Financial Distress Prediction of China Listed Companies Based on SD Model and LSSVM

Financial distress prediction has drown a lot of research interests in previous literature, and many studies have shown that artificial intelligence techniques achieve better performance than traditional statistical ones. In this paper, we use Least Squares Support Vector Machine (LSSVM) to carry through empirical study for financial distress prediction of China listed companies. The independent variables as input to LSSVM classifier are deduced by Higgins´s sustainable Development (SD) model. Result shows that the combination of SD model and LSSVM has good classification abilities.