Merger waves and the structure of merger and acquisition time-series

Robert Town

93 citations0/0 files storedJournal of Applied Econometrics

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Corporate Finance and Governance Financial Markets and Investment Strategies Merger and Competition Analysis

Abstract

What is the best characterization of mergers and acquisitions time-series? The traditional response is that mergers occur in ‘waves’. I estimate a two-state, Markov switching-regime model which should capture wave structure if it is present in the data. Linear and nonlinear diagnostics tests suggest that the switching regime model fits the data well, and better than ARIMA models. Said differently, the underlying pattern in the M&A data can be characterized by dichotomous shifts between high and low levels of activity. In addition, objective inferences about the precise dates for these waves are available through a nonlinear filter.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1977Time Series Analysis: Forecasting and Control 1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1977Time Series Analysis: Forecasting and Control 1971Industrial Market Structure and Economic Performance.1981Principles of Corporate Finance.1990Industrial market structure and economic performance 1979Forecasting Economic Time Series

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2000The Market that Antitrust Built: Public Policy, Private Coercion, and Railroad Acquisitions, 1825 TO 1922 1998Sequential horizontal mergers 2010Markowitz's Mean-Variance Asset–Liability Management with Regime Switching: A Multi-Period Model 2007Incentive Asymmetries in the Mergers and Acquisitions Process*2001Waves and persistence in merger and acquisition activity 2010The impact of the macroeconomic environment on merger activity: evidence from US time-series data

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