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Economic Surveillance using Corporate Text

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Abstract

FULL AND CORRECT ORDER OF AUTHORS: Tarek A. Hassan, Stephan Hollander, Aakash Kalyani, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. This article applies simple methods from computational linguistics to analyze unstructured corporate texts for economic surveillance. We apply text-as-data approaches to earnings conference call transcripts, patent texts, and job postings to uncover unique insights into how markets and firms respond to economic shocks, such as a nuclear disaster or a geopolitical event---insights that often elude traditional data sources. This method enhances our ability to extract actionable intelligence from textual data, thereby aiding policy-making and strategic corporate decisions. By integrating computational linguistics into the analysis of economic shocks, our study opens new possibilities for real-time economic surveillance and offers a more nuanced understanding of firm-level reactions in volatile economic environments.

Suggested Citation

  • Tarek A. Hassan & Stephan Hollander & Aakash Kalyani & Markus Schwedeler & Ahmed Tahoun & Laurence van Lent, 2024. "Economic Surveillance using Corporate Text," Working Papers 2024-022, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:98767
    DOI: 10.20955/wp.2024.022
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    Keywords

    text as data; natural language processing;

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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