Global Business Networks

Forthcoming Journal of Financial Economics

52 Pages Posted: 3 Apr 2023 Last revised: 11 Feb 2025

See all articles by Christian Breitung

Christian Breitung

Technische Universität München (TUM) - TUM School of Management

Sebastian Müller

Technische Universität München (TUM) - TUM School of Management

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Date Written: March 21, 2023

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

JEL Classification: G10, G12, G14

Suggested Citation

Breitung, Christian and Müller, Sebastian, Global Business Networks (March 21, 2023). Forthcoming Journal of Financial Economics, Available at SSRN: https://ssrn.com/abstract=4395079 or http://dx.doi.org/10.2139/ssrn.4395079

Christian Breitung (Contact Author)

Technische Universität München (TUM) - TUM School of Management ( email )

Germany

Sebastian Müller

Technische Universität München (TUM) - TUM School of Management ( email )

Germany

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