Risk Ranking from Financial Reports
Ming-Feng Tsai1 and Chuan-Ju Wang2
(1) Department of Computer Science, National Chengchi University, Taipei 116, Taiwan
(2) Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan
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.