Aggregate Australian Takeovers: A Review of Markov Regime Switching Models

Abstract

This article reviews the case of modeling merger waves in the Australian market for the period 1972–2004. Three Markov switching models are examined, the Gaussian AR(1), Poisson AR(1), and State-Space autoregressive moving average (ARMA) (1,1), to find which gives the best fit. The State-Space Markov switching ARMA(1,1) model is found to be the best for describing Australian takeover activity as estimation results based on it have a lower Bayesian information criterion score than the other two models. Each model's ability to predict a ‘wave’ is then tested by including its estimated probability in a macroeconomic model to explain merger activity. The State-Space model also performs better because the inclusion of its estimated probability substantially increases the explanatory power of the regression model (measured by the regression adjusted R2). In addition, it predicted a takeover wave in 2009, which was closer to the actual incidents of takeover activity in the market at that time than the predictions of the other two models. The results are robust when the measure of takeover activity is changed from the number of takeover bids to the proportion of takeover bids relatively to the number of exchange-listed companies. JEL classification: G34, C32.