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

The debate about the efficacy of mergers and acquisitions as a growth strategy in terms of ex-post value creation has been developing for decades. This paper aims to develop an artificial neural network to examine trends in the financials and mark the potential sources of value creation in M&A; mergers in high-tech industry for the period of 2011-2021 are used to train and cross-validate the ANN. The findings show that ANN is able to predict the financial market performance of mergers with high accuracy, and that important determinants of the long-term market value growth of the businesses in the sample include the cross-industry nature of the merger, profitability of both parties, the bidder’s size, and its capitalization before the merger occurs. Hence, we demonstrate that ANN can be implemented as a highly efficient model for analyzing complex financial events due to its flexibility and lack of prior assumptions about the data