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Automated text mining process for corporate risk analysis and management

Author

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  • Ming-Fu Hsu

    (National United University)

  • Chingho Chang

    (National Chi Nan University)

  • Jhih‐Hong Zeng

    (Chinese Culture University)

Abstract

The aim of this research is to introduce innovative automated text mining process to extract operation risks from accounting narratives and to further examine the association between these risk types and operating performance. Specifically, we perform topic modeling to decompose a large amount of unstructured textual disclosures into some topics and preserve these topics, which are relevant to business operation risk. Sequentially, we propose a measure for the degree of financial default, referred to as the “intensity of risk-word list,” by joint utilization of text mining and a statistical approach. The analyzed results are then fed into a support vector machine-based model to construct the forecasting model. The results show that the textual-based risk indicators are significantly and positively related to a corporate’s operation efficiency. This study also echoes the recent trend of financial reporting regulations to add a new section on risk factors in annual reports.

Suggested Citation

  • Ming-Fu Hsu & Chingho Chang & Jhih‐Hong Zeng, 2022. "Automated text mining process for corporate risk analysis and management," Risk Management, Palgrave Macmillan, vol. 24(4), pages 386-419, December.
    • Handle: RePEc:pal:risman:v:24:y:2022:i:4:d:10.1057_s41283-022-00099-6
    DOI: 10.1057/s41283-022-00099-6
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