Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach
Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach
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DOI：
10.3390/jrfm15030131
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发表时间：
2022-03
影响因子：
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通讯作者：
Meng-Meng Tan-Meng;Dongling Xu;Jianbo Yang
中科院分区：
文献类型：
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作者：
Meng-Meng Tan-Meng;Dongling Xu;Jianbo Yang
本文建立了一种新的风险评估模型，并将证据推理方法应用于英国知识密集型服务（KIS）企业的失败风险评估。仅凭一般的量化财务指标（如经营能力或盈利能力）无法全面评价KIS行业公司破产的概率。这个新模型将定量财务指标与宏观经济变量、行业因素和公司非财务标准相结合，以进行稳健和平衡的风险分析。它以企业风险管理（ERM）理论为基础，作为风险管理的一个重要方面，可以用来分析公司失败的可能性。本研究在统计分析的基础上，为宏观因素和行业因素的选择提供了新的视角。另一项创新与如何构建变量的边际效用函数以及如何在分布式评估框架中处理不完美数据有关。本文首次采用似然分析方法代替主观判断，将观察到的数据转化为概率分布，在ER框架下对KIS行业的企业破产进行数据驱动风险分析，使模型更具可解释性和信息量。该模型可用于提供早期预警机制，以帮助利益相关者做出投资和其他决策。
In this study, a new risk assessment model is developed and the evidence reasoning (ER) approach is applied to assess failure risk of knowledge-intensive services (KIS) corporates in the UK. General quantitative financial indicators alone (e.g., operational capability or profitability) cannot comprehensively evaluate the probability of company bankruptcy in the KIS sector. This new model combines quantitative financial indicators with macroeconomic variables, industrial factors and company non-financial criteria for robust and balanced risk analysis. It is based on the theory of enterprise risk management (ERM) and can be used to analyze company failure possibility as an important aspect of risk management. This study provides new insight into the selection of macro and industry factors based on statistical analysis. Another innovation is related to how marginal utility functions of variables are constructed and imperfect data can be handled in a distributed assessment framework. It is the first study to convert observed data into probability distributions using the likelihood analysis method instead of subjective judgement for data-driven risk analysis of company bankruptcy in the KIS sector within the ER framework, which makes the model more interpretable and informative. The model can be used to provide an early warning mechanism to assist stakeholders to make investment and other decisions.