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TitleVisualization on Financial Terms via Risk Ranking from Financial Reports
CreatorTsai, Ming-Feng;Wang, Chuan-Ju
蔡銘峰
Contributor資科系
Key WordsText Ranking, Stock Return Volatility, Financial Report, 10-K Corpus
Date2012
Date Issued22-Jun-2016 17:09:34 (UTC+8)
SummaryThis paper attempts to deal with a ranking problem with a collection of financial reports. By using the text information in the reports, we apply learning-to-rank techniques to rank a set of companies to keep them in line with their relative risk levels. The experimental results show that our ranking approach significantly outperforms the regression-based one. Furthermore, our ranking models not only identify some financially meaningful words but suggest interesting relations between the text information in financial reports and the risk levels among companies. Finally, we provide a visualization interface to demonstrate the relations between financial risk and text information in the reports. This demonstration enables users to easily obtain useful information from a number of financial reports.
RelationProceedings of the 24th International Conference on Computational Linguistics (COLING `12), 447-452, 2012
Typeconference
dc.contributor 資科系
dc.creator (作者) Tsai, Ming-Feng;Wang, Chuan-Ju
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2012
dc.date.accessioned 22-Jun-2016 17:09:34 (UTC+8)-
dc.date.available 22-Jun-2016 17:09:34 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2016 17:09:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98228-
dc.description.abstract (摘要) This paper attempts to deal with a ranking problem with a collection of financial reports. By using the text information in the reports, we apply learning-to-rank techniques to rank a set of companies to keep them in line with their relative risk levels. The experimental results show that our ranking approach significantly outperforms the regression-based one. Furthermore, our ranking models not only identify some financially meaningful words but suggest interesting relations between the text information in financial reports and the risk levels among companies. Finally, we provide a visualization interface to demonstrate the relations between financial risk and text information in the reports. This demonstration enables users to easily obtain useful information from a number of financial reports.
dc.format.extent 166147 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Proceedings of the 24th International Conference on Computational Linguistics (COLING `12), 447-452, 2012
dc.subject (關鍵詞) Text Ranking, Stock Return Volatility, Financial Report, 10-K Corpus
dc.title (題名) Visualization on Financial Terms via Risk Ranking from Financial Reports
dc.type (資料類型) conference