Bankruptcy Prediction Using Support Vector Machines and Feature Selection During the Recent Financial Crisis

Abstract

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.

Distressed Chinese firm prediction with discretized data

“…Härdle et al (2009) reported on exploring the suitability of smooth support vector machines to examine the important factors on influencing the precision of prediction. Dellepiane et al (2015) propose new country-specific factors using SVM as the forecasting model and assess the general effectiveness of SVMs by comparing it with the performances of other commonly used methods.…”

Predicting non-life insurer's insolvency using non-kernel fuzzy quadratic surface support vector machines

“…In the last decade, SVM approach has been widely applied in insolvency prediction to improve the performance [19,23,26,30,31,32]. In general, the numerical results demonstrate that SVM outperforms neural networks and statistical methods in predicting financial failures [9]. However, a kernel function is always necessary for the nonlinearly separable data sets in the classical SVM model [25].…”

“…Note that feature selection aims to find a subset of input features which performs as well as all available features. Several researchers have noticed this problem and proposed some algorithms for searching optimal subsets of predictors [9,11]. Finally, we conduct some numerical experiments based on the real data of non-life insurers from USA to show the predictive power and efficiency of our proposed method compared with other benchmark methods.…”

“…However, not all the provided variables are actually useful in the forecast. Some unrelated factors can even worsen the performance [9]. Besides, different orders of variables may bias the model and affects the accuracy of prediction.…”

“…As usual, we choose the variables with IV bigger than 0.02 into the final model. It is worth pointing out that, among these 38 variables ( [15], traditional support vector machine (SVM) [9] and multivariate statistical methods such as multivariate discriminant analysis (MDA) [14], logistic regression analysis (LRA) [5] and stepwise logistic regression (SLR) [33]. ANN is a parallelized computational model in computer science and other research disciplines, which is able to learn from examples and adapt to new situations.…”