Fed in Print
All Title Author Abstract
Bank Series Content Type Author JEL Classification

Working Paper Revision

Text as Data in Economic Analysis


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.

JEL Classification: C55;

https://doi.org/10.20955/wp.2024.022

Status: Published in Journal of Economic Perspectives

Access Documents

File(s): File format is application/pdf https://doi.org/10.20955/wp.2024.022
Description: Full text

Authors

Bibliographic Information

Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2025-09-11

Number: 2024-022

Note: Publisher DOI: https://doi.org/10.1257/jep.20231365

Related Works