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Submitted
May 16, 2024
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Jun 4, 2024
Published
Jun 6, 2024
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Abstract

The COVID-19 pandemic has significantly disrupted the banking sector, leading to a decline in profit growth as an indicator of financial distress. Bank financial health can be evaluated using the RGEC (Risk Profile, Good Corporate Governance, Earnings, Capital) analysis. While Linear Discriminant Analysis (LDA) ideally requires normality and homogeneity of covariance matrices, financial data often fail to meet these assumptions. Therefore, this study employs robust linear discriminant analysis using the Modified One-Step M-Estimator with Qn scale estimator (MOM-Qn) to classify ‘distress’ and ‘non-distress’ bank conditions. Given these challenges, this study acts as a preventive measure for banks to evaluate financial health simultaneously. The objective is to provide a robust discriminant function for more accurate and stable classification, particularly in the presence of outliers. It focuses on conventional private banks listed on the Indonesia Stock Exchange (IDX) during December 2021-2022. The results show a classification accuracy of 69.23% and a Press’s Q value of 11.53846, indicating the method’s effectiveness in classifying real financial data.  

Keywords

Robust Linear Discriminant Analysis Modified One-step M-estimator Qn estimator

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How to Cite
1.
Nabila Putri, Parmikanti K, Gusriani N. Robust Linear Discriminant Analysis with Modified One-Step M-Estimator Qn Scale for Classifying Financial Distress in Banks: Case Study . EKSAKTA [Internet]. 2024Jun.6 [cited 2024Nov.21];25(02):219-30. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/515

References

  1. Nazarudin, J., Gusriani, N., Parmikanti, K., & Susanti, S. (2023). Application of Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) Model in Forecasting the LQ45 Stock Price Return. Eksakta: Berkala Ilmiah Bidang MIPA, 24(2), 271–284.
  2. Darjana, D., Wiryono, S. K., & Koesrindartoto, D. P. (2022). The COVID-19 Pandemic Impact on Banking Sector. Asian Economics Letters, 3(3).
  3. Sutra, F. M., & Mais, R. G. (2019). Faktor-Faktor yang Mempengaruhi Financial Distress dengan Pendekatan Altman Z-Score pada Perusahaan Pertambangan yang Terdaftar di Bursa Efek Indonesia Tahun 2015-2017. Jurnal Akuntansi dan Manajemen, 16(1), 34–72.
  4. Bank Indonesia. (2011). Peraturan Bank Indonesia Nomor: 13/1/2011 tentang Penilaian Tingkat Kesehatan Bank Umum.
  5. Purnamasari, D., Suwandi, T., Rahayu, E. P., Kesehatan Hangtuah Pekanbaru, I., & Rahayu, E. P. (2020). Multivariate Analysis on The Determinants of Work Fatigue Factors for Nurses Inpatient Care at RSUD Arifin Achmad Hospital Pekanbaru. Eksakta: Berkala Ilmiah Bidang MIPA, 21(1), 59–69.
  6. Hartono, A., Dewi, L. A., Yuniarti, E., Putri, S. T. H., Harahap, T. S., & Hartono, A. (2023). Machine Learning Classification for Detecting Heart Disease with K-NN Algorithm, Decision Tree and Random Forest. Eksakta: Berkala Ilmiah Bidang MIPA, 24(4), 513–522.
  7. Tahmasebi, R., Rostamy, A. A. A., Khorshidi, A., & Sadeghi Sharif, S. J. (2020). A data mining approach to predict companies’ financial distress. International Journal of Financial Engineering, 7(3), 2050031.
  8. Wieprow, J., & Gawlik, A. (2021). The use of discriminant analysis to assess the risk of bankruptcy of enterprises in crisis conditions using the example of the tourism sector in Poland. Risks, 9(4).
  9. Tjahaya, M. S., & Tinungki, G. M. (2022). Analisis Diskriminan Linear Robust Dengan Metode Winsorized Modified One-Step M-Estimator. Journal of Statistics and Its Application, 3(1), 1–13.
  10. Alrawashdeh, M. J., Radwan, T. R., & Abunawas, K. A. (2018). Performance of Linear Discriminant Analysis Using Different Robust Methods. European Journal of Pure and Applied Mathematics, 11(1), 284–298.
  11. Auguin, N., Morales-Jimenez, D., & McKay, M. R. (2019). Robust Linear Discriminant Analysis using Tyler’s Estimator: Asymptotic Performance Characterization. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5317–5321.
  12. Yahaya, S., Lim, Y. F., Ali, H., & Omar, Z. (2016). Robust Linear Discriminant Analysis with Automatic Trimmed Mean. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(10), 1–3.
  13. Melik, H. N., Ahad, N. A., & Yahaya, S. S. S. (2018). Modified one-step M-estimator with Robust Scale Estimator for Multivariate Data. Journal of Engineering and Applied Sciences, 13(24), 10396–10400.
  14. Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2023). Multivariate Analysis an Application-Oriented introduction. Springer Gabler.
  15. Dewi, K., Gusriani, N., & Parmikanti, K. (2023). Factors Affecting the Number of Infant Morality Cases in West Java for the 2019-2020 Period using Generalized Poisson Regression (GPR). Eksakta: Berkala Ilmiah Bidang MIPA, 24(2), 259–270.
  16. Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Statistics for Business and Economics (11th ed.). South Western College Pub.
  17. Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Pearson Prentice Hall.
  18. Achiar, A. L. M., Aidi, M. N., & Kurnia, A. (2023). Identification of Atherosclerosis Based on The Differences in Cholesterol and Creatinine in Indonesia with Multivariate Analysis of Variance. Eksakta: Berkala Ilmiah Bidang MIPA, 24(3), 315–329.
  19. Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R Package for Assessing Multivariate Normality. R Journal, 6(2), 151–162.
  20. Wilcox, R. R. (2003). Applying Contemporary Statistical Techniques. Academic Press.
  21. Abdul Rahman, A., Syed Yahaya, S. S., & Atta, A. M. A. (2020). Robustification of CUSUM control structure for monitoring location shift of skewed distributions based on modified one-step M-estimator. Communications in Statistics - Simulation and Computation, 49(11), 3001–3018.
  22. Adekeye, K. S., Adewara, J. A., Aako, O. L., & Olaomi, J. O. (2021). Performance of median absolute deviation and some alternatives to median absolute deviation control charts for skewed and heavily tailed process. Quality and Reliability Engineering International, 37(8), 3431–3440.
  23. Rousseeuw, P., & Croux, C. (1993). Alternatives to the Mean Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273–1283.
  24. Pang, Y. S., Ahad, N. A., & Yahaya, S. S. S. (2022). Robust Linear Discriminant Rule Using Double Trimming Location Estimator with Robust Mahalanobis Squared Distance. Pertanika Journal of Science & Technology, 30(4), 2393–2406.
  25. Ahad, N. A., Pang, Y. S., Yahaya, S. S. S., & Abdullah, S. (2023). Robust multiple discriminant rule with Hodges-Lehmann in handling equal proportion of cellwise-casewise outliers. AIP Conference Proceedings, 2896(1).
  26. Lim, Y. F., Yahaya, S. S. S., & Ali, H. (2016). Winsorization on linear discriminant analysis. In The 4th International Conference on Quantitative Sciences and Its Applications. American Institute of Physics Inc.
  27. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multiple Discriminant Analysis. In Multivariate Data Analysis (7th ed., p. 267). Pearson Prentice Hall.
  28. Destriana, M., Gusriani, N., & Irianingsih, I. (2018). Klasifikasi Status Kinerja Bank yang Terdaftar di BEI dengan Pendekatan Winsorized Modified One-Step M-Estimator. Jurnal Matematika Integratif, 14(2), 133–140.
  29. Fakhruddin, A. H., & Aji, T. S. (2024). Risk-Based Bank Rating (RBBR) Method Using Risk Profile, Good Corporate Governance, Earning, and Capital (RGEC) Factors as an Assessment of Bank Health: Case Study of PT Bank Pembangunan Daerah Jawa Timur Tbk Period 2018-2022. International Journal of Social Science and Human Research, 7(1), 151–159.
  30. Abdullah, J., Hasan, W., & Dusa, S. Y. (2021). Analysis of Capital Adequacy Ratio (CAR), Non-Performing Loan (NPL), and Net Interest Margin (NIM) in Predicting Financial Distress in Financial Reports of PT. Bank Rakyat Indonesia (Persero) Tbk. Journal of Economics, Finance and Accounting Studies (JEFAS), 3(2), 81–90.