[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121751-en":3,"doc-seo-121751-105":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},121751,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Comparative Study of Logistic Regression and Machine Learning to Identify Acquirer Success Factors","This thesis develops and tests two research questions addressing explanations of shareholder wealth creation in mergers and acquisitions. It identifies pre-acquisition success factors and evaluates their practical usefulness for managers seeking to acquire other firms. Building on prior M&A frameworks about persistent acquisition failings, the study connects agency problems, limited practitioner relevance, and the need for improved methods. Results show financial ratios matter, and both logistic regression and machine learning uncover significant drivers of acquirer success.","Norwegian School of Economics Bergen, Spring 2023  \nA Comparative Study of Logistic Regression and Machine Learning to Identify Acquirer Success Factors  \nMartin Hol Hellesøe & Alexander Hellevik Supervisor: Jonas Andersson  \nMaster of Science in Economics and Business Administration Majors: Business Analysis and Performance Management and Business  \nAnalytics  \nNORWEGIAN SCHOOL OF ECONOMICS  \nThis thesis was written as a part of the Master of Science in Economics and Business Administration at NHH. Please note that neither the institution nor the examiners are responsible – through the approval of this thesis – for the theories and methods used, or results and conclusions drawn in this work.  \nAcknowledgements  \nAs our enriching journey at NHH comes to a close, we are filled with a deep sense of gratitude and honor. We feel privileged to have attended this exceptional institution with brilliant professors and a remarkable community of students, and our time spent here has been truly invaluable.  \nUndertaking this paper has been a demanding yet fulfilling experience. Our thesis has required rigorous pre-processing and cleaning of a large dataset, as well as embracing state-of-the-art analytic tools. Our hope is that this paper can contribute to the literature on M&A research by employing novel machine learning techniques to address the current limitations of the field.  \nWe would like to convey our sincerest gratitude to Jonas Andersson, our supervisor, for his support, insightful discussions, and valuable guidance throughout this project. His expertise and profound knowledge have been instrumental in steering our research in the right direction. Finally, we would like to thank our family and friends for their unconditional support.  \nNorwegian School of Economics  \nBergen, May 2023  \nMartin Hol Hellesøe Alexander Hellevik  \nAbstract  \nThis paper develops, presents and tests two research questions that contribute to current explanations of shareholder wealth creation in mergers and acquisitions transactions. We (1) identify pre-acquisition success factors and (2) evaluate their practical usefulness for managers seeking to acquire other firms. We build on Cartwright and Schoenberg (2006)’s framework for understanding the persistent failings of acquisitions. This includes agency problems, research not reaching practitioners and the need for new methods to explain M&A success. Our findings indicate that financial ratios play a significant role in determining the success of acquirers. We develop and validate both a logistic regression and two machine learning models, revealing significant factors that impact acquirer success.  \nOur results from the logistic regression mirror those from much of existing literature, identifying several significant factors for acquirer success. Furthermore, we find support for the prevalence of agency problems in acquisition decisions (Jensen, 1986; Maloney et al. , 1993) and the internal market hypothesis (Stein, 1997; Shin and Stulz, 1998) . Yet, our results also conflict with existing literature on several points. While our logistic regression reveals statistically significant acquirer success factors, its poor predictive performance makes it impractical for managers in real-world applications. In contrast, our machine learning methods identify complex non-linear relationships and discriminates well between successful and unsuccessful acquirers, resulting in ROC curves with excellent AUC scores.  \nThis supports the argument that the true relationships between acquirer success and the predictors are too complex for a logistic regression approach, even though much of existing literature on the subject builds on the logistic regression. We thus provide a possible explanation for why M&A success rates are still low, despite the extensive research on the subject. Finally, we argue that the key to enabling managers to use machine learning models directly lies in the adoption of partial dependence plots,","cbCaii4vZJnkH1Ns","https://ap.wps.com/l/cbCaii4vZJnkH1Ns","pdf",2499797,1,83,"English","en",105,"# Introduction\n## Agency problems in acquisitions\n## Do acquisitions create shareholder value?\n## Why do managers initiate acquisitions?\n## Is M&A research reaching the practitioner?\n## The quest for new theory, methods and variables\n## Which factors impact acquisition success?\n## The organizational culture and behavioral approach\n## Research question development\n## Determining success factors\n## Determining the usefulness of the results\n# Data\n## Data Sample Structure\n## COMPUSTAT Database\n## Center for Research in Security Prices Database\n## Fama-French Database\n## Final data sample\n## Data delimitation\n## Descriptive statistics\n# Methodology\n## Financial measures of performance\n## Measuring acquirer success\n## The models\n## Logistic regression\n## Random forest\n## Gradient Boosting Machine (GBM)\n## Validation and interpretation methods\n## Receiver Operating Characteristic (ROC) curve\n## Variable Importance Plot\n## Partial Dependence Plot","[{\"question\":\"What research questions does the thesis investigate?\",\"answer\":\"It identifies pre-acquisition success factors and evaluates their usefulness for managers when acquiring other firms.\"},{\"question\":\"Which models are used to study acquirer success?\",\"answer\":\"The thesis develops and validates one logistic regression model and two machine learning models: Random Forest and Gradient Boosting Machine (GBM).\"},{\"question\":\"Why do the results suggest machine learning is more practical than logistic regression?\",\"answer\":\"Logistic regression shows statistically significant factors but has poor predictive performance, while machine learning captures complex non-linear relationships and discriminates well between successful and unsuccessful acquirers.\"}]","A Comparative Study of Logistic Regression and Machine Learning to Identify Acquirer Success Factors | 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research questions does the thesis investigate?","Question",{"text":75,"@type":76},"It identifies pre-acquisition success factors and evaluates their usefulness for managers when acquiring other firms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are used to study acquirer success?",{"text":80,"@type":76},"The thesis develops and validates one logistic regression model and two machine learning models: Random Forest and Gradient Boosting Machine (GBM).",{"name":82,"@type":73,"acceptedAnswer":83},"Why do the results suggest machine learning is more practical than logistic regression?",{"text":84,"@type":76},"Logistic regression shows statistically significant factors but has poor predictive performance, while machine learning captures complex non-linear relationships and discriminates well between successful and unsuccessful 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