 2009. Using neural networks and data mining techniques for the financial distress prediction model. Expert systems with applications, 36(2), pp. 4075-4086.

Dietrich, J. R., 2015. Discussion of methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, pp. 83-86.

Doumpos, M. & Constantin, Z., 2014. A multicriteria discrimination method for the prediction of financial distress: The case of Greece. Multinational Finance Journal, 3.2(2), pp. 71-101.

Fanning, K. & Kenneth, C., 2014. A comparative analysis of artificial neural networks using financial distress prediction. Intelligent Systems in Accounting, Finance and Managemen, 34(4), pp. 241-252.

Gepp, A. & Kuldeep, K., 2014. The role of survival analysis in financial distress prediction. International research journal of finance and economics, 6(4), pp. 13-34.

Gunawan, P., Pamungkas, H. & Merinti, A. S., 2017. Comparison of Financial Distress Predictions with Altman, Grover and Zmijewski Models. Jurnal Akuntansi dan Investasi, 18(1), pp. 119-127.

Hopwood, W., McKEOWN & Jane, R., 2016. The sensitivity of financial distress prediction models to departures from normality. Contemporary Accounting Research, 27(1), pp. 284-298.

Husein, A., Fakhri, M. & Galuh, T. P., 2015. Precision of the models of Altman, Springate, Zmijewski, and Grover for predicting the financial distress. Journal of Economics, Business, & Accountancy Ventura, 17(3), pp. 405-416.

Keasey, K. & Robert, W., 2014. Financial distress prediction models: a review of their usefulness. British journal of Management, 17(1), pp. 289-102.

Lau, A. & Hing, L., 2014. A five-state financial distress prediction model. Journal of accounting research, pp. 127-138.

Liang, D., Chih, F. T. & Hsin, T. W., 73. The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73(1), pp. 208-297.

McDoll, F. & Zumanikah, A. A., 2014. Prediction of fetal anemia with Doppler measurement of the middle cerebral artery peak systolic velocity in pregnancies complicated by maternal blood group alloimmunization or parvovirus B19 infection. Ultrasound in Obstetrics and Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 18(1), pp. 232-236.

Rondes, 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, 282(1), pp. 131-171.

Sun, J. & Hui, L., 2018. Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications, 35(3), pp. 818-827

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Published
2020-06-08
Issue
Vol. 3 No. 1 (2020): Januari (2020) - Juni (2020)
Section
Section Editor

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