Understand Corporate Rationales for Engaging in Reverse Stock Splits - A Data Mining Application

Abstract

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and reverse stock splits. However, there is little research into the relationship between reverse stock splits and corporate performance as well as bankruptcies. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups, those declaring bankruptcy within 2 years and those remaining solvent. The apparent rationales for engaging in reverse splits differ between the two groups, i.e., weak firms attempting to increase their stock price while solid firms seeking to reposition their stock in the market. Two alternative approaches, Altman's Z-scores and artificial neural networks, are used for classifying reverse splitting firms into the two groups. A comparison is then made of the relative success of Z-scores and neural networks in the classification. This study should generate an understanding of corporate rationale for engaging in reverse splits and the relative success of Z-scores and artificial neural networks in forecasting the two groups.