Analisis Rasio Keuangan Terhadap Kondisi Financial Distress pada Perusahaan Manufaktur
Abstract
Bankruptcy does not just happen, but starts from financial difficulties and liquidation, which is commonly referred to as a financial distress. In predicting financial distress, measurements can be made with certain ratios. This study aims to examine the role of financial ratios in predicting financial distress. The sample consists of 24 manufacturing companies on the Indonesia Stock Exchange. The data used are the 2013-2016 financial statements. The 2015-2016 financial reports are used as determinants of financial distress, and the 2013-2014 reports are the data processed in this study. Logistic regression is an analytical tool used to test hypotheses. In this case, the financial ratios of the Altman model and the financial ratios of cash flows will be used to predict the company's financial distress. The results of this study indicate that the ratio of EBITTA, WCTA, RETA, CFOTA has a significant negative effect in predicting financial distress. Meanwhile, the MVETL and SATA financial ratios have no effect in predicting the company's financial distress. The classification power of prediction accuracy in this research model is 83.3%.
References
Ashraf, S., & Elisabete, G. S. F. (2019). Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress ? 2005. https://doi.org/10.3390/jrfm12020055
Assaji, J. P., & Machmuddah, Z. (2019). Rasio Keuangan Dan Prediksi Financial Distress. Jurnal Penelitan Ekonomi Dan Bisnis, 2(2), 58–67. https://doi.org/10.33633/jpeb.v2i2.2042
Cahyani, D. M., & Diantini, N. N. A. (2016). Peranan Good Corporate Governance Dalam Memprediksi Financial Distress. Matrik : Jurnal Manajemen, Strategi Bisnis Dan Kewirausahaan, 10(2), 144. https://doi.org/10.24843/matrik:jmbk.2016.v10.i02.p05
Khan, M. M., & Raj, K. B. (2020). LIQUIDITY-PROFITABILITY ANALYSIS & PREDICTION OF BANKRUPTCY- A STUDY OF SELECT TELECOM COMPANIES. 7(3), 307–316.
Krusita, N. W. Y., & Wiagustini, N. L. P. (2019). Prediksi Financial Distress Menggunakan Model Zmijewski Dan Model Grover Pada Perusahaan Migas Di Bei. E-Jurnal Manajemen Universitas Udayana, 8(5), 2891. https://doi.org/10.24843/ejmunud.2019.v08.i05.p11
Nailufar, F., Sufitrayati, & Badaruddin. (2018). Pengaruh Laba dan Arus Kas Terhadap Kondisi Financial Distress Pada Perusahaan Non Bank Yang Terdaftar Di Bursa Efek Indonesia. Jurnal Penelitian Ekonomi Akuntansi (JENSI), 2(2), 147–162.
Patunrui, K. I. A., & Yati, S. (2017). Analisis Penilaian Financial Distress Menggunakan Model Altman ( Z- Score ) Pada Perusahaan Farmasi Yang Terdaftar di Bursa Efek Indonesia Periode 2013-2015. Jurnal Akuntansi, Ekonomi Dan Manajemen Bisnis, 5(1), 55–71.
Paulalengan, A. J., & Dwi Ratnadi, N. M. (2019). Pengaruh Financial Distress, Umur Perusahaan, dan Good Corporate Governance pada Kecepatan Publikasi Laporan Keuangan Tahunan. E-Jurnal Akuntansi, 27, 2010. https://doi.org/10.24843/eja.2019.v27.i03.p14
Prasetiono, F. D. A. (2011). FINANCIAL DISTRESS PERUSAHAAN MANUFAKTUR. Jurnal Dinamika Ekonomi Dan Bisnis, 8, 1–14.
Primasari, N. S. (2018). ANALISIS ALTMAN Z-SCORE, GROVER SCORE, SPRINGATE, DAN ZMIJEWSKI SEBAGAI SIGNALING FINANCIAL DISTRESS (Studi Empiris Industri Barang-Barang Konsumsi di Indonesia). Accounting and Management Journal, 1(1), 23–43. https://doi.org/10.33086/amj.v1i1.70
Sunarji, A., & Sufyani, M. (2017). Rasio Keuangan Dalam Memprediksi Kondisi Financial Distress Perusahaan Tekstil Dan Garmen. Jurnal Riset Bisnis Dan Manajemen, 10(2), 11. https://doi.org/10.23969/jrbm.v10i2.445
Waqas, H., & Md-rus, R. (2018). Cogent Economics & Finance Predicting financial distress : Importance of accounting and firm-specific market variables for Pakistan ’ s listed firms Predicting financial distress : Importance of accounting and firm-specific market variables for Pakistan ’ s listed firms. Cogent Economics & Finance, 6(1), 1–16. https://doi.org/10.1080/23322039.2018.1545739
This work is licensed under a Creative Commons Attribution 4.0 International License.
