Conventional and Neural Network Target-Matching Methods Dynamics: The information technology mergers and acquisitions market in the USA

In this paper, we explore, apply, and compare two types of target screening classification techniques—the NN and the traditional logistic regression (LR) M&A forecasting techniques—in terms of successful target prediction in the IT M&A market for the USA. We provide for a demonstration of the growing prospects of the use of an NN to systematize feature engineering from raw time series, in a more methodical way as a result of the strategic change in the types of digital commodities that decision-makers demand. In that respect, and within the context of M&As, predicting which companies will become takeover targets and the ability to discriminate between high‑ and low-quality targets is very important for managers and financiers, as well as for regulators and competition market committees. Our findings provide valuable insights to guide managers in financial and other organizations to improve their performance through suitable target (or nontarget) screening methods.