Abstract

The growth-resources hypothesis has been widely used empirically to develop models to predict takeover targets. This hypothesis states that firms with low (high) growth and high (low) resources make good potential targets. In this thesis I test this hypothesis in the context of predicting future acquirers. Using binary, multinomial and two-step logit models, I document that a firm’s growth is actually the main factor leading to acquisitions, contrary to the generally accepted view that a firm’s cash position is one of the primary factors associated with future acquisition activity. As such, the growth-resources imbalance hypothesis is partly validated: high growth-low resources firms are the most likely to make a bid while low growth-high resource firms which were expected to be the most likely to acquire, are, in fact, the least likely to take over other firms. These results are robust to different measures of the imbalance variables and are consistent with the market timing theory. In a hold out sample, I find that the binary logit models show higher prediction accuracy than the multinomial logit models.