Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Abstract

Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.

Post-Merger Financial Performance – A Study of High-Tech Companies in the United States using Artificial Neural Networks

“…Specifically, by assessing a "withdrawn takeover prediction model" using a neural network with an enhanced logit activation function, they present the most significant variables based on importance analysis and showcase the superiority of neural networks compared to traditional forecasting techniques. Bi and Zhang [2021] using neural networks provide more insight into the issue by assessing and identifying additional variables that contribute to M&A failure prediction models. Applying neural networks, Zhu and Meng [2021] try to assess and interpret synergy effects by analyzing the rate of changes in the selected financial ratios that represent overall post-M&A performance.…”