Are Corporate Restructuring Events Driven by Common Factors? Implications for Takeover Prediction
发现常用于收购预测的变量也能解释资产剥离、破产和裁员等其他重组事件，说明传统二元收购预测模型存在误分类问题，而采用多项模型能显著降低预测误差。
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.