This paper attempts to deal with a ranking problem with a collection of financial reports. By using the text information in the reports, we apply learning-to-rank techniques to rank a set of companies to keep them in line with their relative risk levels. The experimental results show that our ranking approach significantly outperforms the regression-based one. Furthermore, our ranking models not only identify some financially meaningful words but suggest interesting relations between the text information in financial reports and the risk levels among companies. Finally, we provide a visualization interface to demonstrate the relations between financial risk and text information in the reports. This demonstration enables users to easily obtain useful information from a number of financial reports.
Proceedings of the 24th International Conference on Computational Linguistics (COLING '12), 447-452, 2012