Pendekatan Model Regresi Logistik Untuk Memprediksi Financial Distress Pada Perusahaan Pertambangan Yang Terdaftar Di Bursa Efek Indonesia - Liza Novietta; Kersna Minan (Jurnal Akuntansi dan Keuangan Kontemporer (JAKK), 2020)
The economic conditions in Indonesia in year 2017 are faced with various challenges that are not easy. As for the domestic challenges in related to slowing economic growth, the expected fiscal deficit will still be substantial, the rising of foreign borrowings, as well as the low credit growth and followed by the increasing of non-performing loans. These conditions have affected many companies in Indonesia, especially the mining industries companies listed in Indonesia Stock Exchange. If not followed up with good strategies, many companies above will experience financial distress. this research aims to analyze the effects of financial ratios such as liquidity ratios (CACL, WCTA, CATA), profitability ratio (ROA), leverage ratio (TLTA), activity ratio (STA), market ratio (MBV) and firm growth (TAG, SG and OPG) to financial distress prediction that experienced by the mining indutry companies listed in Indonesian Stock Exchange for periode of observation year 2011 up to year 2016, for 6 (six) years of observation. Data analysis method used is binary logistic regression model. The result of this research are only the firm growth consisted of Total Assets Growth (TAG) and SG (Sales Growth) partially has negative and significant influence to the financial distress prediction in mining industries companies listed in Indonesian Stock Exchange for the period of observation year 2011 – 2016. Meanwhile, the Operating Profit Growth (OPG) has positive and significant influence to financial distress prediction. In the other hand, liquidity ratio (CACL, WCTA, CATA), profitability ratio (ROA) and leverage ratio (TLTA) has negatif and unsignificant influence to companies’ financial distress. Activity ratio (STA) and market ratio (MBV) has positive and unsignificant influence to financial distress prediction.