Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting risk from financial reports with regression
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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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.