| dc.contributor | 資科系 | - |
| dc.creator (作者) | Tsai, Ming-Feng | - |
| dc.creator (作者) | 蔡銘峰 | zh_TW |
| dc.creator (作者) | Wang, C.-J. | en_US |
| dc.date (日期) | 2013-03 | - |
| dc.date.accessioned | 21-May-2015 17:25:25 (UTC+8) | - |
| dc.date.available | 21-May-2015 17:25:25 (UTC+8) | - |
| dc.date.issued (上傳時間) | 21-May-2015 17:25:25 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75278 | - |
| dc.description.abstract (摘要) | This paper attempts to use soft information in finance to rank the risk levels of a set of companies. Specifically, we deal with a ranking problem with a collection of financial reports, in which each report is associated with a company. By using text information in the reports, which is so-called the soft information, we apply learning-to-rank techniques to rank a set of companies to keep them in line with their relative risk levels. In our experiments, a collection of financial reports, which are annually published by publicly-traded companies, is employed to evaluate our ranking approach; moreover, a regression-based approach is also carried out for comparison. The experimental results show that our ranking approach not only significantly outperforms the regression-based one, but identifies some interesting relations between financial terms. © 2013 Springer-Verlag. | - |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7814, 804-807 | - |
| dc.subject (關鍵詞) | Financial reports; Learning to rank; Ranking; Ranking approach; Ranking problems; Soft information; Text information; Volatility; Industry; Information retrieval; Finance | - |
| dc.title (題名) | Risk ranking from financial reports | - |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/978-3-642-36973-5_89 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/978-3-642-36973-5_89 | - |
