Do reverse stock splits indicate future poor stock performance?
Introduction
After the burst of high tech bubble in the year 2000, the stock prices of many firms plummeted. The prices of once high flying stocks such as Lucent Technology and Nortel Networks dropped more than 90%. As a result, some firms engaged in reverse stock splits to re-price their stock in the market. In a reverse split the number of shares outstanding (as well as, generally, the number of shareholders) decreases and the per share market price increases, while the opposite is true for a forward (normal) split. Both forward and reverse splits are considered as non-economic events since the total market capitalization of the stock is essentially the same on a pre- and post-split basis. In this study, we focus on the underlying rationales for reverse splits.
In general, firms engaging in reverse splits are sending a signal to the investment community that could reflect either of two possible realities (Peterson and Peterson, 1992, Vafeas, 2001). First, the reverse split could be a desperate move by a sinking firm to increase its low-stock price to a more respectable trading range, especially in the case of an imminent de-listing due to too low a share price, and thereby gain respect from the investment and lending communities. Alternatively, a firm with solid financial fundamentals may use a reverse split to reposition its low-priced stock to be more congruent with the shares of similar firms in the market and thereby attract a broader clientele of potential shareholders.
Most previous research concerning reverse splits has not tried to distinguish between the two groups of firms and, thus, suggested that reverse splits would result in negative abnormal returns. Desai and Jain (1997) reported:
“For stock splits, on average, the 1- and 3-year buy- and-hold abnormal returns after the announcement month are 7.05% and 11.87%, respectively. For reverse splits, the corresponding abnormal returns are −10.76% and −33.90%.”
The study is based on 5596 stock split and 76 reverse split announcements made during the period 1976–1991.
Our study presents the results of a two-part research effort. First, this study classifies reverse splitting firms into two groups, firms that declare bankruptcy within 2 years of reverse split and firms that remain solvent. We assert that 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 and attract a broader stockholding clientele. Two alternative approaches are employed for classifying reverse splitting firms into the two groups, Altman’s Z-scores and artificial neural networks. Both approaches are used to measure the likelihood of bankruptcy based on the financial health of the firm. We anticipate that the weak firms will be more likely to declare bankruptcy within 2 years of reverse split while solid firms will remain solvent and perform well in the stock market. A comparison is then made of the relative success of Z-scores and neural networks in forecasting bankruptcy and classifying the reverse splitting firms into two groups. Second, we evaluate the stock returns of the two groups classified by both neural networks and Z-scores with that of the S & P 500 index 2-year subsequent reverse splits. The results of the 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 corporate bankruptcy and performance.
The rest of the article is organized as follows, In Section 2, we review the literature of reverse splits studies. In Section 3, we briefly describe Altman’s Z-scores and artificial neural networks approaches used in this study to classify reverse splitting firms into the two groups. This is followed by discusses of the sample and experiment design of the study. Section 4 presents the results and analysis, and Section 5 concludes the paper with our preliminary findings.
Section snippets
Evidence on bankruptcies and reverse splits
Over the years, there has been much written on the individual topics of bankruptcy prediction (Altman, 1968, Altman, 2000, Altman et al., 1977, Beaver, 1967, Collins, 1980) and reverse stock splits (Desai and Jain, 1997, Han, 1995, The Napeague Letter, 2002, Vafeas, 2001). However, there is little research into the relationship between reverse stock splits and corporate bankruptcy as well as performance.
Early seminal studies concerning bankruptcy prediction were made by Altman (1968) and Beaver
Methodologies and experiment design
In the following subsection, we present an overview of the Altman’s Z-score model and the artificial neural networks.
Analysis of results
We first performed bankruptcy prediction using the three models, Altman’s Z-Score, 5-factor neural network, and 10-factor neural network models. As suggested by Altman, the cut off ratios for Z-scores are: Z < 1.81 for bankruptcy, 2.99 ⩾ Z ⩾ 1.81 for zone of ignorance, and Z 2.99 for non-bankrupt. The two neural network models that we constructed implemented a sigmoid function to normalize the output to a value between zero and one. Following the convention used for neural network dichotomous
Conclusions and limitations
There has been little research into the relationship between reverse stock splits and corporate bankruptcy as well as the rationale for a reverse split. This study classifies reverse splitting firms into two groups, those declaring bankruptcy within 2 years of reverse split and those remaining solvency. 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
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2016, Investment Management and Financial Innovations