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    题名: On the risk prediction and analysis of soft information in finance reports
    作者: Tsai, Ming-Feng;Wang, Chuan-Ju
    蔡銘峰
    贡献者: 資科系
    关键词: Finance;Risk prediction;Text mining;Sentiment analysis
    日期: 2017-02
    上传时间: 2016-12-15 16:17:40 (UTC+8)
    摘要: We attempt in this paper to utilize soft information in financial reports to analyze financial risk among companies. Specifically, on the basis of the text information in financial reports, which is the so-called soft information, we apply analytical techniques to study relations between texts and financial risk. Furthermore, we conduct a study on financial sentiment analysis by using a finance-specific sentiment lexicon to examine the relations between financial sentiment words and financial risk. A large collection of financial reports published annually by publicly-traded companies is employed to conduct our experiments; moreover, two analytical techniques – regression and ranking methods – are applied to conduct these analyses. The experimental results show that, based on a bag-of-words model, using only financial sentiment words results in performance comparable to using the whole texts; this confirms the importance of financial sentiment words with respect to risk prediction. In addition to this performance comparison, via the learned models, we draw attention to some strong and interesting correlations between texts and financial risk. These valuable findings yield greater insight and understanding into the usefulness of soft information in financial reports and can be applied to a broad range of financial and accounting applications.
    關聯: European Journal of Operational Research, Volume 257, Issue 1, Pages 243–250
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1016/j.ejor.2016.06.069
    DOI: 10.1016/j.ejor.2016.06.069
    显示于类别:[資訊科學系] 期刊論文

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