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Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach

Author

Listed:
  • Meng-Meng Tan

    (Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK)

  • Dong-Ling Xu

    (Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK)

  • Jian-Bo Yang

    (Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK)

Abstract

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.

Suggested Citation

  • Meng-Meng Tan & Dong-Ling Xu & Jian-Bo Yang, 2022. "Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach," JRFM, MDPI, vol. 15(3), pages 1-29, March.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:131-:d:768117
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    References listed on IDEAS

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    1. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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