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Global Business Networks

Author

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  • Breitung, Christian
  • Müller, Sebastian

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.

Suggested Citation

  • Breitung, Christian & Müller, Sebastian, 2025. "Global Business Networks," Journal of Financial Economics, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:jfinec:v:166:y:2025:i:c:s0304405x25000157
    DOI: 10.1016/j.jfineco.2025.104007
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    More about this item

    Keywords

    Business network; Textual analysis; Natural language processing; GPT-3; Large language models;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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