Title: Prediction of abnormal returns on bidding firms in mergers and acquisitions

Authors: Jianyu Ma; Yun Chu; John C. Stewart

Addresses: Department of Finance, Rockwell School of Business, Robert Morris University, Pittsburgh, USA ' Department of Marketing, Rockwell School of Business, Robert Morris University, Pittsburgh, USA ' Department of Computer and Information Systems, School of Data Intelligence and Technology, Robert Morris University, Pittsburgh, USA

Abstract: This study investigates the predictability of abnormal returns for bidding firms following M&A announcements in China, focusing on deal characteristics and firm size. Using 1,253 transactions from 2010-2018, we apply a K-nearest neighbours (KNN) model to classify whether post-announcement abnormal returns are positive or negative across Day +1, Day +2, and CAR (1, 2) windows. Predictions draw on four variables: form of acquisition, payment method, industry-relatedness, and firm size. Results show strong predictability for small acquirers - those in the bottom 15%-30% by assets - achieving over 70% accuracy across windows. Predictive power declines with larger firms and approaches randomness for the full sample. The findings indicate that abnormal returns are more systematically predictable when involving smaller bidders and clear structural deal features, offering insights for investors and managerial decision-making in short-term market reactions.

Keywords: merger and acquisition; abnormal return prediction; K-nearest neighbours; KNN algorithm; machine learning; China Stock Market; firm size effect.

DOI: 10.1504/IJRM.2026.155516

International Journal of Revenue Management, 2026 Vol.16 No.1/2, pp.1 - 13

Received: 25 Oct 2025
Accepted: 09 Dec 2025
Published online: 04 Aug 2026 *

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