M&A Short-Term Performance Based on Elman Neural Network Model: Evidence from 2006 to 2019 in China

Abstract

Based on the event study method, this paper conducts the analysis on the short-term performance of 1302 major mergers and acquisitions (M&A) in China from 2006 to 2019 and takes the cumulative abnormal return (CAR) as the measurement index. After comparing the five abnormal return (AR) calculation models, it is found that the commonly used market model method and the market adjustment method have statistical defects while the Elman feedback neural network model is capable of good nonlinear prediction ability. The study shows that M&A can create considerable short-term performance for Chinese listed company shareholders. The CAR in window period reached 14.45% with a downward trend, which is the win-win result achieved through the cooperation between multiple parties and individuals driven by their respective rights and interests in the current macro-microeconomic environment in China.