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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Business & Management |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311975.2025.2487219 |
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| author | Inés Rodríguez-Muñoz-de-Baena María Coronado-Vaca Esther Vaquero-Lafuente |
| author_facet | Inés Rodríguez-Muñoz-de-Baena María Coronado-Vaca Esther Vaquero-Lafuente |
| author_sort | Inés Rodríguez-Muñoz-de-Baena |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-37c116c3fe3648029844f97d0bb4c934 |
| institution | DOAJ |
| issn | 2331-1975 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Business & Management |
| spelling | doaj-art-37c116c3fe3648029844f97d0bb4c9342025-08-20T03:06:06ZengTaylor & Francis GroupCogent Business & Management2331-19752025-12-0112110.1080/23311975.2025.2487219Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sectorInés Rodríguez-Muñoz-de-Baena0María Coronado-Vaca1Esther Vaquero-Lafuente2ICADE School of Economics and Business Administration, Universidad Pontificia Comillas, Madrid, SpainICADE School of Economics and Business Administration, Universidad Pontificia Comillas, Madrid, SpainICADE School of Economics and Business Administration, Universidad Pontificia Comillas, Madrid, SpainThis 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.https://www.tandfonline.com/doi/10.1080/23311975.2025.2487219Mergers and acquisitions (M&A)renewable energytakeover target predictiongreen M&Anatural language processing (NLP)transformer models |
| spellingShingle | Inés Rodríguez-Muñoz-de-Baena María Coronado-Vaca Esther Vaquero-Lafuente Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector Cogent Business & Management Mergers and acquisitions (M&A) renewable energy takeover target prediction green M&A natural language processing (NLP) transformer models |
| title | Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector |
| title_full | Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector |
| title_fullStr | Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector |
| title_full_unstemmed | Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector |
| title_short | Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector |
| title_sort | fine tuning transformer models for m a target prediction in the u s energy sector |
| topic | Mergers and acquisitions (M&A) renewable energy takeover target prediction green M&A natural language processing (NLP) transformer models |
| url | https://www.tandfonline.com/doi/10.1080/23311975.2025.2487219 |
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