Automated text mining process for corporate risk analysis and management

CCUR > College of Social Sciences > Department of Economics & Graduate Institute of Economics > journal articles > Item 987654321/50398

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/50398

Title:	Automated text mining process for corporate risk analysis and management
Authors:	Zeng, Jhih-Hong Chang, Chinghob Hsu, Ming-Fu
Contributors:	Department of Economics
Keywords:	Annual reports;Automated text mining process;Multiple attribute decision-making;Risk management
Date:	2022-08
Issue Date:	2022-10-24 09:07:10 (UTC+8)
Publisher:	Palgrave Macmillan
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.
Relation:	Risk Management Risk Management卷 24, 期 4, 頁 386 - 419December 2022
Appears in Collections:	[Department of Economics & Graduate Institute of Economics ] journal articles

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