Abstract
We propose an eXplainable Risk Ranking (XRR) model that uses multilevel encoders and attention mechanisms to analyze financial risks among companies. In specific, the proposed method utilizes the textual information in financial reports to rank the relative risks among companies and locate top high-risk companies; moreover, via attention mechanisms, XRR enables to highlight the critical words and sentences within financial reports that are most likely to influence financial risk and thus boasts better model explainability. Experimental results evaluated on 10-K financial reports show that XRR significantly outperforms several baselines, yielding up to 7.4% improvement in terms of ranking correlation metrics. Furthermore, in our experiments, the model explainability is evaluated by using finance-specific sentiment lexicons at word level and a newly-provided annotated reference list at the sentence level to examine the learned attention models.