Robust Linear Discriminant Analysis with Modified One-Step M-Estimator Qn Scale for Classifying Financial Distress in Banks: Case Study

Nabila Putri; Kankan Parmikanti; Nurul  Gusriani

doi:10.24036/eksakta/vol25-iss02/515

EKSAKTA: Berkala Ilmiah Bidang MIPA

Open Access

Robust Linear Discriminant Analysis with Modified One-Step M-Estimator Qn Scale for Classifying Financial Distress in Banks: Case Study

Nabila Putri ¹

, Kankan Parmikanti ¹

, Nurul Gusriani ¹

¹ Department of Mathematics, Faculty of Mathematics and Natural Science (FMIPA), Universitas Padjadjaran, Bandung, Indonesia

Published 2024-06-06

Vol. 25 No. 02 (2024) PAGES 219-230

DOI 10.24036/eksakta/vol25-iss02/515

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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 â€˜distressâ€™ and â€˜non-distressâ€™ 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 Pressâ€™s Q value of 11.53846, indicating the methodâ€™s effectiveness in classifying real financial data.Â Â

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