Extraction and classification of risk-related sentences from securities reports
With the drastically changing business environment, it is difficult even for experts to properly extract and classify risk statements from securities reports, which contain large volumes and unstructured information. Several methods have been proposed, but the existing methods face difficulty in dea...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000398 |
| _version_ | 1811321358442823680 |
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| author | Motomasa Fujii Hiroki Sakaji Shigeru Masuyama Hajime Sasaki |
| author_facet | Motomasa Fujii Hiroki Sakaji Shigeru Masuyama Hajime Sasaki |
| author_sort | Motomasa Fujii |
| collection | DOAJ |
| description | With the drastically changing business environment, it is difficult even for experts to properly extract and classify risk statements from securities reports, which contain large volumes and unstructured information. Several methods have been proposed, but the existing methods face difficulty in dealing with flexible risk expressions. This study presents an open-domain risk-analysis framework that combine the strengths of both humans and machines. They include defining appropriate business risks and constructing supervised data based on those definitions. Risks were then extracted and classified from the securities reports of a representative group of Japanese companies. We confirmed the limitations of pattern matching, and the usefulness of contextual analysis methods. We also confirmed the importance of constructing supervised data based on appropriate guidelines of data classification. This study presents a framework that quickly and effectively derives the risk structure of a given industry or company from vast and unstructured information. |
| first_indexed | 2024-04-13T13:16:15Z |
| format | Article |
| id | doaj.art-7e65fbbeba804fe1a83a9bebbfe3c4ad |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-13T13:16:15Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-7e65fbbeba804fe1a83a9bebbfe3c4ad2022-12-22T02:45:28ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-11-0122100096Extraction and classification of risk-related sentences from securities reportsMotomasa Fujii0Hiroki Sakaji1Shigeru Masuyama2Hajime Sasaki3Graduate School of Management, Tokyo University of Science, Tokyo, JapanGraduate School of Engineering, The University of Tokyo, Tokyo, JapanGraduate School of Management, Tokyo University of Science, Tokyo, JapanInstitute for Future Initiatives, The University of Tokyo, Tokyo, Japan; Corresponding author.With the drastically changing business environment, it is difficult even for experts to properly extract and classify risk statements from securities reports, which contain large volumes and unstructured information. Several methods have been proposed, but the existing methods face difficulty in dealing with flexible risk expressions. This study presents an open-domain risk-analysis framework that combine the strengths of both humans and machines. They include defining appropriate business risks and constructing supervised data based on those definitions. Risks were then extracted and classified from the securities reports of a representative group of Japanese companies. We confirmed the limitations of pattern matching, and the usefulness of contextual analysis methods. We also confirmed the importance of constructing supervised data based on appropriate guidelines of data classification. This study presents a framework that quickly and effectively derives the risk structure of a given industry or company from vast and unstructured information.http://www.sciencedirect.com/science/article/pii/S2667096822000398Risk analysisFinancial text miningRisk extractionRisk classification |
| spellingShingle | Motomasa Fujii Hiroki Sakaji Shigeru Masuyama Hajime Sasaki Extraction and classification of risk-related sentences from securities reports International Journal of Information Management Data Insights Risk analysis Financial text mining Risk extraction Risk classification |
| title | Extraction and classification of risk-related sentences from securities reports |
| title_full | Extraction and classification of risk-related sentences from securities reports |
| title_fullStr | Extraction and classification of risk-related sentences from securities reports |
| title_full_unstemmed | Extraction and classification of risk-related sentences from securities reports |
| title_short | Extraction and classification of risk-related sentences from securities reports |
| title_sort | extraction and classification of risk related sentences from securities reports |
| topic | Risk analysis Financial text mining Risk extraction Risk classification |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000398 |
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