Predicting Micro-Enterprise Failures Using Data Mining Techniques

摘要

Research analysis of small enterprises are still rare, due to lack of individual leveldata. Small enterprise failures are connected not only with their financial situation abut also withnon-financial factors. In recent research we tend to apply more and more complex models. However,it is not so obvious that increasing complexity increases the effectiveness. In this paper the sample of806 small enterprises were analyzed. Qualitative factors were used in modeling. Some simple andmore complex models were estimated, such as logistic regression, decision trees, neural networks,gradient boosting, and support vector machines. Two hypothesis were verified: (i) not only financialratios but also non-financial factors matter for small enterprise survival, and (ii) advanced statisticalmodels and data mining techniques only insignificantly increase the prediction accuracy of smallenterprise failures. Results show that simple models are as good as more complex model. Datamining models tend to be overfitted. Most important financial ratios in predicting small enterprisefailures were: operating profitability of assets, current assets turnover, capital ratio, coverage ofshort-term liabilities by equity, coverage of fixed assets by equity, and the share of net financialsurplus in total liabilities. Among non-financial factors only two of them were important: the sectorof activity and employment.