Global Business Networks

Christian Breitung and Sebastian Müller

Journal of Financial Economics, 2025, vol. 166, issue C

Abstract: We leverage the capabilities of GPT-3 to generate historical business descriptions for over 63,000 global firms. Utilizing these descriptions and advanced embedding models from OpenAI, we construct time-varying business networks that represent business links across the globe. We showcase the performance of these networks by studying the lead–lag effect for global stocks and predicting target firms in M&A deals. We demonstrate how masking firm-specific details can mitigate look-ahead bias concerns that may arise from the use of embedding models with a recent knowledge cutoff, and how to differentiate between competitor, supplier, and customer links by fine-tuning an open-source language model.

Keywords: Business network; Textual analysis; Natural language processing; GPT-3; Large language models (search for similar items in EconPapers)
JEL-codes: G10 G12 G14 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:166:y:2025:i:c:s0304405x25000157

DOI: 10.1016/j.jfineco.2025.104007

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