Work

Global Business Networks

Year: 2025

Type: article

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 .

Source: Journal of Financial Economics

DOI: 10.1016/j.jfineco.2025.104007

Authors: Christian Breitung, Sebastian Müller

Institution: Technical University of Munich

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,740

OpenAlex ID: W4407554187

FWCI: 26.84

Cites: 51

Cited by: 34

Related to: 10