Abstract

The dramatic deterioration in a corporate’s profitability not only threatens its own interests, employees, and investors, but can also impact external entities and people through financial losses and high risk exposure. Thus, in today’s turbulent market environments an essential issue arises as to how to set up an effective pre-warning model that provides managers with specific avenues to avoid financial troubles from getting worse and offers investors useful directions to adjust their investment portfolios. However, extant forecasting models are not yet capable of fully explaining the relationships between past and future performances, which may be due to the omission of some critical information. To capture the multidimensional nature of performance assessment, this study extends a singular data envelopment analysis (DEA) specification to multiple DEA specifications and further incorporates them with a risk-adjusted metric so as to present an overarching reflection of corporates’ operations. To make the outcome much more accessible to non-specialists, we utilize a visualization technique to represent the data’s main structure and then feed the analyzed data into a twin parametric-margin support vector machine (TPSVM) to construct the forecasting model. Due to the obscure nature of the SVM-based model, this study executes the multiple instances learning (MIL) algorithm to extract the inherent decision logics and to represent them in human readable way. After examining it with real cases, the proposed model is a promising alternative for performance assessment and forecasting.

摘要

公司盈利能力的急剧恶化不仅威胁其自身利益，员工和投资者，而且还可能因财务损失和高风险敞口而影响外部实体和人员。因此，在当今动荡的市场环境中，出现了一个基本问题，即如何建立有效的预警模型，该模型为管理人员提供了特定的途径来避免财务问题恶化，并为投资者提供了调整其投资组合的有用指导。但是，现存的预测模型还不能完全解释过去和将来表现之间的关系，这可能是由于缺少一些关键信息所致。为了捕捉绩效评估的多维性质，这项研究将单一数据包络分析（DEA）规范扩展到多个DEA规范，并进一步将它们与风险调整指标结合在一起，从而全面反映了公司的运营状况。为了使非专家更容易获得结果，我们使用可视化技术来表示数据的主要结构，然后将分析后的数据输入到双参数保证金支持向量机（TPSVM）中以构建预测模型。由于基于SVM的模型的晦涩本质，本研究执行了多实例学习（MIL）算法，以提取内在的决策逻辑并以人类可读的方式表示它们。在对实际案例进行检查之后，提出的模型是性能评估和预测的有前途的替代方法。