论文标题
通过拓扑数据分析映射Altman的Z-Score模型来完善对公司失败的理解
Refining Understanding of Corporate Failure through a Topological Data Analysis Mapping of Altman's Z-Score Model
论文作者
论文摘要
公司失败引起了广泛的共鸣，使从业者寻找了解默认风险的理解。管理人员试图避免麻烦，信贷提供者避免有风险的贷款和投资者减轻损失。应用拓扑数据分析工具本文探讨了来自美国失败的公司是否沿Z分数模型提出的违约预测指标整齐地组织。公司在五维空间中表示为点云，每个预测因子的一个轴。使用球形映射器的云可视化表明，失败的公司并不是邻居。随着新的建模接近更好地预测企业失败的竞争，通常使用黑匣子来提供潜在的过度拟合模型，因此及时提醒了证明识别过程的重要性。价值添加到对参数空间故障中的位置的理解中，以及公司如何采取行动摆脱财务困扰。此外，贷方可能会在传统上被认为是破产危险但实际上坐落在没有发生故障的特征空间中的公司中的子集中找到机会。
Corporate failure resonates widely leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Firms are represented as a point cloud in a five dimensional space, one axis for each predictor. Visualising that cloud using Ball Mapper reveals failing firms are not often neighbours. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy but actually sit in characteristic spaces where failure has not occurred.
