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Uncertainty of M&As under asymmetric estimation

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  • Kanungo, Rama Prasad

Abstract

Mergers and acquisitions (M&As) are often surrounded by uncertainty since they do not always deliver the anticipated results. The uncertainty ascribed to the outcomes of M&As is not a boundary concept, rather guides firms’ decision in favour of or against undertaking M&As. Typically, M&As suffer from estimation biases. Moreover, M&As are always treated synonymously as a single entity. Thus, evaluating M&A as a single event under traditional stationary specifications is not adequate for capturing the disaggregate effect of an individual event. This study examines announcement returns of both events individually under the asymmetric specification. Asymmetric specification captures the variance of uncertainty associated with the event outcome for each set of events. The findings further document acquirers (acquisition sample) register pre-announcement wealth loss, but not so with mergers. The cross-sectional analysis indicates that the announcement returns under asymmetric specification are associated with the deal characteristics of both sets of events, except deal premium.

Suggested Citation

  • Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
  • Handle: RePEc:eee:jbrese:v:122:y:2021:i:c:p:774-793
    DOI: 10.1016/j.jbusres.2020.07.029
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    More about this item

    Keywords

    Uncertainty; Risk; Mergers and acquisitions; GJR-GARCH; Cross-sectional study;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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