Bank distress in the news: Describing events through deep learning
This provides a tool for financial analysts and policymakers to monitor bank distress and systemic risk through timely news data, though it is incremental in applying existing deep learning techniques to a specific domain.
The authors tackled the problem of automatically extracting qualitative descriptions of financial events from news articles, using a deep learning model that combines supervised event detection with unsupervised semantic representations. They demonstrated the approach on 6.6 million news articles, identifying 243 bank distress and government intervention events, and showed it can generate indices and explanations for financial risk analysis.
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.