TY - JOUR
T1 - Bankruptcy Prediction Using Support Vector Machines and Feature Selection During the Recent Financial Crisis
AU - Dellepiane, Umberto
AU - Di Marcantonio, Michele
AU - Laghi, Enrico
AU - Renzi, Stefania
PY - 2015
Y1 - 2015
N2 - This study aims at identifying an optimal set of features for predicting firms bankruptcy events in the current macroeconomic context. To this aim, among many financial features, we propose new country-specific factors which consider the macroeconomic conditions of the countries where firms operate. Our forecasting model is based on Support Vector Machines (SVMs), which are tools employed in supervised learning. Firstly, starting from a wide set of variables commonly used for bankruptcy prediction we assess the general effectiveness of SVMs also in comparison with the performances of other commonly used methods. Secondly, we try to improve the accuracy of forecasts by selecting optimal subsets of variables through a feature selection method. The results show that, in the current socio-economic context, the conjunct use of SVMs and the proposed feature selection technique significantly improves the accuracy of bankruptcy predictions compared to the performance of the other methods examined. Furthermore, we show that the proposed country-specific factors are relevant information for predicting the failure of firms and that most of the ratios proposed by Altman in 1968 are still relevant nowadays.
AB - This study aims at identifying an optimal set of features for predicting firms bankruptcy events in the current macroeconomic context. To this aim, among many financial features, we propose new country-specific factors which consider the macroeconomic conditions of the countries where firms operate. Our forecasting model is based on Support Vector Machines (SVMs), which are tools employed in supervised learning. Firstly, starting from a wide set of variables commonly used for bankruptcy prediction we assess the general effectiveness of SVMs also in comparison with the performances of other commonly used methods. Secondly, we try to improve the accuracy of forecasts by selecting optimal subsets of variables through a feature selection method. The results show that, in the current socio-economic context, the conjunct use of SVMs and the proposed feature selection technique significantly improves the accuracy of bankruptcy predictions compared to the performance of the other methods examined. Furthermore, we show that the proposed country-specific factors are relevant information for predicting the failure of firms and that most of the ratios proposed by Altman in 1968 are still relevant nowadays.
KW - bankruptcy prediction, default risk, credit scoring, support vector machines, feature selection, data mining, country-specific factors
KW - bankruptcy prediction, default risk, credit scoring, support vector machines, feature selection, data mining, country-specific factors
UR - http://hdl.handle.net/10807/67769
UR - http://dx.doi.org/10.5539/ijef.v7n8p
U2 - 10.5539/ijef.v7n8p
DO - 10.5539/ijef.v7n8p
M3 - Article
SN - 1916-971X
SP - 182
EP - 196
JO - INTERNATIONAL JOURNAL OF ECONOMICS AND FINANCE
JF - INTERNATIONAL JOURNAL OF ECONOMICS AND FINANCE
ER -