[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120396-en":3,"doc-seo-120396-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120396,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting mergers and acquisitions using machine learning - Thesis","Mergers and acquisitions (M&As) drive major corporate expansion, and in typical deals the target company receives a premium over its share price, causing a noticeable price increase on announcement. Accurately forecasting M&As is difficult because such events are rare, yet it creates significant value for investors. This thesis evaluates advanced machine learning methods for M&A prediction, using logistic regression as a baseline and comparing random forest, LightGBM, LSTM, and TabTransformer. Models are trained on historical datasets with extensive missing values handled via imputation, redundant features removed through feature selection, and class imbalance addressed through sampling to improve validation performance. Results show all advanced models outperform logistic regression, with LightGBM and an ensemble model achieving the strongest performance, and ensemble-based portfolio construction supporting an investment strategy.","Predicting mergers and acquisitions using machine learning  \nby  \nGordon Beckenstrater  \nSupervised by Prof Patrick Marais  \nA Thesis submitted for the degree of MSc by dissertation  \nin the  \nDepartment of Computer Science University of Cape Town  \nThe copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or noncommercial research purposes only.  \nPublished by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author.  \nDeclaration of Authorship  \nI, Gordon Beckenstrater, declare that this work presented is my own. I confirm that:  \n■ This work was done wholly or mainly while in candidature for a research degree at this University.  \n■ Where any part of this thesis has previously been submitted for a degree or anyother qualification at this University or any other institution, this has been clearly stated.  \n■ Where I have consulted the published work of others, this is always clearly attributed.  \n■ Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n■ I have acknowledged all main sources of help.  \n■ Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nDate: 21/08/2023  \nAbstract  \nMergers 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.  \nThis 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 inan investment portfolio.  \nThe 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.  \nThe 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.  \nAcknowledgements  \nI would like to thank my family for their support over the period of time it took to complete this dissertation as well as Professor Patrick Marais for his guidance throughout the research process.  \nContents  \nDeclaration of Authorship i  \nAbstract ii  \nAcknowledgements iii  \nList of Figures vi  \nList of ","cbCaifCOzrubKeZ5","https://ap.wps.com/l/cbCaifCOzrubKeZ5","pdf",4140192,1,117,"English","en",105,"# Declaration of Authorship\n# Abstract\n# Acknowledgements\n# 1 Introduction\n## 1.1 Aims\n## 1.2 Contributions\n## 1.3 Thesis Structure\n# 2 Background\n## 2.1 The Stock Market\n## 2.2 Mergers and Acquisitions\n## 2.3 Machine Learning Overview\n## 2.4 Core Machine Learning Algorithms\n## 2.5 Data Imputation\n## 2.6 Data Sampling\n## 2.7 Summary\n# 3 Literature Review\n## 3.1 Traditional Statistical Architecture\n## 3.2 Machine Learning Architecture Analysis\n## 3.3 Summary\n# 4 Experimental Design and Datasets\n## 4.1 Datasets\n## 4.2 Experimental Design\n## 4.3 Experiments\n# 5 Imputation and Feature Selection\n## 5.1 Imputing Missing Data\n## 5.2 Feature Selection\n## 5.3 Summary\n# 6 Hyperparameter Tuning\n## 6.1 Tuning Process\n## 6.2 Algorithms\n## 6.3 Summary\n# 7 Data Sampling\n## 7.1 Oversamplers\n## 7.2 Undersamplers\n## 7.3 Discussion\n## 7.4 Summary","[{\"question\":\"Why is forecasting mergers and acquisitions important for investors?\",\"answer\":\"M\\u0026A deals typically offer a significant premium to the target company, which leads to a material share-price increase when the acquisition is announced. Accurate forecasting therefore creates opportunities for investors.\"},{\"question\":\"What baseline method is used to evaluate the machine learning models?\",\"answer\":\"Logistic regression is used as a baseline, reflecting a popular statistical approach in M\\u0026A forecasting research.\"},{\"question\":\"How does the study handle missing data, feature redundancy, and class imbalance?\",\"answer\":\"Missing values are addressed with imputation so models can train properly, redundant features are removed via feature selection, and data imbalance is treated using data sampling techniques to improve validation performance.\"}]","Predicting mergers and acquisitions using machine learning - Thesis | PDF",1785729820,295,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-mergers-and-acquisitions-using-machine-learning-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-mergers-and-acquisitions-using-machine-learning-thesis/120396/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is forecasting mergers and acquisitions important for investors?","Question",{"text":75,"@type":76},"M&A deals typically offer a significant premium to the target company, which leads to a material share-price increase when the acquisition is announced. Accurate forecasting therefore creates opportunities for investors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What baseline method is used to evaluate the machine learning models?",{"text":80,"@type":76},"Logistic regression is used as a baseline, reflecting a popular statistical approach in M&A forecasting research.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study handle missing data, feature redundancy, and class imbalance?",{"text":84,"@type":76},"Missing values are addressed with imputation so models can train properly, redundant features are removed via feature selection, and data imbalance is treated using data sampling techniques to improve validation performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]