[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127299-en":3,"doc-seo-127299-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127299,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","Are acquirer stock price reactions to M&A announcements in any way predictable? - A machine-learning analysis","The study tests whether acquirer stock price reactions to M&A deal announcements can be forecasted using ex ante characteristics of the acquirer, target, deal structure, and macroeconomic conditions. Machine-learning methods are evaluated with out-of-sample testing and standard cross-validation across parametric and nonparametric models. Overall predictability is low, but nonparametric approaches show some forecasting ability while parametric models do not. Feature-importance results highlight key predictors such as acquirer size and relative deal size.","This is a repository copy of Are acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/232439/](https://eprints.whiterose.ac.uk/id/eprint/232439/)  \n[Version: Accepted Version](Version: Accepted Version)  \nArticle:  \nQuariguasi Frota Neto, J., Bozos, [K. orcid.org/0000-0003-2914-6495](K. orcid.org/0000-0003-2914-6495), Dutordoir, M. et al.(1 more author) (Accepted: 2025) Are acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis. Journal of the Operational Research Society. ISSN: 0160-5682 (In Press)  \nThis is an author produced version of an article accepted for publication in Journal of the Operational Research Society, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nAre acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis  \nJoão Quariguasi Frota Neto  \nUniversity of Manchester, Manchester, UK  \nKonstantinos Bozos  \nLeeds University Business School, University of Leeds, Leeds, UK  \nMarie Dutordoir  \nUniversity of Manchester, Manchester, UK  \nKonstantinos Nikolopoulos  \nUniversity of Durham, Durham, UK  \nABSTRACT  \nWe examine whether acquirer stock price reactions to M&A deal announcements can be forecasted based on ex ante acquirer, target, deal, and macroeconomic characteristics. We employ machine learning methodologies with out-of-sample testing and standard cross-validation procedures to assess the forecasting accuracy of various parametric and nonparametric models. While overall predictability is low, nonparametric models exhibit some ability to forecast acquirer stock price reactions to M&A announcements, whereas parametric models do not. Feature importance analyses reveal that a handful of predictors, including acquirer size and (relative) deal size, contribute most to the predictions. Our findings have practical implications for corporate managers and various corporate stakeholders.  \nAre acquirer stock price reactions to M&A announcements in anyway predictable? A Machine-Learning analysis.  \nAnonymous  \nSeptember 19, 2025  \nAbstract  \nWe examine whether acquirer stock price reactions to M&A deal announcements can be forecasted based on ex ante acquirer, target, deal, and macroeconomic characteristics. We employ machine learning methodologies with out-of-sample testing and standard cross-validation procedures to assess the forecasting accuracy of various parametric and nonparametric models. While overall predictability is low, nonparametric models exhibit some ability to forecast acquirer stock price reactions to M&A announcements, whereas parametric models do not. Feature importance analyses reveal that a handful of predictors, including acquirer size and (relative) deal size, contribute most to the predictions. Our findings have practical implications for corporate managers and various corporate stakeholders.  \nKeywords: Mergers and Acq","cbCaib8amo86RLl9","https://ap.wps.com/l/cbCaib8amo86RLl9","pdf",640819,2,1,47,"English","en",105,"# Abstract\n# Introduction\n## Motivation and research question\n## Stock-price impact of M&A announcements\n# Methodology\n## Machine-learning models and validation\n# Findings\n## Predictability results\n## Feature importance and key predictors\n# Implications","[{\"question\":\"Can acquirer stock price reactions to M\\u0026A announcements be forecasted?\",\"answer\":\"The analysis finds overall predictability is low, but some forecasting ability exists using nonparametric machine-learning models.\"},{\"question\":\"Do parametric and nonparametric models perform differently?\",\"answer\":\"Nonparametric models show some ability to forecast acquirer stock price reactions, whereas parametric models do not show comparable predictive performance.\"},{\"question\":\"Which factors matter most for the predictions?\",\"answer\":\"Feature-importance analysis indicates that a handful of predictors—especially acquirer size and relative deal size—contribute most to the forecasts.\"}]","Are acquirer stock price reactions to M&A announcements in any way predictable? 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