PREDICTING FINANCIAL DISTRESS FOR ROMANIAN COMPANIES
使用多种财务比率和四种分类技术（支持向量机、决策树、贝叶斯逻辑回归、Fisher线性分类器），为布加勒斯特证券交易所上市公司构建财务困境风险分类模型，其中前两种预测精度最高。
Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.