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Are Corporate Restructuring Events Driven by Common Factors? Implications for Takeover Prediction

Authors

  • Ronan Powell,

    1. The authors are respectively, Senior Lecturer in Finance and Lecturer in Finance at the University of New South Wales, Sydney, Australia.
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  • Alfred Yawson

    Corresponding author
    1. The authors are respectively, Senior Lecturer in Finance and Lecturer in Finance at the University of New South Wales, Sydney, Australia.
      * Address for correspondence: Ronan Powell, School of Banking and Finance, UNSW, Sydney, NSW 2052, Australia.
      e-mail: r.powell@unsw.edu.au
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  • They would like to acknowledge helpful comments received from colleagues at UNSW on earlier drafts of the paper and detailed comments made by the anonymous referee, which have greatly improved the paper. Yawson acknowledges financial support form the Sir Halley Stewart Trust. Naturally, with respect to the paper, the usual caveat applies.

* Address for correspondence: Ronan Powell, School of Banking and Finance, UNSW, Sydney, NSW 2052, Australia.
e-mail: r.powell@unsw.edu.au

Abstract

Abstract:  The paper shows that variables commonly used in takeover prediction models also help to explain the likelihood of several other restructuring events, including divestitures, bankruptcies and significant employee layoffs. This finding helps to explain the larger misclassification errors in binomial takeover prediction models commonly used in prior research. The results show that modelling takeover prediction models in a binomial setting is likely to lead to misspecification in the parameter estimates and, further, result in erroneous conclusions about the determinants of takeover likelihood. The paper shows that controlling for other restructuring events by using a multinomial framework results in consistently lower misclassification errors in out-of-sample prediction tests, when compared to the benchmark of a typical binomial model.

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