Bank distress in the news: Describing events through deep learning

TL;DR

This paper introduces a deep learning method that automatically detects and describes financial events like bank distress from news articles, enhancing risk analysis with detailed, timely information.

Contribution

A novel deep learning approach that extracts qualitative event descriptions from news data using minimal supervision and unsupervised semantic representations.

Findings

Effective detection of bank distress events from 6.6 million news articles.

Ability to generate natural language descriptions of financial events.

Indices derived from news can signal levels of financial risk.

Abstract

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.