Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values
用企业财务与缺失信息预测困境并识别僵尸企业AI 解读 ↓
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用企业财务与缺失信息预测困境并识别僵尸企业
- 研究问题
- 如何利用企业披露的财务信息及非随机缺失模式预测失败风险，并据持续处于高风险状态的预测结果识别僵尸企业？
- 方法与思路
- 作者在意大利企业数据上训练 XGBoost，结合误报与漏报选择风险阈值，将连续至少三年超过阈值的企业定义为僵尸。
- 材料陈述的贡献
- 摘要称缺失模式提供重要预测信息，所选模型优于多类基准，并发现僵尸企业平均规模更小、生产率更低且危机时增多。
- 阅读关注点
- 应核对失败标签、训练测试划分、阈值损失函数、缺失信息的时间可用性，以及僵尸定义与基准比较的稳健性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未提供预测指标及外部验证，所述企业特征关系不构成因果结论。
原文摘要
In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and non-random missing values of 304,906 firms active in Italy from 2008 to 2017. Then, we spot the highest financial distress conditional on predictions that lies above a threshold for which a combination of false positive rate (false prediction of firm failure) and false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that persist in a state of financial distress, i.e., their forecasts fall into the risk category above the threshold for at least three consecutive years. For our purpose, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our predictive model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z-scores and the Distance-to-Default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are on average less productive and smaller, and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies-e.g., optimal bankruptcy laws and information disclosure policies.