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Predicting mergers and acquisitions using machine learning
Mergers and acquisitions (M&As) play a crucial role in the expansion of companies. During a typical M&A deal, the target company is offered a significant premium over their current share price by the acquirer. This results in a material increase in the target company's share price on the announcemen...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2024
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| _version_ | 1867613248175472640 |
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| access_status_str | Open Access |
| author | Beckenstrater, Gordon |
| author2 | Marais, Patrick |
| author_browse | Beckenstrater, Gordon Marais, Patrick |
| author_facet | Marais, Patrick Beckenstrater, Gordon |
| author_sort | Beckenstrater, Gordon |
| collection | Thesis |
| description | Mergers and acquisitions (M&As) play a crucial role in the expansion of companies. During a typical M&A deal, the target company is offered a significant premium over their current share price by the acquirer. This results in a material increase in the target company's share price on the announcement of acquisition. Therefore, accurately forecasting M&As, despite the challenge due to their rarity, presents a lucrative opportunity for investors. Traditional statistical forecasting techniques, reliant on fundamental and technical metrics along with a few macroeconomic indicators, often struggle to pick up underlying relationships between features and targets. This study investigates the effectiveness of advanced machine learning techniques, which have found large success in stock price and fraud prediction, in predicting M&As. logistic regression, a popular statistical technique in M&A literature, serves as a baseline. The performance of algorithms such as random forest, LightGBM, long short-term memory networks (LSTM) and the TabTransformer are evaluated against the baseline. A secondary objective is the development of a robust ensemble model for potential use in an investment portfolio. The algorithms were trained on a comprehensive historical dataset with diverse financial indicators. Given the considerable amount of missing values in the dataset, imputation was applied to allow all algorithms to function properly. Feature selection was conducted to remove redundant features, mitigating their impact on validation performance of the models. Data imbalance was addressed with data sampling techniques which proved substantial in improving validation performance. The findings are that all the advanced algorithms surpassed the performance of logistic regression in M&A prediction, signalling a shift from traditional statistical methods to advanced machine learning techniques. LightGBM and the Ensemble model displayed the best performance in M&A prediction. These results also show that an investment portfolio, constructed based on the most confident predictions of the Ensemble model, forms the basis for a profitable investment strategy. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40791 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:07.122Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40791 Predicting mergers and acquisitions using machine learning Beckenstrater, Gordon Marais, Patrick machine learning Mergers and acquisitions (M&As) play a crucial role in the expansion of companies. During a typical M&A deal, the target company is offered a significant premium over their current share price by the acquirer. This results in a material increase in the target company's share price on the announcement of acquisition. Therefore, accurately forecasting M&As, despite the challenge due to their rarity, presents a lucrative opportunity for investors. Traditional statistical forecasting techniques, reliant on fundamental and technical metrics along with a few macroeconomic indicators, often struggle to pick up underlying relationships between features and targets. This study investigates the effectiveness of advanced machine learning techniques, which have found large success in stock price and fraud prediction, in predicting M&As. logistic regression, a popular statistical technique in M&A literature, serves as a baseline. The performance of algorithms such as random forest, LightGBM, long short-term memory networks (LSTM) and the TabTransformer are evaluated against the baseline. A secondary objective is the development of a robust ensemble model for potential use in an investment portfolio. The algorithms were trained on a comprehensive historical dataset with diverse financial indicators. Given the considerable amount of missing values in the dataset, imputation was applied to allow all algorithms to function properly. Feature selection was conducted to remove redundant features, mitigating their impact on validation performance of the models. Data imbalance was addressed with data sampling techniques which proved substantial in improving validation performance. The findings are that all the advanced algorithms surpassed the performance of logistic regression in M&A prediction, signalling a shift from traditional statistical methods to advanced machine learning techniques. LightGBM and the Ensemble model displayed the best performance in M&A prediction. These results also show that an investment portfolio, constructed based on the most confident predictions of the Ensemble model, forms the basis for a profitable investment strategy. 2024-12-20T07:04:33Z 2024-12-20T07:04:33Z 2024 2024-12-20T07:01:59Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40791 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | machine learning Beckenstrater, Gordon Predicting mergers and acquisitions using machine learning |
| thesis_degree_str | Master's |
| title | Predicting mergers and acquisitions using machine learning |
| title_full | Predicting mergers and acquisitions using machine learning |
| title_fullStr | Predicting mergers and acquisitions using machine learning |
| title_full_unstemmed | Predicting mergers and acquisitions using machine learning |
| title_short | Predicting mergers and acquisitions using machine learning |
| title_sort | predicting mergers and acquisitions using machine learning |
| topic | machine learning |
| url | http://hdl.handle.net/11427/40791 |
| work_keys_str_mv | AT beckenstratergordon predictingmergersandacquisitionsusingmachinelearning |