Bank distress in the news: Describing events through deep learning. (arXiv:1603.05670v1 [cs.CL])

While many models are purposed for detecting the occurrence of events in complex 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, 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 country or European level, while being coupled with explanations. Thus, we exemplify how text, as timely and widely available data, can serve as a useful complementary source of information for financial risk analytics.

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