September 24, 2012
An Examination of the Survivability of Reverse Stock Splits: If they lose
value, then why do Companies continue to perform Reverse Splits?
ABSTRACT
The recent financial crisis precipitated a number of reverse stock splits by distressed companies
such as American International Group (AIG) and Citigroup. Generally, reverse splits are tools
for company recapitalizations that attempt to resurrect poorly performing common stock,
maintain exchange listings, or enhance liquidity. Many companies fail shortly after a reverse
split, but others survive. Adapting the Hensler, Rutherford, and Springer (1997) IPO proportional
survival model to 1,215 reverse splits during the 1995 to 2011 period, we are the first study to
examine the survivability of reverse split companies. We observe that survivability varies for
reverse split companies based on firm size, pre-split operating performance, market volatility,
and pre-split stock price performance. We also find a relation between ex-date returns and
survivability.
JEL classification: G32, G34
Keywords: Stock splits, reverse stock splits, survival, survivability
An Examination of the Survivability of Reverse Stock Splits: If they lose value,
then why do Companies continue to perform Reverse Splits?
1. Introduction
The recent financial crisis precipitated a number of reverse stock splits for distressed companies such
as American International Group (AIG) and Citigroup. Generally, reverse stock splits are tools for
company recapitalization that increase the stock price in an attempt to resurrect poorly performing
common stock, maintain exchange listings, or enhance liquidity. Many companies fail shortly after a
reverse split, but others survive. This raises two questions: How long do reverse split companies
remain viable? What factors are associated with survival? Adapting the Hensler, Rutherford, and
Springer (1997) accelerated failure time model to 1,237 reverse splits during the 1995 to 2011 period,
we are, to the best of our knowledge, the first study to examine the survivability of reverse split
companies.
A reverse stock split, some times called a share consolidation, is an exchange of old shares for a
lesser number of new shares. Prices of reverse split stocks increase proportionally. Factors affecting
the survival of reverse split companies include firm size, operating performance, market volatility,
and stock price performance. As documented in previous studies (e.g., Martell and Webb), we find
that reverse-splits are generally undertaken by small, Nasdaq-listed, low-priced stocks. In addition,
we find that these reverse-splitting firms are often unprofitable and that many of them are technology
companies.
2. Background
Peterson and Peterson (1992) report that there are several motives behind the decision to undertake a
reverse stock split. These include increasing marketability by listing (or continuing to list) on an
1
exchange or the NASDAQ, allowing companies to maintain prices of $2 to be eligible under Fed
Regulation T margin requirements; and significantly reducing the number of shareholders to reduce
servicing costs or enable the company to go private.
Woolridge and Chambers (1983) find that due to information effects stock prices of reverse split
firms decline significantly on the proposal, approval, and effective dates; that trading considerations
may cause declines on effective dates; that reverse splits are unanticipated by the market and are not
preceded by adverse stock price movement within six weeks; that stock prices continue to decline for
a brief period after the effective date; that relative earnings performance influences shareholder
returns on the proposal, approval, and effective dates, with better (poorer) performing firms obtaining
smaller (larger) absolute negative returns on all three dates; if the proposal or approval news is not
published in the financial press or company SEC filings, this information is not quickly included in
the security price.
Lamoureux and Poon (1987) find that reverse split announcement negative abnormal returns result in
increased volatility and increased liquidity. Also, Han (1995) shows enhanced liquidity with
decreases in bid-ask spreads and increases in trading volumes after reverse splits.
Peterson and Peterson (1992) report that discretionary reverse splits include those used to reduce
shareholder servicing costs, increase earnings per share, improve marketability (allow for margin
trading or voluntarily increase share price to list on an exchange), or to recapitalize (except for
Chapter 11. Given that non-discretionary reverse splits are coerced, these reverse splits are
implemented to satisfy a price per share listing requirement or Chapter 11 creditors. They find that
discretionary reverse splits incur negative announcement effects, but that non-discretionary reverse
2
splits generate positive announcement returns. Also, they opine that returns vary for decisions by
informed parties such as management returns and for those by uninformed parties such as exchanges.
Desai and Jain (1997) examine initial and long-term returns for forward and reverse stock splits for
the period from 1976 to1991. While they find positive announcement period returns around forward
stock splits, they observe negative announcement period returns for reverse stock splits. Also, they
report one-year and three-year post-split announcement returns of 7.05% and 11.87%, respectively,
for forward splits and -10.76% and -33.90%, respectively, for reverse splits.In a similar vein, Kim,
Klein, and Rosenfeld (2008) examine long-term returns of 1,612 reverse stock splits over a 40-year
period from 1962 to 2001 and find statistically significant negative excess returns and poor operating
performance over the three-year period following the ex-split month.
Given the generally low survival rates and short survival times of firms following reverse stock splits,
we adapt the Cox proportional hazards model. Lane, Looney, and Wansley (1986); Hensler,
Rutherford, and Springer (1997); and Adjei, Cyree, and Walker (2008) use this model for bank
failures, for IPO survival, and for survival of reverse mergers and IPOs, respectively. They note that
hazard methodology is appropriate to determine the length of time a company remains viable before
being delisted or dropped by exchanges for negative reasons such as bankruptcy of failure to meet
listing requirements.
3. Sample selection and data
3.1. Sample selection procedure
Using Center for Research in Security Prices (CRSP) distribution code 5523 with negative share
factors, we identify our initial sample of 1394 reverse splits from 1995 to 2011. We eliminate 11
closed end funds with SIC 6726 and 44 REITs or master limited partnerships (SIC three-digit code
3
679). Following Peterson and Peterson (1992) we eliminate 79 companies with confounding events
such as concurrent IPOs, concurrent forward splits, going private transactions, or the second of
duplicate share classes. Also, we follow Kim, Klein, and Rosenfeld (2008) and delete 42 companies
with split factors less than 1:2. Such companies have negligible stock price reactions (Byun and
Rozeff; 2003). This reduces the sample to 1,215 observations.1
Each observation must have a news article or SEC filing to validate the reverse split announcement
and completion. We cross check all events with Lexis-Nexis, the Investment Dealer’s Digest,
Mergent Industrial Manual (formerly Moody’s) and the Dow Jones News Service. We obtain
operating data including book values of equity and debt from Research Insight (Compustat) and
Mergent. SEC filings and news articles provide additional information for the sample. We obtain
announcements or filings for 1215 companies, our final sample.
We obtain stock returns, stock price, and market capitalization data from CRSP. Similar to Kim,
Klein, and Rosenfeld (2008), we compare market-adjusted returns to CRSP equal-weighted index
results (including distributions) during the sample period. This avoids the small company bias noted
by Barber and Lyon (1997) and Loughran and Ritter (2000).
3.2 Variables
We examine several variables from the reverse stock split and survivability literature. We report only
the variables that make a significant contribution to our reverse split survivability logit model.2 To
ensure that our results are not heavily influenced by outliers, we winsorize the survival period, the
1
After a search of SEC filings, Dow Jones News Service, Lexis Nexis, Newspaper data bases, S&P and Mergent Dividend
Records we could not confirm a reverse split for two companies.
2
In addition to the variables reported we examined…..
4
trading period, total assets, and return on assets by setting the values of each variable below the first
percentile and above the ninety-ninth percentile equal to the value at the first and ninety-ninth
percentile, respectively.
3.2.1 Log of total assets
Following Hensler, Rutherford and Springer (1997), we use the log of total assets for the year before
the reverse split as a proxy for size. Consistent with Schultz (1993) who finds that the probability of a
company delisting is inversely related to offering size, we anticipate that survivability varies with
company size
3.2.2 Trading period
We calculate the trading period as the number of weeks between the IPO date and the reverse split
announcement date. Similar to the Hensler, Rutherford, and Springer (1997) study for IPOs, we
expect that the longer the trading period for reverse stock splits, the greater the chance of
survivability.3
3.2.3. Recent operating performance
We measure operating performance as the return on assets (ROA) for the year before the reverse
stock split. Consistent with Kim, Klein, and Rosenfeld (2008) who observe that reverse split firms
with poor operating performance have weak stock performance, we expect that survivability varies
with ROA.
3
For the four companies that conduct reverse splits before the IPO date, we report a trading period of zero.
5
3.2.4. Market volatility
Market forces such as volatility tend to influence security prices. We use the CBOE Volatility
Index® (VIX®) which measures market expectations of near-term volatility using S&P 500 stock
index option prices. The value of the VIX® on the ex-date, which reflects the annualized expected
movement in the market over the next 30 days, is used as a proxy for market volatility. We anticipate
that survivability will vary inversely with market volatility.
3.2.5. Run-up
Consistent with Peterson and Peterson (1992) who find that run up is inversely related with returns,
we use annualized company weekly returns for the six months before the reverse split to indicate runup. We expect that survivability varies with run up.
3.2.6 Discretionary versus non-discretionary motive
Kim, Klein, and Rosenfeld (2008) observe that only reverse splits with an ex-split price of $5 or less
have significant negative abnormal returns. Thus, we segregate our model into two segments: greater
than $5 ex-split price and $5 or less ex-split price to examine the price impact on survivability. Firms
are required to maintain a minimum stock price of at least $1 on the NYSE and Nasdaq, firms that
have pre-split prices less than $1 are essentially forced to undertake the reverse split in order to
maintain their listing. Firms that have post-split prices greater than $5 reflect a desire for investment
by corporate investors and increased liquidity.
3.3. Logit model
We adapt the Adjei, Cyree, and Walker (2008) logit model to test whether a reverse split will fail in
bankruptcy or delisting for negative reasons or will survive as a continuing company or be acquired
6
by another company. We define our logit model as:
Yi = a0 + a1lnTA + a2TPi + a3 ROA(t-1)i + a4VIX + a5Runup + ei
(1)
where Yi =1 if the reverse split survives (CRSP delisting code 100) and 0 if it ceases to exist (all other
CRSP delisting codes); lnTA is the natural logarithm of total assets of firm i, TP is the trading period
in months from the IPO to the reverse split announcement), ROA is the return on assets of firm i for
the fiscal year prior to the reverse split announcement), VIX is the S&P options volatility index, and
Run-up is the return from the IPO to the announcement of the reverse stock split. Also we apply
equation (1) to delisting reverse splits where Yi = 1 if the firm ceases to exist for positive reasons
(CRSP delisting codes from 200 to 399) or goes private (code 573) and 0 if the firm ceases to exist
for negative reasons such as bankruptcy or failure to meet exchange listing standards (CRSP delisting
codes of 500-572 and 574-599).4
3.4. Survival model
We apply the Cox (1972) hazard technique to estimate the survivability of reverse stock splits. Cox’s
model assumes that for any two firms, the ratio of their likelihood of failure is constant over time.
The hazard function is used to determine the probability that a firm will experience an event within a
particular time period.
We use the length of time from reverse split to delisting as a proxy for survival time. Censored firms
4
We exclude CRSP delisting codes 231, 241, and 331 from the acquired set if the company acquires another firm or
restructures its shares and continues with another PERMNO. We then use the delisting code for the company’s final
PERMNO.
7
survive for an unknown period beyond the sample period. The hazard probability is the conditional
probability that a reverse split announced at t=0 is delisted at time t given that it has not been delisted
before time t. The hazard probability is shown by:
𝑓(𝑡;𝑿)
𝐻(𝑡; 𝑿) = (1−𝐹(𝑡;𝑿))
(2)
.
where t is the number of months the reverse split firm has been listed, F(t) is the probability that a
reverse split firm has been delisted prior to time t and f(t) is the probability density function on t.
Hensler, Rutherford, and Springer (1997) show that since the probability of delisting is duration
dependent, the duration data are right-censored, that the log-logistic model best approximates the
distribution of the duration data. Thus, our log-logistic baseline hazard model is
H0 (t) =tt
3)
where = eXB and  =  and t is the failure time. X is a vector of independent variables known to
affect the survival period and B is a vector of model parameters. We use the variables and parameters
from the results of equation 1 as the vector model (eXB). The standard deviation of the right censored
duration t is . The log-logistic function decreases monotonically if <but increases monotonically
if >1. According to Hensler, Rutherford, and Springer (1997), the most likely failure time occurs at:
𝑡=
1
(𝜌−1) ⁄𝜌
𝜆
(4)
Because some observations continue trading at the end of the sample period, the failure of these
observations is unobserved. Those firms that continue to trade through the end of the sample period
are censored. Failure time models can be estimated by including or excluding the censored
observations; however, the estimates derived when censored data are included are generally more
8
reliable. Therefore, we include an additional dummy variable, δ, in the model to denote those
observations that are censored. Under these conditions, the general form of the likelihood function is:
1−𝛿𝑖
𝐿 = 𝑐 ∏𝑛𝑖=1 𝑓𝑖 (𝑡𝑖 ; 𝑿𝑖 )𝛿𝑖 (1 − 𝐹𝑖 (𝑡𝑖 ; 𝑿𝑖 ))
(5)
where 𝑓𝑖 (𝑡𝑖 ; 𝑿𝒊 ) and 1 − 𝐹𝑖 (𝑡𝑖 ; 𝑿𝑖 ) are as defined in equation (2) and c is a constant.
3.5. Returns
We calculate announcement period returns as the cumulative abnormal returns for the three day
period centered on the announcement date.5 Woolridge and Chambers (1983) and Peterson and
Peterson (1992) observe negative abnormal reverse split announcement returns. Thus, we expect
survivability to vary with announcement returns.
Ex-date returns are holding period abnormal returns from the day prior closing to the reverse split exdate close. Han (1995) and Kim, Klein, and Rosenfeld (2008) observe negative abnormal ex-date and
long-term returns. We anticipate that survivability varies with ex-date returns.
3.6. Robustness checks
We conduct several robustness checks. Next we examine survivability by share factor: 1:2, 1:3, 1:4,
1:5, and 1:10. Thereafter, we contrast results for reverse split survivors and failures, and multiple
versus single reverse splits. We anticipate that companies with multiple reverse splits will survive
longer than those with single reverse splits. Similar to Ritter (1991) and Hensler, Rutherford, and
Springer (1997) who find increased survivability for IPOs in the drug, airline, or financial industries,
we examine survivability by industry (two digit SIC code)6 . We expect that reverse stock split
5
For those 177 companies with announcements reported on or after the reverse split date, we use the reverse split date
as the announcement date.
6
CITE observes that Research Insight SIC codes are more accurate than CRSP SIC.
9
survivability varies with IPO survivability. As an additional check we apply a dummy variable, AN,
where 1 = the reverse split is announced after or on the ex-date and 0 otherwise.
4. Results
4.1 Descriptive statistics
Panel A of Table 1 reports descriptive statistics for the final sample of 1,215 reverse split firms. For
our sample, only 28.9% of firms survive through 2011, the end of the sample period. The mean
(median) survival period is 44.9 (25.9) months. Therefore, half these firms survive for approximately
two years or less following the reverse split. The total assets variable is extremely right-skewed with
a mean (median) of $1.9 billion ($38.4 million). The largest firm in our sample, AIG Inc., has total
assets of $860.4 billion at the time of its reverse split. Reverse split stocks exhibit a mean (median)
trading period (months listed prior to the split) of 111.7 (88.0) months with a range from about two
weeks (0.5 month) to about 84 years (1,012.9 months). The return on assets (ROA) variable is leftskewed with a mean (median) value of -64.0% (-22.4%). Clearly, reverse splitting firms are generally
not profitable. In addition, some extreme values are observed with a minimum ROA of -7508.7% and
maximum ROA of 168.5%. The CBOE volatility index (VIX) has a mean (median) value of 22.6%
(21.5%) and ranges from 9.9% to 80.9%. The pre-split stock price run-up has a mean (median)
of -2.1% (-1.8%) with a range of -47.8% to 9.4%. Consistent with previous studies, the mean
(median) unadjusted announcement date returns are -2.9% (-3.0%) while the mean (median)
unadjusted ex-date returns are -6.6% (-5.5%).
Panel B of Table 1 shows the distribution of split factors for the sample of reverse splits. The most
common choices are the 1 for 10 split chosen by 244 firms or 20% of the sample and the 1 for 5 split
chosen by 231 firms or 19% of the sample. Split factors of 1:4 and 1:3 are also quite common with
10
175 and 138 sample firms or 14% and 11% of the sample, respectively, choosing these split factors.
Taken together, these four reverse split factors comprise almost two-thirds of the sample.
Panel C of Table 1 reports the distribution of sample firms by industry. Following Loughran and
Ritter (2004), we include 325 firms, or about 27% of the sample, in the Technology industry.7 After
creating the Technology classification, we sort the remaining firms into industry groups based on
2-digit SIC codes. The second largest grouping is Chemicals & Allied Products Manufacturers with
129 firms, or over 10% of the sample in this industry.
Since the focus of this study is on survivability of reverse-splitting firms, Table 2 compares the
surviving firms (Panels A and C) with the non-surviving firms (Panels B and D) in terms of the
variables of interest. The surviving firms have a lower mean (median) survival period than the nonsurviving firms. Given that the surviving firms are censored, i.e., the figures shown represent
minimum survival times for these firms, it is hard to draw strong conclusions from the comparison of
survival periods. However, comparison across the other variables may allow us to identify the
characteristics that distinguish surviving firms from non-surviving firms. Specifically, surviving firms
appear to be much larger, somewhat older, and more profitable (i.e., less unprofitable) than nonsurviving firms. In addition, non-surviving firms exhibit lower returns in the six months prior to the
split announcement. As documented in previous studies, announcement date and ex-date mean (and
median) returns are negative for both surviving and non-surviving reverse split firms. Lastly, while
announcement date returns appear to be similar for surviving and non-surviving firms, the ex-date
7
The four-digit SIC codes 3571, 3572, 3575, 3577, 3578, 3661, 3663, 3669, 3671, 3672, 3674, 3675, 3677, 3678, 3679,
3812, 3823, 3825, 3826, 3827, 3829, 3841, 3845, 4812, 4813, 4899, 7371, 7372, 7373, 7374, 7375, 7378, and 7379 are
designated as Technology firms.
11
returns are much more negative for non-surviving firms.
Table 3 reports the results of our logit model. The dependent variable is a dummy variable that equals
1 if the firm still trades at the end of the sample period and 0 otherwise. Model 1 includes each of the
previously discussed explanatory variables. Model 2 includes the same explanatory variables plus 17
industry dummy variables. The estimated coefficient on firm size as measured by the natural
logarithm of total assets is significantly positive, suggesting that large firms are more likely to survive
following a reverse stock split than small firms. We find no significant relation between the survival
and profitability as measured by ROA. Perhaps these firms are all doing so poorly in terms of
profitability that the degree of losses has little effect on survival. Somewhat surprisingly, market
volatility is positively related to survival. Therefore, reverse-splitting firms are generally more likely
to survive when expected market volatility is high. One possible explanation is that the survival of
these firms has an option-like quality which makes them more likely to survive if the market turns up
and the market volatility variable is capturing this upside potential. Pre-split market returns are
positively related to survival, suggesting that firms that have relatively better stock price performance
prior to the split, are more likely to survive following the split. Ex-date returns are also positively
related to survival, suggesting that the market is able to forecast to some extent which firms will
survive.
As shown in Table 3, the results in Model 2 for the non-industry variables are both qualitatively and
quantitatively similar to those from Model 1. However, a number of the industry dummy variables are
also significant at the 10% level or better. We find that survival is more likely for reverse splitting
firms in the following industries: oil & gas, chemicals manufacturing, electronic & electrical
equipment manufacturing, measuring & analyzing equipment manufacturing, communications,
12
depository financial institutions, and doctors, hospitals & nursing homes.
Table 4 presents the maximum likelihood results for the log-logistic AFT models of post-split
duration. As in Table 3, Model 1 includes only the quantitative explanatory variables and Model 2
uses the same quantitative variables plus the industry dummy variables. As predicted, size is
positively associated with survival time. In addition, more profitable firms (or at least less
unprofitable firms) survive longer following a reverse split. Interestingly, market volatility is not
significant in explaining post-split survival time. However, the six-month pre-split market return is
positively associated with survival time, suggesting firms that do relatively well prior to the split
survive for a longer period. The ex-date returns are positively associated with survival time,
suggesting that the market is able to predict to some extent the ex-post survival time of reversesplitting firms.
When the industry dummy variables are added in Model 2, the results are qualitatively similar. The
estimated coefficient on ROA increases in significance. Firms in the oil & gas, chemicals
manufacturing, and doctors, hospitals & nursing homes industries tend to have longer survival times
while firms in the mining and movies, amusement & recreation industries tend to have shorter
survival times after controlling for the other explanatory factors.
5. Conclusions and implications
We document low survival rates and short survival periods for most firms that undertake a reverse
stock split. Consistent with other studies, we find that mean returns are negative for reverse stock
split announcements. We also find negative returns around the ex-date. Our main contribution is
documenting the relationship between survivability and firm characteristics. We find a positive
13
relation between survival and firm size, market volatility, pre-split stock price performance, and exdate returns. We also document a positive relation between survival time and firm size, pre-split
operating performance, pre-split stock price performance, and ex-date returns.
14
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15
Table 1: Descriptive statistics for reverse split firms
Statistics in Panel A are reported prior to winsorization of values below (above) the 1st (99th) percentile.
Panel A: Statistics for full sample (N=1,215)
Mean
Median
Survival dummy (%)
28.9
Survival period (months)
44.9
Total assets ($ millions)
1,890.8
Months listed prior to split
111.7
Return on assets (%)
-64.0
CBOE volatility index (%)
22.6
Pre-split 6-month run-up (%)
-2.1
Announcement date returns (%)
-2.9
Ex-date returns
-6.6
Panel B: Sample distribution by split factor
Split factor
1 for 10
1 for 5
1 for 4
1 for 3
1 for 6
1 for 2
1 for 8
1 for 20
Panel C: Sample distribution by industry
SIC code (Industry description)
*
28
73
61-67
52-59
13
50-51
35
36
(Technology)
(Chemicals & allied products mfrs.)
(Business services)
(Financial institutions – non-depository)
(Retail trade)
(Oil & gas extraction)
(Wholesale trade)
(Industrial & commercial machine mfrs.)
(Electrical & electronic equipment)
0.0
25.9
38.4
88.0
-22.4
21.5
-1.8
-3.0
-5.5
# of
firms
244
231
175
138
71
67
53
48
# of
firms
325
129
84
72
68
61
52
37
36
Minimum
0.0
0.3
0.1
0.5
-7508.7
9.9
-47.8
-77.3
-78.2
Maximum
Std Dev
1.0
206.4
860,418.0
1,012.9
168.5
80.9
9.4
359.5
175.4
45.3
46.7
26,790.0
99.2
278.0
8.2
2.9
21.6
19.3
# of
firms
39
39
15
15
12
9
60
1,215
Split factor
1 for 7
1 for 15
2 for 5
1 for 12
1 for 25
1 for 50
All other split factors
Total firms
SIC code (Industry description)
60
80
87
10-12
38
78-79
49
48
(Financial institutions - depository)
(Doctors, hospitals, nursing homes)
(Engineering, accounting & mgt svcs.)
(Metal & coal mining)
(Measuring & analyzing equipment mfrs.)
(Movies & amusement and recreation svcs.)
(Utilities)
(Communications)
Firms in industries with fewer than 10 obs.
Total firms
*Following Loughran and Ritter (2004), SIC codes 3571, 3572, 3575, 3577, 3578, 3661, 3663, 3669, 3671, 3672, 3674,
3675, 3677, 3678, 3679, 3812, 3823, 3825, 3826, 3827, 3829, 3841, 3845, 4812, 4813, 4899, 7371, 7372, 7373, 7374,
7375, 7378, and 7379 are categorized as Technology firms. Other classifications are based solely on 2-digit SIC codes
after excluding those companies classified as Technology firms.
16
# of
firms
36
31
28
26
26
26
17
14
147
1,215
Table 2: Comparison of surviving firms with non-surviving firms
Panels A and B report unwinsorized statistics for the full sample while Panels C and D report statistics for the
full sample after winsorization of values below (above) the 1st (99th) percentile.
Panel A: Surviving firms (N = 351)
Mean
Survival period (months)
Total assets ($ millions)
Months listed prior to split
Return on assets (%)
CBOE volatility index (%)
Pre-split 6-month run-up (%)
Announcement date returns (%)
Ex-date returns
Panel B: Non-surviving firms (N = 864)
Median
38.3
5,532.5
124.8
-40.9
23.4
-1.4
-2.9
-1.9
Mean
20.8
66.0
95.9
-12.9
21.9
-1.2
-2.7
-1.6
Median
Survival period (months)
47.6
Total assets ($ millions)
411.4
Months listed prior to split
106.3
Return on assets (%)
-73.3
CBOE volatility index (%)
22.3
Pre-split 6-month run-up (%)
-2.4
Announcement date returns (%)
-2.9
Ex-date returns
-8.4
Panel C: Surviving firms (N = 351) – Winsorized variables
Mean
29.7
32.6
84.0
-27.5
21.3
-2.1
-3.4
-7.3
Median
Survival period (months)
38.3
20.8
Total assets ($ millions)
1,229.1
66.0
Months listed prior to split
120.9
95.9
Return on assets (%)
-40.0
-12.9
Panel D: Non-surviving firms (N = 864) – Winsorized variables
Mean
Survival period (months)
Total assets ($ millions)
Months listed prior to split
Return on assets (%)
47.6
332.8
105.2
-57.8
Median
29.7
32.6
84.0
-27.5
Minimum
0.3
2.6
1.0
-920.1
10.2
-47.8
-50.5
-42.2
Minimum
0.4
0.1
0.5
-7,508.7
9.9
-14.6
-77.3
-78.2
Minimum
0.8
2.6
8.5
-571.8
Minimum
0.8
1.5
8.5
-571.8
Maximum
200.6
860,418.0
930.7
35.7
79.1
6.3
67.5
122.2
Maximum
206.4
75,501.6
1,012.9
168.5
80.9
9.4
359.5
175.4
Maximum
195.4
19,489.5
461.4
20.8
Maximum
195.4
19,489.5
461.4
20.8
Std Dev
43.9
49,468.4
115.9
86.4
8.9
3.3
12.4
15.7
Std Dev
47.6
3,092.4
91.0
324.6
8.0
2.7
24.3
20.2
Std Dev
43.8
3,659.4
97.4
77.8
Std Dev
47.3
1,659.3
83.1
96.0
17
Table 3: Results for logit model of the post-split survival of reverse splitting firms
Variable
Intercept
Size – Ln(Total assets)
Operating perf. - Return on assets
Market volatility - VIX
Pre-split market return
Ex-date return
Model 1
-2.0324***
(0.2473)
0.2508***
(0.0362)
-0.0510
(0.0854)
1.6763**
(0.7963)
12.4746***
(2.8581)
1.5265***
(0.3781)
Technology
Mining - SIC 10 & 12
Oil & gas - SIC 13
Chemicals manufacturers - SIC 28
Industrial & commercial machinery - SIC 35
Electronic & electrical equipt mfrs - SIC 36
Measuring & analyzing equipt mfrs - SIC 38
Communications - SIC 48
Utilities - SIC 49
Wholesale trade - SIC 50-51
Retail trade - SIC 52-59
Financials - depository institutions - SIC 60
Financials - non-depository - SIC 61-67
Business services - SIC 73
Movies & amusement and recreation - SIC 78-79
Doctors, hospitals & nursing homes - SIC 80
Engineering, accounting & mgt services - SIC 87
Model 2
-2.4065***
(0.3336)
0.2605***
(0.0399)
0.0005
(0.0902)
1.8711**
(0.8213)
12.1296***
(2.9595)
1.4977***
(0.3892)
0.1814
(0.2404)
-0.5400
(0.6338)
0.7549**
(0.3400)
1.1258***
(0.2808)
0.5534
(0.4278)
0.7419*
(0.4226)
0.8094*
(0.4887)
1.0283*
(0.6166)
-0.2095
(0.6766)
-0.4952
(0.4570)
-0.3671
(0.3964)
1.0263***
(0.4217)
-0.3312
(0.3611)
0.4035
(0.3195)
-1.4388
(0.8948)
0.9489**
(0.4292)
0.4674
(0.4790)
***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
18
Table 4: Maximum likelihood results for log-logistic accelerated failure time model of the
post-split duration of reverse splitting firms
Variable
Intercept
Size – Ln(Total assets)
Operating perf. - Return on assets
Market volatility - VIX
Pre-split market return
Ex-date return
Model 1
3.8292***
(0.1423)
0.0826***
(0.0236)
0.1112**
(0.0461)
-0.0017
(0.5096)
15.0488***
(1.6273)
1.2151***
(0.2317)
Technology
Mining - SIC 10 & 12
Oil & gas - SIC 13
Chemicals manufacturers - SIC 28
Industrial & commercial machinery - SIC 35
Electronic & electrical equipt mfrs - SIC 36
Measuring & analyzing equipt mfrs - SIC 38
Communications - SIC 48
Utilities - SIC 49
Wholesale trade - SIC 50-51
Retail trade - SIC 52-59
Financials - depository institutions - SIC 60
Financials - non-depository - SIC 61-67
Business services - SIC 73
Movies & amusement and recreation - SIC 78-79
Doctors, hospitals & nursing homes - SIC 80
Engineering, accounting & mgt services - SIC 87
Model 2
3.5733***
(0.1891)
0.0753***
(0.0237)
0.1310***
(0.0457)
0.0418
(0.5023)
13.2283***
(1.5885)
1.1418***
(0.2282)
0.1357
(0.1379)
-0.4487*
(0.2719)
0.3827*
(0.2164)
0.4404***
(0.1730)
0.1043
(0.2569)
-0.0536
(0.2609)
0.2985
(0.3013)
0.4402
(0.4651)
0.1541
(0.3489)
-0.1472
(0.2182)
-0.2317
(0.1990)
0.5080
(0.3243)
-0.1357
(0.1985)
-0.0894
(0.1877)
-0.5523**
(0.2590)
0.6440**
(0.2898)
0.1525
(0.2755)
***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
19