Bank distress in the news: Describing events through deep learning

By Samuel Rönnqvist, Peter Sarlin. 2016

56 citations recorded by the source corpus.

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.

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “In another study [97], text mining was again used for identifying the bank distress by extracting the data from financial news and then using a Deep Feed Forward Network (DFFN) on semantic sentence vectors extracted from word embeddings to classify if there was an event or not.”
Financial Time Series Prediction Using Deep Learning (2017, methodology): “Ronnqvist [25] proposed a DL approach for estimating financial risk based on the news articles.”
Deep learning bank distress from news and numerical financial data (2017, methodology): “As R¨onnqvist and Sarlin (2017) applies a fully data driven model with unsupervised semantic generalization, supervised only by a small set of events, this paper extends upon that work by incorporating numerical ﬁnancial data as well.”