  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.
