Text as Data in Economic Analysis

Tarek Hassan, Stephan Hollander, Aakash Kalyani, Markus Schwedeler (), Ahmed Tahoun () and Laurence van Lent ()
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Ahmed Tahoun: https://www.london.edu/faculty-and-research/faculty-profiles/t/tahoun-a

No 2024-022, Working Papers from Federal Reserve Bank of St. Louis

Abstract: FULL AND CORRECT ORDER OF AUTHORS: Tarek A. Hassan, Stephan Hollander, Aakash Kalyani, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. This article discusses how to apply computational linguistics techniques to analyze largely unstructured corporate-generated text for economic analysis. As a core example, we illustrate how textual analysis of earnings conference call transcripts can provide insights into how markets and individual firms respond to economic shocks, such as a nuclear disaster or a geopolitical event: insights that often elude traditional non-text data sources. This approach enables extracting actionable intelligence, supporting both policy-making and strategic corporate decision-making. We also explore applications using other sources of corporate-generated text, including patent documents and job postings. By incorporating computational linguistics techniques into the analysis of economic shocks, new opportunities arise for real-time economic data, offering a more nuanced understanding of market and firm responses in times of economic volatility.

Keywords: text as data; natural language processing (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2024-09, Revised 2025-09-11
New Economics Papers: this item is included in nep-big
Note: Publisher DOI: https://doi.org/10.1257/jep.20231365
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Citations: View citations in EconPapers (1)

Published in Journal of Economic Perspectives

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlwp:98767

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DOI: 10.20955/wp.2024.022

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