通过Altman的Z分数模型的拓扑数据分析映射来细化对公司失败的理解
Refining Understanding of Corporate Failure through a Topological Data Analysis Mapping of Altman's Z-Score Model
摘要：
企业破产引起了广泛的共鸣，让从业者寻求对违约风险的理解。管理者寻求避开麻烦，信贷提供者寻求避免高风险贷款，投资者寻求减轻损失。应用拓扑数据分析工具，本文探讨了失败的公司是否从美国整齐地组织沿Z分数模型提出的五个预测违约。企业被表示为一个点云在一个五维空间，一个轴为每个预测。使用Ball Mapper可视化云显示，失败的公司通常不是邻居。随着新的建模方法竞相更好地预测公司的失败，通常使用黑匣子来提供潜在的过度拟合模型，及时提醒人们证明识别过程的重要性。价值是增加到参数空间失败发生的地方，以及公司如何采取行动，以摆脱财务困境的理解。此外，放款人可能会在传统上被认为有破产危险但实际上处于尚未发生破产的特征空间的公司子集中找到机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
