ESRB publishes a working paper that shows the news as a rich source of data on distressed firm links that drive firm-level and aggregate risks. The authors developed a machine learning methodology that takes text data as input and outputs a data-implied firm network. The news tends to report about links in which a less popular firm is distressed and may contaminate a more popular firm. This constitutes a contagion channel that yields predictable returns and downgrades. Shocks to the degree of news-implied firm connectivity predict increases in aggregate volatilities, credit spreads, default rates, and declines in output. The results of this paper enable the estimation of accurate measures of firm-level and aggregate risks.
The news-implied networks include a vast majority of links recorded in the currently available data sets. In contrast to the currently available networks, however, news-implied networks capture a wider range of firms and links and are available in high frequencies. On an aggregate level, the authors show that news-implied firm networks capture information about contagion and uncertainty effects that drive aggregate outcomes. The first set of results shows that demand-side considerations incite the news to report about firm links that actively transmit risks across firms and lead to contagion. The next set of results shows that the information contained in news-implied firm networks is highly predictive of aggregate outcomes. Finally, the results show that news-implied firm networks capture information that is not contained in alternative networks. All in one, the results of this paper enable the estimation of accurate measures of firm-level and aggregate risks.
Related Link: Working Paper (PDF)
Keywords: Europe, EU, Banking, Securities, Research, Technology, Machine Learning, Fintech, Natural Language Processing, Risk Measurement, Sentiment Analysis, ESRB
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