Bank distress in the news: Describing events through deep learning
| Autor: | Peter Sarlin, Samuel Rönnqvist |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Government
050208 finance Leverage (finance) Descriptive statistics Event (computing) business.industry Computer science Cognitive Neuroscience Deep learning Financial risk 05 social sciences 02 engineering and technology Data science Computer Science Applications Artificial Intelligence Analytics 0502 economics and business 0202 electrical engineering electronic engineering information engineering Systemic risk Unsupervised learning 020201 artificial intelligence & image processing Distributional semantics Artificial intelligence business Natural language |
| Zdroj: | Neurocomputing. 264:57-70 |
| ISSN: | 0925-2312 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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