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Machine learning for the selection of post-merger executive´s compensation
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This paper analyzes the use of a neural network to assign the total compensation an executive should have during post-merger integration by considering firm performance, firm size, similarity in major industry groups of the merger firms and executive age. The prediction model found that female executives are being underpaid on average by 24% whereas male executives are being overpaid by 22%. Furthermore, the major industry sector that underpays the most is the petroleum refining sector, whereas the sector that overpays the most is the general merchandise stores sector.
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Machine learning Mergers and acquisitions Post merger integration Compensation