Predicting Financial Distress in Indonesian Manufacturing Industry

We attempt to develop and evaluate financial distress prediction models using financial ratios derived from financial statements of companies in Indonesian manufacturing industry. The samples are manufacturing companies listed in Indonesian Stock Exchange during 2003-2011. The models employ two kinds of methods : traditional statistical modeling (Logistic Regression and Discriminant Analysis) and modern modeling tool (Neural Network). We evaluate 23 financial ratios (that measure a company's liquidity, profitability, leverage, and cash position) and are able to identify a set of ratios that significantly contribute to financial distress condition of the companies in sample group. By utilizing those ratios, prediction models are developed and evaluated based on accuracy and error rates to determine the best model. The result shows that the ratios identified by logistic regression and the model built on that basis is more appropriate than those derived from discriminant analysis. The research also shows that although the best performing prediction model is a neural network model, but we have no solid proof of neural network's absolute superiority over traditional modeling methods.