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All Journal El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Journal of Ekonomics, Finance, and Management Studies
Mawarda, Aisa Senta
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Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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Analisis Pengaruh Kinerja Keuangan dalam Memprediksi Financial Distress pada Perusahaan Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2019-2023 Mawarda, Aisa Senta; Enny Susilowati Mardjono; Ira Septriana
El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Vol. 6 No. 6 (2025): El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmal.v6i6.7491

Abstract

The aim of this study is to analyze the impact of financial performance in predicting financial distress in banking companies listed on the Indonesia Stock Exchange from 2019 to 2023. This study examines 46 banking companies registered on the Indonesia Stock Exchange during the period. The sample consists of 28 companies selected using purposive sampling. Data analysis is conducted using multiple linear regression.
Analisis Pengaruh Kinerja Keuangan dalam Memprediksi Financial Distress pada Perusahaan Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2019-2023 Mawarda, Aisa Senta; Enny Susilowati Mardjono; Ira Septriana
El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Vol. 6 No. 6 (2025): El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmal.v6i6.7491

Abstract

The aim of this study is to analyze the impact of financial performance in predicting financial distress in banking companies listed on the Indonesia Stock Exchange from 2019 to 2023. This study examines 46 banking companies registered on the Indonesia Stock Exchange during the period. The sample consists of 28 companies selected using purposive sampling. Data analysis is conducted using multiple linear regression.
Co-Authors Enny Susilowati Mardjono Ira Septriana