Analisis Rasio Keuangan Untuk Memprediksi Kondisi Financial Distress Perusahaan Yang Terdaftar Di Bursa Efek Indonesia Dengan Menggunakan Regresi Logistik

It’s neccesary an early warning system to predict financial distress firm. Prediction model could be used as early warning system for user of financial ratio such as lender, investor, regulator, auditor, and management in decision making that relevant with likelihood of financial distress for listed companies. This research aims at providing empirical evidence on difference of financial ratio between financially distressed and non-financialy distressed firms. This research also examines the role of financial ratio in predicting the occurence of financial distress in the context of Indonesia Stock Exchange (ISX). Independent variable is financial ratio and the opposite, dependent variable is nominal data that is condition of financial distress (annotation by figure 1 for financial distress situation and 0 for non-financial distress situation). The financial ratios were used in this research about fifteen ratios that was grouped in profitability, financial leverage, liquidity, activity, and cash flow ratios.The analysis was used for examine these variables are Independen Sample ttest and Mann-Whitney-U test for mean difference and logistic regression model for role of financial ratio in predicting of financial distress. The samples are manufactur company listed in ISX in amount about 100 companies. The data became from account figure of financial statement in observed periods, 2005 to 2008.This research has resulted the finding that financial ratio of financially distressed firms is significantly diferrent with non-financialy distressed firms. Based on Hosmer and Lemeshow test, this research has also resulted two prediciton model that fit with observed data in two years and a year before financial distress situation. This research has also resulted the finding that profit margin, ROA, net working capital to total assets ratio, sales to assets ratio, CFTS, dan CFTL are significantly variables in determine financialy distressed firms. At last, the model has resulted ability to predict for clasiffied observed data in one of two firm categories about 96,1% for two years and 93,4% for a year before financial distress situation.