Abstract:
We leverage the capabilities of GPT-3 to generate historical business descriptions for over 71,000 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.
Date: April 19th, 2024, 15:00 (Warsaw time)
Speaker's bio: Sebastian Müller serves as Professor of Finance at the TUM School of Management, TUM Campus Heilbronn. He received his PhD from the University of Mannheim, and was a visiting researcher at the University of California at Berkeley, USA. Professor Müller's research primarily focuses on capital markets and investor behavior. He adopts an empirical quantitative approach, utilizing methods of machine learning, text analysis, and generative artificial intelligence (AI). He explores the digitization of the financial industry and the rising significance of sustainability and ESG-related topics. Mr. Müller received several awards for his research which has been published in leading finance journals such as The Journal of Financial Economics and The Review of Financial Studies.
Place: Teams webinar.
