Identifying Financial Crises Using Machine Learning on Textual Data

Mary Chen (), Matthew DeHaven, Isabel Kitschelt, Seung Jung Lee and Martin Sicilian

No 1374, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies.

Keywords: Financial crises; Machine learning; Natural language processing (search for similar items in EconPapers)
JEL-codes: C53 C55 G01 (search for similar items in EconPapers)
Date: 2023-03-31
New Economics Papers: this item is included in nep-big, nep-cmp, nep-des, nep-fdg and nep-mon
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgif:1374

DOI: 10.17016/IFDP.2023.1374

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