Global Business Networks - Breitung, Christian; Müller, Sebastian (Journal of Financial Economics, 2025)
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 .