M&A PREDICTIONS: RECONSIDERING THEIR VALUE, END-USERS, AND METHODOLOGIES

The article explores market participants who may benefit from M&A predictions and how their goals may impact the requirements for M&A predictions. These participants (also called end-users of M&A predictions) are company shareholders considering selling their business, shareholders and company management considering acquiring one or a few other companies, shareholders and company management competing with potential M&A targets or buyers, and advisory firms providing investment banking services in the industries where M&A deals occur. Analyzing their goals while applying M&A predictions, the article concludes that the requirements for M&A predictions can be changed depending on these goals. These end-users may benefit from M&A predictions even if the deals they predict won’t happen. These end-users have the potential to significantly influence the outcome of the M&A events they are predicting. The M&A prediction quality criterion imposed by earlier research - the M&A prediction is correct only when a predicted M&A deal happens - can be relaxed depending on the end-users of M&A predictions and their goals. An M&A prediction will be more valuable for end-users if it includes information on both potential targets and potential buyers. M&A prediction may have a more significant value for end-users if it allows for predicting multiple counterparties for a potential party to an M&A deal. The article analyses the existing theoretical basis behind the M&A predictions and concludes that these theories are insufficient to cover all possible reasons behind the deals from the buyers’ and sellers’ perspectives – additional reasons exist that trigger M&A deals. Also, the existing theories are not always proven by the existing research, showing that their correctness may depend on the context. The article explores the current stance of M&A prediction methodologies, such as: binary state prediction models based on a linear combination of independent variables, starting from the earlier works focused on prediction variables for M&A targets to later works dedicated to adding new company-specific prediction variables of the targets and reflecting the context; alternative computational techniques to predict M&A targets, like non-parametric computational techniques, including machine learning; methodologies to predict M&A buyers; methodologies to predict pairs of buyers and targets, researching the relatedness between them. The article concludes that the M&A prediction methodology shall consider and reflect additional motivations for the M&A deal for targets and buyers and shall always include the context. Predicting only targets seems like a one-sided approach. On the contrary, predicting both parties of the deal seems like a promising prediction methodology. Non-parametric computational techniques based on a broader range of prediction variables, reflecting the motivations of the M&A deal’s parties and the context, look like a promising basic prediction methodology that should be further developed. Testing new M&A prediction methodologies within a specific sector for a longer time looks promising for increasing the robustness of the model's prediction ability. Finally, out-of-sample tests done over a longer time are necessary to check the models’ prediction ability.