Aggregate Australian takeovers: A review of Markov regime switching models

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 Marko...

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Main Author: Duong, Lien
Format: Journal Article
Published: Wiley-Blackwell Publishing Asia 2013
Online Access:http://hdl.handle.net/20.500.11937/35669
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author Duong, Lien
author_facet Duong, Lien
author_sort Duong, Lien
building Curtin Institutional Repository
collection Online Access
description 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.
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institution Curtin University Malaysia
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publishDate 2013
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spelling curtin-20.500.11937-356692019-02-19T05:36:13Z Aggregate Australian takeovers: A review of Markov regime switching models Duong, Lien 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. 2013 Journal Article http://hdl.handle.net/20.500.11937/35669 10.1111/irfi.12015 Wiley-Blackwell Publishing Asia fulltext
spellingShingle Duong, Lien
Aggregate Australian takeovers: A review of Markov regime switching models
title Aggregate Australian takeovers: A review of Markov regime switching models
title_full Aggregate Australian takeovers: A review of Markov regime switching models
title_fullStr Aggregate Australian takeovers: A review of Markov regime switching models
title_full_unstemmed Aggregate Australian takeovers: A review of Markov regime switching models
title_short Aggregate Australian takeovers: A review of Markov regime switching models
title_sort aggregate australian takeovers: a review of markov regime switching models
url http://hdl.handle.net/20.500.11937/35669