Many previous studies have examined classification models for enterprise financial distress. While earlier models were built using traditional statistical methods, other machine learning algorithms are being used for building models nowadays. Support Vector Machine (SVM) and Least Squares Support Vector Machine (LS-SVM) are relatively new machine learning algorithms. How are they different? This study builds two classification models for financial distress using these two methods and compares the differences between them. This study also combines discriminant analysis, t-test, and factor analysis with SVM and LS-SVM to build a two-step classification model of enterprise financial distress and discusses the predictive performance of the models built using the abovementioned feature selection methods.
The sample includes listed and OTC companies in Taiwan, which were observed during the period 2001–2011. The study uses financial ratios, index of intellectual capital, and corporate governance for building the models.
It was found that models based on feature selection by t-test could forecast enterprise financial distress more accurately, and both SVM and LS-SVM had similar classified ability for building prediction models of financial distress. These findings could be useful for future studies.
