Predicting Nordic Takeover Targets: A Binary Logit Analysis

Abstract: This thesis aims to investigate if certain firm characteristics increase the likelihood of a company being acquired and if these factors can successfully predict which firms become subjects to takeovers in the Nordic setting. We compare data on a sample of Nordic targets and non-targets between the years 2012 and 2021, and test hypotheses regarding firm and industry characteristics, as well as market sentiments through binary logit models. Finally, we test the predictive ability of the best model on a separate sample of targets and non-targets from 2022. Our study suggests that firms with undervalued assets, lower growth and increased trading volume have a higher likelihood of becoming Nordic targets. Our prediction model has an overall 55.14% accuracy in its classification and is able to predict targets with a 50% success rate.