Penelitian ini bertujuan untuk menganalisis kemampuan rasio keuangan dalam memprediksi kondisi financial distress pada perusahaan perbankan yang terdaftar di BEI dengan menggunakan rasio CAR, NPL, BOPO, ROA, ROE, dan LDR. Tehnik pengambilan sampel adalah purposive sampling dengan kreteria perusahaan yang berpotensi mengalami financial distress ditandai dengan perusahaan yang mengalami laba bersih negatif minimal selama dua tahun berturut-turut dan yang tidak. Metode analisis dengan regresi logistik dengan pooling data. Hasil penelitian menunjukkan bahwa NPL dapat memprediksi kondisi financial distress perusahaan perbankan, sedangkan CAR, BOPO, ROA, ROE, LDR tidak dapat memprediksi kondisi financial distress perusahaan perbankan.
This study aims to analyze financial ratios in predicting financial distress in banking companies listed on the IDX using the CAR, NPL, BOPO, ROA, ROE, and LDR ratios. The sampling technique is purposive sampling with the criteria of companies that have the potential to experience financial distress, characterized by companies that experience negative net income for at least two consecutive years and those that do not. The method of analysis is logistic regression with data pooling. The results show that NPL can predict the financial distress condition of a banking company, while CAR, BOPO, ROA, ROE, LDR cannot predict the financial distress condition of a banking company.
Keywords: Financial Distress, Financial Ratios, Banking, Logistic Regression
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.