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Waves and Persistence in Merger and Acquisition Activity

  • John T. Barkoulas

    (Louisiana Tech University)

  • Christopher F. Baum

    ()

    (Boston College)

  • Atreya Chakraborty

    (Charles River Associates)

Does merger and acquisition (M&A) activity occur in waves, that is, are there oscillations between low and high levels of M&A activity? The answer to this question is important in developing univariate as well as structural models of explaining and forecasting the stochastic behavior of M&A activity. There is evidence to suggest that aggregate U.S. time-series data on merger and acquisition (M&A) activity exhibit a "wave" behavior, which has been modeled by fitting either a two-state Markov switching-regime model or a sine-wave model to the data. This study provides an alternative characterization of the temporal patterns in M&A as a nonlinear process with strongly persistent or long-memory dynamics. The apparent level changes or partial cycles of differing magnitudes in aggregate M&A time series are consistent with an underlying data generating process exhibiting long memory. Time- and frequency-domain estimation methods are applied to a long M&A time series constructed by Town (1992), covering approximately a century of merger activity in the U.S. economy. We find significant evidence of long-term cyclical behavior, nonperiodic in nature, in the M&A time series, even after accounting for potential shifts in the mean level of the series. A shock to M&A activity exhibits significant persistence as it is damped at the very slow hyperbolic rate, but it eventually dissipates. We provide both theoretical and empirical rationales for the presence of fractional dynamics with long-memory features in M&A activity. Theoretically, long-term dependence may be due to persistent differences in firm valuation between stockholders and nonstockholders following an "economic disturbance," as suggested by Gort (1969). Empirically, long-memory dynamics in M&A activity may reflect the statistical properties of fundamental factors underlying its behavior, as several of the proposed determinants of M&A activity have been shown to exhibit strong persistence.

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File URL: http://fmwww.bc.edu/EC-P/wp396.pdf
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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 396.

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Length: 19 pages
Date of creation: 01 Dec 1997
Date of revision: 14 Dec 1999
Publication status: published, Economics Letters, 70, 237-243, 2001.
Handle: RePEc:boc:bocoec:396
Contact details of provider: Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Phone: 617-552-3670
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/EC/
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  1. Shea, Gary S, 1991. "Uncertainty and Implied Variance Bounds in Long-Memory Models of the Interest Rate Term Structure," Empirical Economics, Springer, vol. 16(3), pages 287-312.
  2. Baillie, Richard T & Bollerslev, Tim, 1994. "The long memory of the forward premium," Journal of International Money and Finance, Elsevier, vol. 13(5), pages 565-571, October.
  3. Town, R J, 1992. "Merger Waves and the Structure of Merger and Acquisition Time-Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S83-100, Suppl. De.
  4. Diebold, Francis X. & Lindner, Peter, 1996. "Fractional integration and interval prediction," Economics Letters, Elsevier, vol. 50(3), pages 305-313, March.
  5. Golbe, Devra L & White, Lawrence J, 1993. "Catch a Wave: The Time Series Behavior of Mergers," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 493-99, August.
  6. Gort, Michael, 1969. "An Economic Disturbance Theory of Mergers," The Quarterly Journal of Economics, MIT Press, vol. 83(4), pages 624-42, November.
  7. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
  8. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
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