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Business analytics for corporate risk management and performance improvement

Abstract

The purpose of this research is to introduce an innovative decision architecture to assess corporate risks by utilizing accounting narratives and to further examine the association between those risks and operating performance. We run text mining algorithms to identify the types of risks from narratives and incorporate them with a readability measure (i.e., linguistic cue(s)) to conjecture a firm manager’s attitude toward corporate risk. We consider a two-stage network data envelopment analysis (TNDEA) with the benefit of opening up the black-box of a production process via considering internal activities. The outcome derived from TNDEA is determined by users who decide “a priori” what the specification of the model should be, without considering any alternatives. The study further incorporates the fuzzy rough set theory (FRST) with TNDEA by considering inclusion/exclusion or different combinations of inputs, intermediates, and outputs so as to realize a corporate’s underlying business situation. Decision attributes taken from the outcome of FRST-TNDEA and condition attributes gathered from annual reports are jointly inserted into an artificial intelligence (AI) technique to establish the forecasting model. The integrated circuit (IC) industry has long been viewed as an essential backbone of Taiwan’s economy, and therefore it is taken as our research target. The results show that the introduced linguistic cues have positive and considerable impacts on firm performance. Overall, the findings herein provide direct support for recent regulators that require corporates to add a new section on risk factors in accounting narratives so as to prevent users from making biased judgments.

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Acknowledgements

The authors would like to thank the Ministry of Science and Technology, Taiwan for financially support this research project under Contracts No. 108-2410-H-034 -056-MY2 and No.110-2410-H-034 -009.

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Hsu, MF., Hsin, YS. & Shiue, FJ. Business analytics for corporate risk management and performance improvement. Ann Oper Res (2021). https://doi.org/10.1007/s10479-021-04259-x

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Keywords

  • Risk management
  • Text mining
  • Readability
  • Data envelopment analysis