Quantifying Logical Relations of Financial Risks with BERT and Mutual Information

[Objective] This paper tries to prevent and control financial risks by quantifying their logical relationship, which also improve the reliability of processing word frequency of financial events. [Methods] We proposed a quantitative analysis method for the logical relation of financial risks based on BERT and mutual information combined with domain knowledge. Then, we quantified the relations with COPA and financial data sets. [Results] The proposed model effectively addressed the issue of unreliable quantization of word frequency. Its accuracy reached 80.1%, which was 3.1%~37.4% higher than the benchmark models. [Limitations] More research is needed to examine our new model with non-financial and other corpora. [Conclusions] Our new method can reveal the evolutionary path of financial risk events and improve the effect quantitative presentation of their logical relationship.