Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress
提出一种新的分类方法：递归分割算法，用于财务分析，并与判别分析在财务困境预测中比较，发现递归分割在多数样本和验证中表现更优。
The purpose of this study is to present a new classification procedure, Recursive Partitioning Algorithm (RPA), for financial analysis and to compare it with discriminant analysis within the context of firm financial distress. RPA is a computerized, nonparametric technique based on pattern recognition which has attributes of both the classical univariate classification approach and multivariate procedures. RPA is found to outperform discriminant analysis in most original sample and holdout comparisons. We also observe that additional information can be derived by assessing both RPA and discriminant analysis results.