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    题名: Risk ranking from financial reports
    作者: Tsai, Ming-Feng
    蔡銘峰
    Wang, C.-J.
    贡献者: 資科系
    关键词: Financial reports;Learning to rank;Ranking;Ranking approach;Ranking problems;Soft information;Text information;Volatility;Industry;Information retrieval;Finance
    日期: 2013-03
    上传时间: 2015-05-21 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
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1007/978-3-642-36973-5_89
    DOI: 10.1007/978-3-642-36973-5_89
    显示于类别:[資訊科學系] 期刊論文

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