Using Non-Financial Information to Predict Bankruptcy: A Study of Public Companies in Taiwan

Numerous researchers have studied bankruptcy prediction over the past sixty years. As a result, various theories have evolved in an effort to explain or distinguish between firms that have failed. The study uses non-financial information to predict the characteristics of failed firms. Comparing the results of this study and prior research shows that the results herein can provide a better prediction than those not including non-financial information. Introduction Corporate bankruptcy always brings about huge economic losses to management, stockholders, employees, customers, and others, together with a substantial social and economical cost to the nation. Therefore, a model predicting corporate failure would serve to reduce such losses by providing a pre-warning to these stakeholders. An early warning signal of probable failure will enable both management and investors to take preventive actions and shorten the length of time whereby losses are incurred. Thus, an accurate prediction of bankruptcy has become an important issue in finance. Numerous researchers have studied bankruptcy prediction over the past sixties years. As a result, various theories have evolved in an effort to explain or distinguish between firms that have failed. Beaver (1966) used a dichotomous classification test to determine the error rates a potential creditor would experience if he classified firms on the basis of their financial ratios as being failed or non-failed. Beaver was able to classify 78% of his sample firms as failures five years before they actually did fail. Altman (1968) used discriminant analysis to rank firms on the basis of a weighted combination of five ratios. His results were 95% effective in selecting future bankruptcies in the year prior to bankruptcy. Numerous follow-up studies have tried to further develop appropriate models by applying data-mining techniques including multivariate discriminant analysis, logistical regression analysis, probit analysis, genetic algorithms, neural networks, decision trees, and other statistical and computational methods. This study attempts to build a financial ratio model to predict any financial crisis to banks before it really happens. This model could help management to improve its financial structure and reduce the probability of financial distress. Furthermore, this model provides in-dcpth information to investors and creditors to examine their investment risk. Literature Reviews Ohlson (1980) used a logit of the maximum likelihood method to build and analyze a model, which sampled 105 failed companies and 2058 non-failed companies during 1970 to 1976. He set up 3 models from 9 explanatory variables to predict corporate failure. From this it was possible to identify four basic factors as being statistically significant in affecting probability of failure (within one year). These are: (1) the size of the company; (2) a measure(s) of the financial structure; (3) a measure(s) of performance; and (4) a measure(s) of current liquidity (the evidence regarding this factor is not as clear as compared to cases (1)-(3)). Ohlson's empirical study showed the prediction accuracy of these first 3 models to be 96.12%, 95.55%, and 92.84%, respectively. Keasey and Watson (1987) utilized a number of non-financial variables, either alone or in conjunction with financial ratios, and were able to predict a small company's failure more accurately than models based solely upon financial ratios. Keasey and Watson employed logit to build a prediction model. They sampled 73 failed companies and 73 non-failed companies from 1970 to 1983, using 28 financial variables and 18 non-financial variables in their study. For the logit functions presented below, the dependent variable is failure / non-failure and the sets of independent variables are as follows: Model 1: Financial Ratios only Model 2: Non-financial Information only Model 3: Financial Ratios and non-financial information The results indicate that marginally better predictions concerning a small company's failure may be obtained from non-financial data as compared to those which can be achieved from using traditional financial ratios. …