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Automated text mining process for corporate risk analysis and management

Author

Listed:
  • 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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    as
    1. Lo, Kin & Ramos, Felipe & Rogo, Rafael, 2017. "Earnings management and annual report readability," Journal of Accounting and Economics, Elsevier, vol. 63(1), pages 1-25.
    2. Andrés Berenguer & Luis Gandarias & Álvaro Arévalo, 2020. "Singular spectrum analysis for modelling the hard-to-model risk factors," Risk Management, Palgrave Macmillan, vol. 22(3), pages 178-191, September.
    3. Chrisovalantis Malesios & Prasanta K. Dey & Fuad Ben Abdelaziz, 2020. "Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling," Annals of Operations Research, Springer, vol. 294(1), pages 623-653, November.
    4. Peters, Matt D. & Wieder, Bernhard & Sutton, Steve G. & Wakefield, James, 2016. "Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 1-17.
    5. Marko Jakšič & Matej Marinč, 2019. "Relationship banking and information technology: the role of artificial intelligence and FinTech," Risk Management, Palgrave Macmillan, vol. 21(1), pages 1-18, March.
    6. Guo-Ya Gan & Hsuan-Shih Lee, 2019. "An alternative MILP-DEA model to choose efficient unit without explicit inputs," Annals of Operations Research, Springer, vol. 278(1), pages 379-391, July.
    7. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Wei, Lu & Li, Guowen & Li, Jianping & Zhu, Xiaoqian, 2019. "Bank risk aggregation with forward-looking textual risk disclosures," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    11. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    12. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    13. Xu, Qiao & Fernando, Guy D. & Tam, Kinsun, 2018. "Executive age and the readability of financial reports," Advances in accounting, Elsevier, vol. 43(C), pages 70-81.
    14. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    15. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    16. Wang, Chi-Tai & Chiu, Chui-Sheng, 2014. "Competitive strategies for Taiwan's semiconductor industry in a new world economy," Technology in Society, Elsevier, vol. 36(C), pages 60-73.
    17. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    18. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    19. Vivien Beattie & Bill McInnes & Stella Fearnley, 2004. "A methodology for analysing and evaluating narratives in annual reports: a comprehensive descriptive profile and metrics for disclosure quality attributes," Accounting Forum, Taylor & Francis Journals, vol. 28(3), pages 205-236, September.
    20. Basso, Antonella & Casarin, Francesco & Funari, Stefania, 2018. "How well is the museum performing? A joint use of DEA and BSC to measure the performance of museums," Omega, Elsevier, vol. 81(C), pages 67-84.
    21. Jagannath Roy & Dragan Pamučar & Samarjit Kar, 2020. "Evaluation and selection of third party logistics provider under sustainability perspectives: an interval valued fuzzy-rough approach," Annals of Operations Research, Springer, vol. 293(2), pages 669-714, October.
    22. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    23. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    24. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    25. Mohamed Abdel-Basset & Weiping Ding & Rehab Mohamed & Noura Metawa, 2020. "An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries," Risk Management, Palgrave Macmillan, vol. 22(3), pages 192-218, September.
    26. Lang, Mark & Stice-Lawrence, Lorien, 2015. "Textual analysis and international financial reporting: Large sample evidence," Journal of Accounting and Economics, Elsevier, vol. 60(2), pages 110-135.
    27. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    28. R. Pelissari & M. C. Oliveira & S. Ben Amor & A. Kandakoglu & A. L. Helleno, 2020. "SMAA methods and their applications: a literature review and future research directions," Annals of Operations Research, Springer, vol. 293(2), pages 433-493, October.
    29. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    30. Nicolas Pröllochs & Stefan Feuerriegel & Dirk Neumann, 2018. "Statistical inferences for polarity identification in natural language," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    31. Mohammad Izadikhah & Reza Farzipoor Saen, 2020. "Ranking sustainable suppliers by context-dependent data envelopment analysis," Annals of Operations Research, Springer, vol. 293(2), pages 607-637, October.
    32. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    33. Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
    34. Ruijun Zhang & Caiyan Lin & Zeping Tong, 2021. "A visual risk identification and early warning research for college net loan based on microblog texts," Risk Management, Palgrave Macmillan, vol. 23(4), pages 261-281, December.
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