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政大典藏 > College of Informatics > Department of Computer Science > Periodical Articles > Item 140.119/75278

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75278

Title:	Risk ranking from financial reports
Authors:	Tsai, Ming-Feng 蔡銘峰 Wang, C.-J.
Contributors:	資科系
Keywords:	Financial reports;Learning to rank;Ranking;Ranking approach;Ranking problems;Soft information;Text information;Volatility;Industry;Information retrieval;Finance
Date:	2013-03
Issue Date:	2015-05-21 17:25:25 (UTC+8)
Abstract:	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.
Relation:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7814, 804-807
Data Type:	article
DOI link:	http://dx.doi.org/10.1007/978-3-642-36973-5_89
DOI:	10.1007/978-3-642-36973-5_89
Appears in Collections:	[Department of Computer Science ] Periodical Articles

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