Detecting Financial Distress : Discriminant Versus Logistic Regression Analysis

This study examines two statistical tests which are discriminant analysis and the logit model to predict the probability of financially distress companies. In addition this study also utilizes the usage of financial ratios as a predictor of a company in a state of financial distressed. The findings show that the logit model shows better prediction accuracy than the discriminant analysis. The logit model correctly classified 91.5 percent of the companies in the estimation sample and 90 percent for the holdout sample. However for discriminant mode the overall accuracy rate fix the estimation and the holdout samples are 84.5 percent respectively. For discriminant analysis there are three factors found to have significant discriminating power current ratio net income to total assets and sales to current assets. Similarly logit model also identified three factors but two of the factors (shareholders' equity to total liabilities and cashflow from financing to total liabilities) are different from those found in discriminant analysis. The only factor which is identified in both models is net income to total assets. The findings give clear understanding of the relevant factors that can cause financial distress. Hence companies could take immediate actions to avoid failure to the company