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.

Ondas de fusões e aquisições e variáveis macroeconômicas

“…Vê-se, conforme Tabela 4, que além da distância encontrada entre as médias (a de baixa representa 75% da de alta), a variância também é condicional (a do estado de alta foi mais que o dobro da de baixa). Essas estatísticas ocorreram tal como apontado genericamente por Duong (2013).…”

“…Um dos raros exemplos é o estudo de Resende (2008), que, novamente com dados do Reino Unido, mas, desta vez, incorporando variáveis macroeconômicas, constatou que um alto nível de F&As teve uma relação positiva, enquanto um baixo nível possui relação negativa para com as variáveis utilizadas. Duong (2013), com dados da Austrália entre 1972 e 2004, testou três encaixes (AR, Poisson e ARMA) dentro da estrutura M-S, constatando que o de melhor desempenho foi o ARMA. Além disso, também realizou regressões em que, dentre as variáveis, tem-se a taxa de juros e o mercado de ações.…”

“…Diante do exposto, em termos empíricos, na análise do padrão de ocorrência em ondas, é esperado que um modelo não-linear seja mais apropriado do que os lineares, uma vez que além de lidar com o fato da variância não poder ser assumida como estacionária, também precisa considerar que ela pode variar abruptamente entre um estado e outro (Duong, 2013).…”

Financial statement comparability and takeover efficiency: a pitch

“…Dr Duong and Dr Truong have successfully published in the field of financial reporting quality (Duong and Evans, 2016;Duong et al, 2020). In addition, Dr Duong has successfully published in the field of mergers and acquisitions (M&As) and Dr Truong has publications in the area of corporate disclosure, governance and regulation (Bugeja et al, 2012;Chapple and Truong, 2015a;Chapple and Truong, 2015b;Dunstan et al, 2011;Duong, 2013;Duong and Izan, 2012). Our combined interest, skill and expertise provides the impetus to successfully complete the replication and extension as proposed.…”

“…Prior Australian studies document a number of factors that affect the post-acquisition performance of acquiring firms such as participation in merger waves, method of payment, takeover competition, powerful chair, compensation of bidders' non-executive directors, and corporate social responsibility of acquirers (Bugeja et al, 2017;Duong, 2013;Duong and Izan, 2012;Faff et al, 2019;Ghannam et al, 2019;Krishnamurtia et al, 2019). However, there is no evidence on how financial statement comparability of Australian target firms can affect the efficiency of acquisition decisions.…”