Stephan Hollander is a Professor of Financial Accounting at Tilburg University. This post is based on a NBER working paper by Professor Hollander, Professor Tarek Alexander Hassan, Professor Aakash Kalyani, Professor Laurence van Lent, Mr. Markus Schwedeler, and Professor Ahmed Tahoun.
Over the past decennium, we have witnessed a significant growth in the volume of company-released text data, ranging from transcripts of periodic earnings calls—in which company management discusses their firms’ financial performance, future outlook, and strategic initiatives—to an extensive array of regulatory filings required of companies traded on U.S. stock exchanges. Economists are increasingly recognizing its potential as a powerful resource for economic analysis and insights.
In our article, we discuss how to apply various computational linguistics tools to analyze unstructured texts provided by firms, uncovering how markets and firms respond to economic shocks—whether caused by a natural disaster or geopolitical event—offering insights often beyond the scope of traditional data sources. We highlight examples of corporate-text analysis, including earnings call transcripts, companies’ patent documents, and job postings.
