LOGIT回归模型对公司违约的预测探讨

为准确有效地预测企业违约的可能性, 以避免信用违约风险, 基于单一变量和多变量两种模型的分析,利用Logit 回归模型对企业的各变量和违约概率进行推断, 并以德国企业为例, 分析违约公司的共性和特性。对模型的交叉验证结果表明, 以Logit 模型衡量企业违约预测的统计方法整体精度达到70% 左右, 并能保持较高的可靠性和稳定性。违约预测可大大降低企业的违约风险, 在金融投资领域发挥重要作用。

Predicting Company Default with Logistic Regression

In order to predict the probability of company default accurately and efficiently, we take the logit regression model with single variable model and multi-variable model to test each enterprise and inference the probability of default. By taking German company as an example, to analysis the common and specific characteristics of the default company. The results show that, measuring the statistics approach to predict company default by logit model has a high degree of accuracy of 70% , maintains high reliability and stability. Default prediction can greatly reduce the risk of default in the field of financial investment with an important role.