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Predicting Micro-Enterprise Failures Using Data Mining Techniques

Institute of Statistics and Demography, Warsaw School of Economics, Warsaw 02-554, Poland
J. Risk Financial Manag. 2019, 12(1), 30;
Received: 30 December 2018 / Revised: 27 January 2019 / Accepted: 3 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Empirical Finance)
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Research analysis of small enterprises are still rare, due to lack of individual level data. Small enterprise failures are connected not only with their financial situation abut also with non-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 of 806 small enterprises were analyzed. Qualitative factors were used in modeling. Some simple and more 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 financial ratios but also non-financial factors matter for small enterprise survival, and (ii) advanced statistical models and data mining techniques only insignificantly increase the prediction accuracy of small enterprise failures. Results show that simple models are as good as more complex model. Data mining models tend to be overfitted. Most important financial ratios in predicting small enterprise failures were: operating profitability of assets, current assets turnover, capital ratio, coverage of short-term liabilities by equity, coverage of fixed assets by equity, and the share of net financial surplus in total liabilities. Among non-financial factors only two of them were important: the sector of activity and employment. View Full-Text
Keywords: data mining; bankruptcy prediction; financial and non-financial variables data mining; bankruptcy prediction; financial and non-financial variables

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Ptak-Chmielewska, A. Predicting Micro-Enterprise Failures Using Data Mining Techniques. J. Risk Financial Manag. 2019, 12, 30.

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