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

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

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...

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Bibliographic Details
Main Authors: Inés Rodríguez-Muñoz-de-Baena, María Coronado-Vaca, Esther Vaquero-Lafuente
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Cogent Business & Management
Subjects:
Mergers and acquisitions (M&A)
renewable energy
takeover target prediction
green M&A
natural language processing (NLP)
transformer models
Online Access:https://www.tandfonline.com/doi/10.1080/23311975.2025.2487219
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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.
ISSN:2331-1975

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