Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

The estimation of the return on firms’ investments – as to ISO 9001

The aim of this paper was to estimate the return on investment in QMS (quality management systems) certification undertaken in Portuguese firms, according to the ISO 9000 series. A total of 426 certified Portuguese firms were surveyed. The response rate was 61.03 percent. The different payback periods were validated through statistical analysis and the relationship between expected and perceived payback periods was discussed. This study suggests that a firm’s sector of activity, size and degree of internationalization are related to the length of the investment in QMS certification recovery period. Furthermore, our findings suggest, that the time taken to obtain the certification is not directly related to the economic component of the certification. The majority of Portuguese firms (58.9%) took up to three years to recoup their investment and 35.5% of companies said they had not yet recovered the initial investment made. The recoup of investment was measured by the increase in the number of customers and consequent volume of deliveries, improved profitability and productivity of the company, improvement of competitive position and performance (cost savings), reduction in the number of external complaints and internal defects/scrap, achievement of some important clientele, among others. We compared our work to similar studies undertaken in other countries. This paper provides a contribution to the research related to the return on investment for costs related to the certification QMS according to ISO 9000. This paper provides a valuable contribution to the field and is one of the first studies to undertake this type of analysis in Portugal.