Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector

I. Rodríguez-Muñoz-de-Baena, M. Coronado‑Vaca, E. Vaquero Lafuente

Cogent Business & Management Vol. 12, nº. 1, pp. 2487219-1 - 2487219-29

Summary:

This study explores the application of transformer models directly for classification in predicting mergers and acquisitions (M&A) targets within the U.S. energy sector. The primary objective is to evaluate the capability and performance of various transformer-based models in directly predicting M&A target companies, while the secondary objective investigates the relationship between target companies and renewable energy terminology in their annual reports. We present a novel approach to predicting M&A targets by utilizing cutting-edge Natural Language Processing (NLP) techniques, such as fine-tuned transformer LLMs (Large Language Models) for direct classification. We analyze textual data from 200 publicly-listed US energy companies’ SEC-filings and employ FinBERT, ALBERT, and GPT-3-babage-002 as predictive models of M&A targets. We provide empirical evidence on LLMs’ capability in the direct classification of M&A target companies, with FinBERT utilizing oversampling, being the top-performing model due to its high precision and minimized false positives, critical for precise financial decision-making. Additionally, while the study revealed key differences in target and non-target report characteristics, it finds no significant evidence that M&A target companies use more renewable energy-related terminology. It is the first paper applying fine-tuned transformer-LLMs to predict M&A targets, effectively showcasing their capability for this task of direct classification as predictive models.

Keywords: Mergers and acquisitions (M&A); renewable energy; takeover target prediction; green M&A; natural language processing (NLP); transformer models; large language models (LLM)

JCR Impact Factor and WoS quartile: 3,000 - Q2 (2023)

DOI reference: https://doi.org/10.1080/23311975.2025.2487219

Published on paper: December 2025.

Published on-line: April 2025.

Citation:
I. Rodríguez-Muñoz-de-Baena, M. Coronado‑Vaca, E. Vaquero Lafuente, Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector. Cogent Business & Management. Vol. 12, nº. 1, pp. 2487219-1 - 2487219-29, December 2025. [Online: April 2025]