论文标题
基准测试机器学习模型以预测公司破产
Benchmarking Machine Learning Models to Predict Corporate Bankruptcy
论文作者
论文摘要
从1990年到2019年，我们使用2,585个破产的综合样本，在预测公开交易的美国公司的财务困境方面，我们基于各种机器学习模型的绩效。我们发现，在一年的预测中，梯度增强的树木的表现优于其他模型。可变排列测试表明，过量的股票收益，特质风险和相对大小是预测的更重要的变量。从公司文件中得出的文本功能并不能实质性提高绩效。在说明默认错误分类成本的信用竞争模型中，生存随机森林能够捕获大量的美元利润。
Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits.
