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題名 Risk ranking from financial reports
作者 Tsai, Ming-Feng
蔡銘峰
Wang, C.-J.
貢獻者 資科系
關鍵詞 Financial reports; Learning to rank; Ranking; Ranking approach; Ranking problems; Soft information; Text information; Volatility; Industry; Information retrieval; Finance
日期 2013-03
上傳時間 21-五月-2015 17:25:25 (UTC+8)
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.
關聯 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7814, 804-807
資料類型 article
DOI http://dx.doi.org/10.1007/978-3-642-36973-5_89
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 21-五月-2015 17:25:25 (UTC+8)-
dc.date.available 21-五月-2015 17:25:25 (UTC+8)-
dc.date.issued (上傳時間) 21-五月-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) articleen
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-