Impact and Value Of Reverse Stock Splits
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Impact and Value Of Reverse Stock Splits

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Impact and Value Of Reverse Stock Splits Impact and Value Of Reverse Stock Splits Document Transcript

  • Impact and Value Of Reverse Stock Splits by Mohsin Memon An honors thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science Undergraduate College Leonard N. Stern School of Business New York University May 2003 Professor Marti G. Subrahmanyam Professor Damodaran Faculty Adviser Thesis Advisor
  • Executive Summary: Stock splits are examples of illusory changes because they should not affect the value of the firm. In a reverse stock split, a firm consolidates its shares by a certain factor and also increases its stock price by the same factor. As a result, the market capitalization of the company does not change and therefore this should have no impact on the value of the company. However, past research on forward stock splits has shown that there is a positive market response on the date of announcement which can be explained by an optimal trading range theory and positive information effects. On the other hand, little research has been done on the impact of reverse stock splits because they have not been a tool used frequently by corporate managers until recently. The purpose of this study is to test the market response to reverse splits with a recent sample and to explain what causes the abnormal returns if any exist. There are many possible motives for companies to reverse split their stock. The discretionary motivations can be to reduce registrar fees and shareholder mailing costs, “squeeze” out shareholders, enhance image or improve marketability. Also, with the current bear market after a period of extreme overvaluations, a non-discretionary motivation to reverse split has become prominent. The NYSE and Nasdaq require a $1 price minimum to remain on their exchange and if a company slips below this level, it will be in danger of being delisted. The fact that many companies have slipped below that $1 level has led to many of them being forced to reverse split their stock to remain on a major exchange. In accordance with some of the research that has been done on reverse splits, I believe that I will find that firms announcing reverse splits will show a statistically significant decline in stock price on the day of the announcement. More specifically, I
  • hypothesis that firms which are forced to reverse split because of delisting fears will perform relatively better than firms that chose to reverse split for discretionary purposes. The reason I believe this is because I feel that the companies which are forced to reverse split actually have the most to gain from the increased marketability since they will also take away the fear of being delisted and no longer be considered “penny-stocks.” Secondly, I hypothesis that companies which have negative earnings before a reverse split will perform worse than companies with positive earnings. Companies which have negative pre-split earnings will send a strong informational signal to investors that these poor earnings are here to stay or else they would not have had to artificially raise their prices. After doing a descriptive analysis on my sample, I saw that most companies that did reverse splits were on the Nasdaq and were trading at less than $1 signaling a non- discretionary split. Sector and year analysis saw a decrease in reverse splits in the boom period and a drastic increase in the last 2 years, especially in the technology sector. In the daily and monthly returns around the announcement dates, I got statistically significant negative returns on the announcement date indicating that reverse splits do have some impact on value. I then ran a multiple regression on the announcement day return with variables indicating discretionary or non-discretionary split, negative or positive earnings per share two quarters before the announcement, split factor, size, and if they are on the Nasdaq or NYSE. Before I analyzed the results, I individually looked at three factors extensively. The first, marketability, was analyzed through average split adjusted trading volumes before and after the reverse split. Results showed a statistically significant increase in volume which increases the liquidity and marketability of the stock. Also, I looked at the
  • price range of companies before and after the split and saw that most companies were below a dollar before the split and above it after. However, the post-split price did not go over the $5 threshold which many institutional investors do not look below, so it seemed as though their main motivation was not as much for marketability but rather desperation to stay on a major exchange. Second, I looked at companies which were forced to reverse split (pre-split price below $1) and those which did it for discretionary purposes (pre-split price above $1). The results showed statistically significant negative abnormal returns for both, but surprisingly a much higher negative return for non-discretionary companies. This can be explained by the fact that a majority of non-discretionary companies actually end up delisting anyways which means that their stock underperforms the market after the reverse split. Investors read this as a sign that these companies are overvalued or distressed and that their price is going to drop which causes the negative impact on the announcement date. For the next factor, I found that companies which had negative earnings prior to the reverse split performed a lot worse than companies which had positive earnings - however the latter still had statistically significant negative returns. The negative earning companies send a strong signal that they are going to continue to perform poorly and are therefore distressed. Finally, I decided to test positive earning companies that reverse split for discretionary reasons and actually got a slightly significant positive return on the announcement date implying that once the two main negative factors are taken out, the benefits of an increase in marketability can be seen.
  • The multiple regression results show that the non-discretionary factor drowns out the effects of most of the other variables and is the major cause for the negative returns on the announcement date. In conclusion, an announcement of a reverse split causes a negative impact if the company has either negative earnings prior to the announcement or is performed for non- discretionary reasons. The investors read the announcement of a reverse split for these companies as an indication of distress and overvaluation. Finally, a trading strategy can be created if every stock that hits $1 on the Nasdaq is shorted because a good percentage of them reverse split within 6 months and as indicated in my analysis, will be expected to have highly negative returns on the announcement date.
  • INTRODUCTION: Stock splits, forward and reverse, are examples of illusory changes to the financial position of a firm. Illusory changes refer to accounting adjustments and window-dressing techniques that, by themselves, have no impact on the firm’s future cash flows. On the other hand, a real change is one that directly affects the earnings and risk factor of the firm such as mergers and acquisitions, new product lines, and patent expirations. These types of changes can be given estimated dollar values and can therefore affect the value and share price of a firm. 1 However, these definitions only hold true in a perfect markets environment with perfectly divisible financial assets, homogenous investor expectations, and no transaction costs. In reality we live in imperfect markets where trading and information imperfections may cause stock prices to react to splits. Substantial research has been done on the impact of forward splits but reverse splits have received little attention in the past. In a forward split, there is an increase in the number of a corporation's outstanding shares, which in turn decreases the value of its stock. The market capitalization remains the same because the factor to decrease the price is the same as the factor to increase shares outstanding. For example, in a 2-for-1 stock split, each stockholder receives an additional share for each share held, but the value of each share is cut in half: two shares now equal the original value of one share before the split. Therefore, the proportional ownership of shareholders remains the same. 1 Radcliffe, R.C. and Gillespie, W., (February 1979), “The Price Impact of Reverse Splits,” Financial Analysts Journal, 63-67
  • Even though shareholder wealth should technically remain the same after a split, extensive research has proven otherwise. The consensus is that a forward split announcement brings about a positive market response. 2 There have been many explanations for the positive stock market response such as the adjustment of the stock price to a more attractive trading range. 3 A second hypothesis explaining the abnormal returns involves the reduction of information asymmetries. Most versions of this argument explain that stock splits reduce information asymmetries either by directly signaling good information that previously was privately known or simply by attracting greater attention to the firm. 4 On the other hand, not much is known about the effects of revere stock splits because they simply have not been a tool frequently used by firms until the recent bear market we are in today. In a reverse split, there is a decrease in the number of a corporation's outstanding shares, which in turn increases the value of its stock. Once again, the market capitalization remains the same because the factor to increase the price is the same as the factor to decrease shares outstanding. This is another example of a paper transaction where there should be no effect on shareholder wealth. However, the little research that has been done in this field shows that there is an abnormal negative response to companies when they announce a reverse stock split. 5 The purpose of this 2 Fama, E., Fischer, M., Jensen, M., and Roll, R. (February 1969), “The adjustment of stock prices to new information,” International Economic Review, 1-21. 3 Lakonishok, J. and Lev, B. (September 1987), “Stock splits and stock dividends: Why, who, and when,” Journal of Finance, 913-932. 4 Brennan, M.J. and Copeland, T.E. (October 1988), “Stock splits, stock prices and transaction costs,” Journal of Finance and Economics, 83-101. Grinblatt, M.S., Masulis, R.W., and Titman, S. (December 1984), “The valuation effects of stock splits and stock dividends,” Journal of Financial Economics, 461-490. 5 Lamoureux, C.G. and Poon, P. (December 1987), “The market reaction to stock splits,” Journal of Finance, 1347-1370. Woolridge, J.R. and Chambers, D.R., (Autumn 1983), “Reverse splits and shareholder wealth,” Financial Management, 5-15.
  • paper is to test the market response to reverse splits with an up-to-date sample and to explain what causes the abnormal returns if any exist. MOTIVES: There are many factors that might motivate firms to reverse split their stock. If reverse splits had no impact on the value of the firm, then there would seem to be no economic use for them by management. Since reverse splits do occur, especially in recent times, corporate managers must believe otherwise. A possible motivation for a reverse split might be to reduce registrar fees and shareholder mailing costs. However, as Gillespie and Radcliffe (1979) explain, it is not probable that the minimal future benefits from reverse splitting firms would in most cases offset the immediate costs involved to undertake this action. 6 As a result, this motivation will not be given much credibility and thus not looked into further. Reverse splits may also be used to eliminate enough shareholders to remove the corporation from disclosure requirements. A reverse split provides a relatively simple means of going private without making a tender offer and, depending upon the corporate charter, without a shareholder vote. Large reverse splits can squeeze out small shareholders since, in some states, shareholders are not permitted to own fractional shares. We do not usually see this done for companies that are traded on the major exchanges which are the only ones I am looking at in this sample, so this factor of motivation will also not be analyzed further in this paper. Next, and probably one of the more compelling motivations for reverse splits, is to increase marketability. Share prices that are too low may affect marketability because 6 See footnote 1
  • they may be considered speculative, and therefore not attractive to investors, especially institutional investors. Also, many institutional stock screens and quantitative models ignore stocks that have a price below $5. Companies with such low prices do not give off a good image because investors feel that there has to be a reason they are trading at such poor prices. Gillespie and Seitz (1977) discovered image improvement to be the most common motivation for reverse splits. 7 If higher stock prices do enhance a firm’s image or provide other benefits to a firm, then stock prices should be affected positively on the announcement date. Other increases in marketability can come from a reduction in transaction costs. Since transaction costs are inversely related to share price, transaction costs (as a percentage of stock price) should decrease after a reverse split. 8 Other things remaining the same, this decrease in cost should improve of the liquidity of the stock. Also, in the U.S. a stock can not be bought on margin if it trades at less than $5 which can be a serious disadvantage for investors. By raising share prices, reverse splits can improve the margin eligibility, which, in turn enhances the liquidity of the stock. So far all the motivations for reverse splits that have been described are for discretionary purposes. However, a non-discretionary motivation which has become of much more importance in recent times, is to artificially raise the stock price through a reverse split in order to prevent the company stock from being delisted off a major exchange. The NYSE and Nasdaq require for a company to remain on its exchange to keep a stock price above $1. If a company fails to meet this requirement for more than 7 Gillespie, W. and Seitz, N., “Price Trends Following Reverse Stock Splits,” paper presented at Regional AIDS Meeting, 1977. 8 See footnote 4
  • 30 days, or any of the other requirements, it will be delisted from the exchange. 9 As a result, its shares typically begin trading either on the OTC Bulletin Board, run and loosely regulated by the Nasdaq, or the Pink Sheets, a barely regulated private quotation service where companies aren’t required to file regular financial reports. These markets are much less liquid because it is harder to match buyers with sellers and as a result, this lack of marketability can further destroy a firm which must already be in trouble because it was forced to delist off an exchange. HYPOTHESIS DEVELOPMENT: The increased threat and concern of delistment in recent times is a result of many reasons. First of all, in the 1980s and 1990s the Nasdaq and NYSE exchanges eased requirements to allow companies to enlist on their exchange because of major competition between them to get business. Then in 1997, for fear of hurting its reputation, the Nasdaq tightened its requirements to stay on its exchange. The technology boom which followed allowed most companies to temporarily comply with those requirements because of all the optimism and hype that caused incredibly high valuations on those companies. However, when the market finally collapsed in 2000 into the bear market we are in today, the tightening of those rules in 1997 really came into affect. Many companies have seen there stock prices plummet during these tough times to below the $1 threshold putting them in danger of being kicked off a major exchange. As a result, a lot of these companies are trying to do whatever it takes to survive and many are resorting to reverse stock splits. 9 Companies receive a warning after being below $1 for 30 days which gives them 90 days to bring the stock price above $1 for 10 consecutive days or else they are delisted (see appendix for list of requirements).
  • In accordance with some of the research that has been done on reverse splits, I believe that I will find that firms announcing reverse splits will show a statistically significant decline in stock price on the day of the announcement. This will show that the negative information effects will outweigh the microstructure benefits that may arise from a reverse split. More specifically, I will make the following two hypotheses: (H1): Firms which are forced to reverse split because of delisting fears will perform relatively better than firms that chose to reverse split for discretionary purposes. (H2): Companies that have negative earnings before a reverse split will perform worse than companies with positive earnings before the reverse split. In the first hypothesis, I am assuming that the companies that are forced to reverse split actually have the most to gain from the increased marketability because they will also take away the fear of being delisted and no longer be considered a “penny-stock” As a result, they should perform better than the companies that do it for discretionary purposes. In the second hypothesis, I am assuming that companies which have negative earnings before a reverse split will send a strong informational signal to investors that these poor earnings are here to stay or else they would not have had to artificially raise their prices. On the other hand, companies that have had positive earnings and then perform reverse splits might be just trying to get an immediate increase in marketability which should help the company. However, the artificial raising of the stock price might still send a negative signal but it will not be as large because they were performing well before the announcement.
  • DATA: To get a sample of companies that performed reverse splits I did a search in Factset by setting the split factor less than 0.5 for all companies from January 1998 to December 2002. However, there was a problem because I couldn’t specify what exchange the company should be on at a certain time, only what exchange it was currently on. Therefore, if I limited my search to only companies on the three major exchanges (AMEX, NYSE, and Nasdaq), I would only get a list comprised of companies that performed reverse splits and are still being traded on one of the major exchanges now, leaving out all delisted companies after reverse splits which is what I want to really look at. As a result, I did a cross search on the list I got from Factset, which contained a few thousand companies including ones currently traded on the OTC with the CRSP database which has only companies that are or have been on the NYSE, AMEX, or Nasdaq. 10 If the company in my sample did not show up on the CRSP database, I excluded it from my dataset which significantly cut down the original list of about 2,000 companies. I then eliminated any companies that were not trading 6 months before the split because I wanted to examine pre-split earnings and stock returns. I also eliminated companies on the AMEX because they delist companies on a case by case basis which would hamper my analysis between discretionary and non-discretionary reverse splits. Finally, any companies for which I could not get pertinent accounting data from Compustat to conduct my analysis were also eliminated, and in the end, I was left with 211 companies in my dataset. 10 I could not search directly for companies that performed reverse splits on CRSP because of limitations through the WRDS account for NYU students.
  • Below is a table which divides up the data by S&P sectors and by years so we can see if there is any trend in the annual number of reverse stock splits and if there are any sectors worth noting. Table 1: Sector and Year distribution of Reverse Stock Splits 1998 1999 2000 2001 2002 Total Consumer Discretionary 7 10 5 6 9 37 16.28% 28.57% 20.00% 12.50% 15.00% 17.54% Consumer Staples 0 4 1 2 1 8 0.00% 11.43% 4.00% 4.17% 1.67% 3.79% Energy 8 2 2 0 1 13 18.60% 5.71% 8.00% 0.00% 1.67% 6.16% Financials 2 2 4 4 2 14 4.65% 5.71% 16.00% 8.33% 3.33% 6.64% Health Care 9 6 4 4 7 30 20.93% 17.14% 16.00% 8.33% 11.67% 14.22% Industrials 7 3 2 6 2 20 16.28% 8.57% 8.00% 12.50% 3.33% 9.48% Information Technology 6 7 4 23 27 67 13.95% 20.00% 16.00% 47.92% 45.00% 31.75% Materials 4 1 2 2 4 13 9.30% 2.86% 8.00% 4.17% 6.67% 6.16% Telecommunication Services 0 0 1 1 7 9 0.00% 0.00% 4.00% 2.08% 11.67% 4.27% Utilities 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Total 43 35 25 48 60 211 20.38% 16.59% 11.85% 22.75% 28.44% 100.00%
  • With the market in full gear during the late 1990s, the exchanges were battling to get companies to enlist with them and many companies that should not have become public did just that. Being the height of the technology boom and more specifically the dot com bubble, many of these companies that should not have been public were technology companies. As the bubble busted in 2000, many of these technology companies were sent to the exits as fast as they had come in. As a result, you see less reverse splits when the market was booming in 1999, and 2000 (there are relatively more reverse splits in 1998 because the Nasdaq tightened their delisting policies at the time and companies which weren’t able to catch the boom in time were booted as a result), and gradually more splits in the latter years with 2002 having the most. Also, the concentration of technology companies performing reverse splits has increased greatly which makes timely sense as a result of the collapse of the technology sector. Descriptive statistics on the sample are displayed in table 2. The average split factor for a reverse split is 11.43, but that figure is skewed upward because of the few companies which took on extremely large splits such as 200 to 1 which is the maximum split in the sample. The median split factor is 5.0 which gives a better idea of what size of reverse split most companies perform. The pre-split average price of the sample is rather low at $1.43 exemplifying the point that low priced companies tend to do reverse splits. The post split average price of $7.05 is interestingly above the $5 threshold which most institutional investors do no look below, however the median is only $3.15 suggesting that most stocks do not try to reverse split for the purpose of getting over $5. In terms of size, companies performing reverse splits indicated by their market cap are only on average $68.3 million with a median of $12.9 million. This goes along with the historical trend of smaller companies usually performing reverse splits but some larger
  • companies are taking on this strategy as well as can be seen by the maximum size of a company in the sample being over $10 billion. Finally, as expected, there are a lot more reverse splits done on the Nasdaq because of the amount of technology companies on that exchange. Table 2: Descriptive Statistics on Sample N Mean Median SE mean Min Max Sample Size 211 Split Factor 11.43 5 1.65 2 200 Pre-Split Price $1.43 $0.54 0.21 $0.01 $26.19 Post-Split Price $7.05 $3.15 0.82 $0.70 $99.58 Market Cap (millions) $68.30 $12.90 $49.50 $0.01 $10,405.60 NYSE 176 Nasdaq 35 ANALYSIS: To analyze the impact of reverse splits on stock price, I looked at the –20 to +20 day period around the announcement date to see the impact of the news, with day zero being the announcement date. Also, to look at a more long term perspective, I looked at the –5 month to + 6 monthly returns around the split with the returns from month zero to month one being the month of the announcement. As the dataset used in this study is a very recent one, it will not be possible in this paper to analyze any longer term effects for these companies. However, Desai and Jain did a study in 1997 and looked at a more dated set of reverse splits (ranging from 1976-91) and analyzed the long run effects on them. They found that companies which do reverse splits have an abnormal return in their announcement period which is followed by even worse abnormal returns in the 3 years post split. So not only does the news of reverse split contain negative news
  • reducing shareholder wealth, the period after the split is followed by deteriorating shareholder wealth. 11 When looking at stock returns around the announcement date, it is important to try and focus on firm specific returns or abnormal returns which exceed that of the market. To get more specific and take into account any sector effects, I adjusted the individual returns with that of its sector S&P index for the same time periods. As a result the adjusted return was: Radj = Rcomp - Rind This will give us the abnormal stock returns of the individual companies which will be analyzed around reverse split announcement dates for each company. The returns should be randomly distributed around zero. Any statistically significant departures from this pattern would imply that there is something special about the sample stocks. (Please Turn Over) 11 Desai, H., Jain, P.C., (July 1997), “Long-Run Common Stock Returns Following Stock Splits and Reverse Splits,” The Journal of Business, 409-433
  • Table 3: Abnormal Daily Returns Around Reverse Stock Split Day Abnormal Return Standard Error t-statistic -20 0.914 0.839 1.09 -19 0.344 0.828 0.42 -18 -0.302 0.748 -0.4 -17 -0.152 0.754 -0.2 -16 0.409 0.915 0.45 -15 -0.148 0.731 -0.2 -14 0.54 0.688 0.78 -13 -0.259 0.61 -0.42 -12 0.151 0.624 0.24 -11 -0.674 0.568 -1.19 -10 1.244 0.805 1.54 -9 -0.231 0.628 -0.37 -8 0.076 0.665 0.11 -7 -0.315 0.664 -0.47 -6 -0.243 0.775 -0.31 -5 1.35 1.36 1.72 -4 -0.657 0.607 -1.08 -3 -0.168 0.67 -0.25 -2 -0.645 0.613 -1.05 -1 -0.322 0.787 -0.41 0 -8.535 0.992 -8.61** 1 -0.342 0.944 -0.36 2 -0.557 0.866 -0.64 3 0.815 0.812 1 4 0.061 0.823 0.07 5 1.784 0.844 2.11* 6 -0.378 0.734 -0.51 7 -0.373 0.678 -0.55 8 -0.83 0.687 -1.21 9 -0.321 0.658 -0.49 10 0.516 0.634 0.81 11 0.249 0.688 0.36 12 0.249 0.669 0.37 13 -0.023 0.732 -0.03 14 0.145 0.67 0.22 15 0.879 0.881 1 16 -0.248 0.646 -0.38 17 -0.23 0.535 -0.43 18 -0.722 0.615 -1.17 19 -0.677 0.493 -1.37 20 -0.256 0.595 -0.43 * statistically significant at the 5-percent level ** statistically significant at the 1-percent level
  • Table 4: Abnormal Monthly Returns Around Reverse Stock Split Month Abnormal Return Standard Error t-statistic -5 1.89 3.87 0.49 -4 -2.44 2.69 -0.91 -3 -4.34 2.86 -1.52 -2 -3.1 2.14 -1.45 -1 -1.43 5.23 -0.27 0 -4.95 2.26 -2.63** 1 -11.51 2.81 -4.09** 2 -3.08 2.93 -1.05 3 2.74 2.59 1.06 4 -2.45 2.48 -0.99 5 3.22 2.08 1.52 6 -4.62 2.25 -2.05* * statistically significant at the 5-percent level ** statistically significant at the 1-percent level The average abnormal return on the announcement date is –8.54%, significant at the 1-percent level, indicating that negative abnormal returns do occur at the announcement of reverse splits. Looking at the monthly returns to see a longer term effect, we find the month of the announcement to have abnormally negative and statistically significant returns with a –11.51% return at the 1-percent level. 12 Interestingly, the month prior to the announcement is significantly negative as well, indicating that companies that perform reverse splits were performing a lot worse than the market before they made their announcement. Even though I am not looking as far long term as Desai and Prem did, my results do not indicate statistically negative returns following the reverse split other than in month six. It seems as though all the information is incorporated at the time of the reverse split and then the stock goes back to it normal return pattern. It is important to note that I am only looking at a 6 month outlook and that my sample size is reduced significantly because I had to eliminate companies from 12 Companies that did reverse splits from October 2002-December 2002 in this sample had to be left out because there +6 month information was not available at the time the data was collected in March 2002.
  • October through December 2002. The reduction in sample size for this longer term analysis may reduce the accuracy and therefore statistical significance of abnormal returns which may in fact exist. EMPIRICAL TESTS AND RESULTS: We analyze the significant abnormal negative return on the announcement date using a multiple regression with the return on day zero being the dependent variable and factors that could represent trading range and information effects as the independent variables. RET = B0Nondiscretionary + B1NegEPS + B2SplitFactor + B3Size + B4NYSE + B5Nas B B B B B B For the non-discretionary variable, I used dummy variables with a 0 for discretionary and 1 for non-discretionary. I also used a dummy variable for earnings per share with a 0 for positive earnings two quarters before split announcement and a 1 for negative earnings per share. For split factor I used the fractional value of the reverse split and for size I used the market capitalization of the company. Finally, for the NYSE and Nasdaq variables I used dummy variables with a 1 if the stock was on the exchange. Before going into the results of this regression, I will individually analyze three key factors which I feel are the most important to look at in explaining the negative returns: Marketability, Discretionary vs. Non-Discretionary, and Negative Earnings Information. Marketability:
  • As mentioned earlier, one of the main motives for companies to undergo reverse stock splits is to increase the marketability of their stock. To determine if marketability does in fact go up after a reverse split I will look at trading volume and price distributions before and after the split. For analyzing trading volume, I looked at split adjusted trading volume from 30 days prior to 30 days after the reverse split. 13 As you can see in the graph below, there was a spike in trading volume around announcement time and then the volume dropped a little before leveling off at a range higher than before the split. The average trading volume for the 30 days prior to the split was 62.9 thousand shares and 95.7 thousand shares after (t-statistic of difference –5.88) indicating that the reverse split did increase the marketability because more shares were being traded implying higher liquidity. Trading Volume Around Reverse Stock Split 200.00 Trading Volume (thousands) 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 -30-28-26-24-22-20-18-16-14-12-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Days Around Split 13 A split-adjusted trading volume lets us compare the volume before and after the split. To illustrate, consider a firm that reverse split its shares by the split ratio of 0.25. Suppose that 1000 shares and 400 shares were trading during the pre and post split periods, respectively. In this case, the volume measures are 1000 shares and 1600 shares (300/0.25) for the pre and post split periods, respectively.
  • The table below shows the pre and post split price ranges for the sample. Before viewing the results, I expected that most companies would be trying to get above the $5 threshold to substantially increase their marketability. However, the results do not go in line with those expectations but rather raise some interesting points. Most companies that performed reverse splits were actually below $1 indicating that they were forced to reverse split to get out of the threat of delistment. This can be confirmed by the post-split price range in which most companies are above the $1 cutoff but surprisingly still under $5. This further shows that these companies weren’t necessarily motivated by the need to increase marketability but were rather forced to save themselves from being delisted. Number Of Firms P<=$1 $1<P>$5 $5<=P>=$10 P>=$10 Pre-Split 162 42 5 5 Post-Split 3 155 40 34 From the analysis on marketability, trading volume and price go up which shows that marketability does in fact go up but the question arises if that is the main motivational force. Discretionary vs. Non-Discretionary: To test my first hypothesis, I will classify reverse splits by companies that had a pre-split price below or equal to $1 as non-discretionary because they faced the threat of delistment and were forced to do so to stay on an exchange. On the other hand, companies above $1 were considered to have performed reverse splits for discretionary purposes. There may be a lot of companies close to $1 but slightly above it that did
  • reverse splits because of the threat of delistment but just to keep an accurate sample and not to cut down the size of the discretionary sample too much, I kept a $1 cutoff. Running a two-sample t-test to compare the announcement date results of the two subsets, I surprisingly found a much greater statistically significant negative impact for non-discretionary splits than discretionary ones. Table 5: Discretionary vs. Non-Discretionary Two-Sample T-Test and CI N Mean (%) StDev SE Mean Non-Discretionary 162 -10.5 15.2 1.2 Discretionary 49 -2.11 8.71 1.2 Estimate for difference: -8.37 95% CI for difference: (-11.78, -4.95) T-Test of difference = 0 (vs not =): T-Value = -4.85 P-Value = 0.000 What is interesting to note is that even though something causes companies that are forced to reverse split to perform a lot worse than ones that do it for discretionary purposes, companies that do it for discretionary purposes still have a statistically significant negative return on announcement date. This means that there is also something else that drives stocks to have abnormal returns on an announcement of a reverse split. To further try to understand why non-discretionary companies perform a lot worse, I will look at the driving force which causes companies to be forced to reverse split – delistment. Out of my sample of 211 firms, 107 or 50.71% have already been delisted by December 31, 2002. The average number of days before delisting after a reverse split
  • was 568 days which means all the companies that have done reverse splits after June 11, 2001 have not even reached the average duration before delisting. This means that this percentage is negatively skewed and that the actual number will probably be a lot higher. Now, separating between non-discretionary and discretionary companies, I find that 58.64% of non-discretionary have delisted by December 31, 2002 while only 24.48% of discretionary companies. If we look at just companies that have done a reverse split before June 11, 2001, we find an astonishing 78.72% of non-discretionary companies have already delisted by December 31, 2002. This is telling us that companies reverse split to artificially raise their price to prevent being delisted but their stock price still drops to below $1 and they are forced to delist. Since their stock prices go down even after the negative announcement date return, these stock are obviously either overvalued or distressed. Management must know that the stock price is going to go down as a result and they try to artificially raise the stock price with enough of a cushion so that it does not drop below the $1 mark. However, as the results show, they have not been very successful in keeping there companies on the major exchanges. These companies do not belong there and are doing whatever they can out of desperation to stay on. Investors are reading this when they see a non-discretionary reverse split and that is why they assign such high negative returns to it on the announcement date. Negative Earnings Information: To test the second hypothesis, I will look at the earnings two quarters before the reverse split for the firms in the sample. The reason I am not looking at earnings one quarter before is because the results of those quarters were possibly not reported by the time the reverse split was announced. As you can see in the table before, companies
  • which had negative earnings before a reverse split performed a lot worse than companies with positive earnings. It is worth noting that the companies with positive earnings still had a statistically significant negative return on announcement date. Table 6: Positive vs. Negative EPS Two-Sample T-Test and CI: Pos -2Q, Neg -2Q N Mean (%) StDev SE Mean Pos -2Q 58 -5.2 12.7 1.7 Neg -2Q 153 -12.1 20.5 1.7 Estimate for difference: 6.90 95% CI for difference: (2.25, 11.55) T-Test of difference = 0 (vs not =): T-Value = 2.93 P-Value = 0.004 The firms which already had negative earnings must be sending a stronger negative signal to the market that their poor results will continue or else they would not have tried to artificially raise their prices. The argument can also be made that positive earning companies might be signaling a bad future because they also artificially raised their prices but the signal is not as strong because they were not already reporting negative returns. Controlling for Negative Factors: Now we have seen that non-discretionary and negative earnings cause a substantial negative impact on the announcement date but their counterparts, discretionary and positive earning companies each also have a much smaller, yet still significant negative return on that day. I thought it would be interesting to see what the results would be if we took out both negative factors and tested the returns on positive
  • earning companies that performed reverse splits. This screening of the data reduced the sample to only 23 companies which hurt the accuracy and significance of the test. However, interestingly enough, as you can see below the average return for these companies on the announcement date was a positive 1.72% on a slightly less, but still decently significant 90% confidence level. This means that two major factors that cause negative returns have possibly been identified and that if they are removed, we can actually see the positive impact of increased marketability of companies that announce reverse splits. Table 7: Pos EPS and Dis One-Sample T: PosEPS and Dis Test of mu = 0 vs mu not = 0 Variable N Mean StDev SE PosEPS and Dis 23 1.722 3.964 0.934 95.0% CI PosEPS and D ( -0.249, 3.693) T=1.84 P=0.083 MULTIPLE REGRESSION RESULTS: When we run the multiple regression mentioned above to see what causes the negative returns on the announcement day, we find that the only statistically significant factor is if the company does it for non-discretionary purposes. I got a highly negative coefficient significant at the 95% confidence level indicating that a non-discretionary split accounts for the major part of the negative returns. All the other factors were drowned out by this one except for the negative earnings factor which showed some statistical significance at the 90% confidence level. For the negative earnings variable,
  • there was a negative coefficient indicating that negative earnings prior to announcement are responsible for some of the negative effect. The R-squared for the regression was rather low at 7.0% but that is normal in finance because of the correlations between all the individual factors that you bring into the equation. Table 8: Multiple Regression Regression Analysis: RET versus nondis, neg eps, factor, size, nyse, nas * nas is highly correlated with other X variables * nas has been removed from the equation The regression equation is RET = - 0.82 - 6.21 nondis - 4.29 neg eps - 0.0406 factor + 0.00049 size + 3.59 nyse Predictor Coef SE Coef T P Constant -0.818 2.461 -0.33 0.74 nondis -6.211 2.423 -2.56 0.011 neg eps -4.295 2.214 -1.94 0.054 factor -0.04064 0.04076 -1 0.32 size 0.000493 0.001355 0.36 0.716 nyse 3.594 2.623 1.37 0.172 S = 13.89 R-Sq = 9.2% R-Sq(adj) = 7.0% CONCLUSION: After running tests on my sample, I find that my first hypothesis was wrong because I had thought non-discretionary companies would gain the most from an increase in marketability and reduction of threat from delistment after a reverse split. As it turns out, these companies performed the worse because, as shown by my results, they are either overvalued or distressed when they reverse split and management is just trying to save the company out of desperation. Investors are smart and read the announcement of
  • a reverse split from these types of companies as a signal that they are highly overvalued or are distressed and will underperform the market in the future. On the other hand, my second hypothesis seemed to show some truth as companies with prior negative earnings sent a stronger negative signal to the market than ones with positive earnings before a reverse split. Investors read that as a sign indicating that poor earnings were going to continue and that the firm was probably distressed. As a result, they assigned it a significantly high negative return on the announcement date. Finally, I saw that the increase in marketability which we proved does occur was drowned out by these two negative factors and if we remove the negative factors, we can actually see the benefits of it with a slightly significant positive return on the announcement day. These findings also bring up an interesting thought on trading strategy. I looked at a sample of 100 companies that hit $1 on the Nasdaq and found that 30% of them performed reverse stock splits within 6 months of hitting $1. If you were to short every stock that hits $1 on the Nasdaq, you could make an expected return of (.30) * (10.5) = 3.15%. The 10.5% is the average negative return on the announcement date for non- discretionary companies which is essentially what we are looking at when we look for stocks that hit $1. You could also possibly short all non-discretionary companies once they reverse split because my research has show that most of them tend to delist meaning their stock price drops after the reverse split. It is also important to note that my analysis is a very time-specific one. We have seen a tremendous boom followed by a bust which has caused many companies to be forced to reverse split to survive. Many of them probably would not even have been on a major exchange if it wasn’t for the ridiculous overvaluations in the late 90s and therefore
  • this data sample is very skewed to those types of companies. Also, if the Nasdaq decides to ease their delisting requirements which they are considering, it could drastically effect the negative returns that I got on the announcement day since much of it was due to the information from companies being forced to reverse split.
  • Appendix 1: NYSE Suspension and Delisting Guidelines
  • Appendix 2: Nasdaq Suspension and Delisting Guidelines
  • Figure 1: Adjusted Daily Returns Around Reverse Stock Split 4 3.5 3 2.5 2 1.5 1 0.5 0 Stock Return (%) -0.5 -1 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 -1.5 -2 -2.5 -3 -3.5 -4 -4.5 -5 -5.5 -6 -6.5 -7 -7.5 -8 -8.5 -9 Daily Return Around Reverse Split Date