We apply a machine learning (ML) algorithm in order to predict bankruptcy rates among companies within the Spanish economy from 1992 to 2016. The model identifies some relevant variables when predicting bankruptcy: such as the ratio total liabilities to total assets or current liability to financial expenses along with size factors such as the log of sales. Additionally, the model allows us to analyse firms individually: the marginal contribution of a given variable to the firm’s prediction depends on all its other observed characteristics. This can be particularly useful in analysing case by case lending decisions within financial institutions. An exercise on the cost of extending the forecasting horizon up to 4 years ahead is also provided, as financial institutions are naturally interested in the early detection of bankruptcy. We also compare XGBoost to a number of ML models, such as a Logistic Model, Support Vector Machine, Neural Network, Random Forest and LightGBM.

我们使用机器学习（ML）算法来预测1992年至2016年西班牙经济内部公司的破产率。该模型在预测破产时确定了一些相关变量：例如总负债与总资产的比率或流动负债与财务的比率费用以及规模因素（例如销售记录）。此外，该模型还允许我们单独分析公司：给定变量对公司预测的边际贡献取决于其所有其他观察到的特征。这在分析金融机构内的逐案贷款决策时特别有用。还提供了将预测范围扩展到未来4年的成本的练习，因为金融机构自然对早期发现破产感兴趣。