论文标题
公司通过机器学习的默认预测
Firms Default Prediction with Machine Learning
论文作者
论文摘要
多年来，学术界和从业人员使用统计和机器学习方法研究了用于预测公司破产的模型。较早的迹象表明，一家公司有财务困难，最终可能破产的是\ emph {默认}，这很松散地说，该公司一直在将其贷款偿还银行系统的困难。公司默认状态在技术上不是失败的，而是与银行贷款政策非常相关，并且经常预测公司的失败。我们的研究首次根据我们的知识使用了意大利意大利银行中央信贷登记册的非常大的颗粒信贷数据数据库，其中包含有关所有意大利公司过去对整个意大利银行系统的行为的信息，以使用机器学习技术来预测其默认情况。此外，我们将这些数据与有关公司公共资产负债表数据的其他信息结合在一起。我们发现集合技术和随机森林提供了最佳结果，从而证实了Barboza等人的发现。 （专家Syst。Appl。，2017）。
Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in \emph{default}, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies' past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies' public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017).
