Unbalanced data, type II error, and nonlinearity in predicting M&A failure

The traditional forecasting methods in the M&A data have three limitations: first, the outcome of M&A deal is an event with a small probability of failure, second, the consequences of misclassifying failure as success are much more severe than those of misclassifying success as failure, and...

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Bibliographic Details
Published inJournal of business research Vol. 109; pp. 271 - 287
Main Authors Lee, Kangbok, Joo, Sunghoon, Baik, Hyeoncheol, Han, Sumin, In, Joonhwan
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.03.2020
Subjects
Generalized logit model
Logit and Probit model
Machine learning
Merger and acquisition
Neural network
Nonlinearity prediction
Unbalanced data
Nonlinearity prediction
Merger and acquisition
Machine learning
Generalized logit model
Neural network
Logit and Probit model
Unbalanced data
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Summary:The traditional forecasting methods in the M&A data have three limitations: first, the outcome of M&A deal is an event with a small probability of failure, second, the consequences of misclassifying failure as success are much more severe than those of misclassifying success as failure, and third, the nonlinear and complex nature of the relationship between predictors and M&A outcome could limit the advantage of logistic regression. To overcome these limitations, we develop a forecasting model that combines two complementary approaches: a generalized logit model framework and a context-specific cost-sensitive function. Our empirical results demonstrate that the proposed approach provides excellent forecasts when compared with traditional forecasting methods.
ISSN:0148-2963
1873-7978
DOI:10.1016/j.jbusres.2019.11.083