Are acquirer stock price reactions to M&A announcements in any way predictable? - A machine-learning analysis

Aug 5, 2026

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages47

Reading Time1 h 58 min

AccessFree to download

Abstract

Introduction

Motivation and research question

Stock-price impact of M&A announcements

Methodology

Machine-learning models and validation

Findings

Predictability results

Feature importance and key predictors

Implications

This is a repository copy of Are acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis. White Rose Research Online URL for this paper: [https://eprints.whiterose.ac.uk/id/eprint/232439/](https://eprints.whiterose.ac.uk/id/eprint/232439/) [Version: Accepted Version](Version: Accepted Version) Article: Quariguasi 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) This 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. Reuse This 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/) Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request. [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/) Are acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis João Quariguasi Frota Neto University of Manchester, Manchester, UK Konstantinos Bozos Leeds University Business School, University of Leeds, Leeds, UK Marie Dutordoir University of Manchester, Manchester, UK Konstantinos Nikolopoulos University of Durham, Durham, UK ABSTRACT We 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. Are acquirer stock price reactions to M&A announcements in anyway predictable? A Machine-Learning analysis. Anonymous September 19, 2025 Abstract We 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. Keywords: Mergers and Acq

FAQs

Can acquirer stock price reactions to M&A announcements be forecasted?

The analysis finds overall predictability is low, but some forecasting ability exists using nonparametric machine-learning models.

Do parametric and nonparametric models perform differently?

Nonparametric models show some ability to forecast acquirer stock price reactions, whereas parametric models do not show comparable predictive performance.

Which factors matter most for the predictions?

Feature-importance analysis indicates that a handful of predictors—especially acquirer size and relative deal size—contribute most to the forecasts.

Aug 5, 2026

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