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 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.

Grantor dc:publisher.institution

Department of Computer Science

Year dc:date.issued

2024

Author dc:creator

• Beckenstrater, Gordon

Advisor dc:contributor.advisor

• Marais, Patrick

• machine learning

Handle dc:identifier.uri

http://hdl.handle.net/11427/40791

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/40791