REIT Splits and Dividend Changes: Tests of Signaling and
Information Substitutability
QIANG LI
Center for Urban Economics and Real Estate, Sauder School of Business, University of British
Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, Canada
Email: qiang.li@sauder.ubc.ca
HUA SUN
Center for Urban Economics and Real Estate, Sauder School of Business, University of British
Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, Canada
Email: hua.sun@sauder.ubc.ca
SEOW ENG ONG
Department of Real Estate, School of Design and Environment, National University of Singapore,
Singapore
Email: seong@nus.edu.sg
Abstract
Recent work on stock splits have attempted to relate the information value associated with splits
with that from dividends signaling. This paper extends this genre of research by evaluating the
issue of dividend predictability using REIT data where the self- selection issue associated with
dividend payment is minimized. The use of REIT data also eliminates the “differential
expectations” effect for non-dividend paying firms, thus rendering a more robust test of the
information substitutability hypothesis postulated by Nayak and Prabhala (2001). To the extent
that stock splits are signals of future cash flows, we further examine the question of leverage
predictability associated with REIT splits, particularly for highly levered firms. We find that
REITs that use dividend changes as a signaling mechanism prior to splits have smaller price
responses to the private information revealed by splits than those that do not provide such signals,
consistent with the notion that dividends and splits are indeed information substitutes. Further,
REIT splits provide useful information about future dividend and leverage changes.
Key Words: REIT split, dividend, leverage, signaling, conditional event study
1.
Introduction
Although stock splits and stock cash dividends are regular occurrences in the stock
market, the academic community has yet to reach a consensus understanding about these
events. Many theoretical and empirical attempts, for instance, have been made to explain
for the positive abnormal returns associated with stock splits. Dividend policy is a
fundamental topic in corporate finance, yet considerable debate has evolved around the
information value associated with dividend changes.
Two lines of research exist in the stock split literature. The first focuses on management
motives such as a desire to guide stock price to an optimal trading range when the price is
too low or too high (Lakonishok and Lev, 1987), to draw “attention” to a stock (Grinblatt,
Masulis, and Titman, 1984) or to signal information to the market (Brennan and
Copeland, 1988). These explanations are consistent with the existence of positive excess
returns around split (McNichols and Dravid, 1990). The second line of enquiry
hypothesizes that positive announcement-date abnormal returns observed for stock splits
are due to liquidity change or volatility change following splits (Lamoureux and Poon,
1987; Lakonishok and Lev, 1987). Regardless of the hypotheses, the consensus is that
there are short-run abnormal returns associated with stock splits. The long-run price
effect of splits, however, is mixed. Byun and Rozeff (2003) find no positive long-run
abnormal returns following splits using calendar-time methodology proposed by Mitchell
and Stafford (1998) and view the results as support for the market-efficiency hypothesis.
In contrast, Ikenberry and Ramnath (2002) find positive and statistically significant longrun abnormal returns using a different methodology and explain the results as support for
systematic market under-reaction.
The literature on dividend policy is similarly divided. The idea that dividend changes
convey information is not new. Miller and Modigliani (1961) suggest that dividends
provide information about a firm’s future cash flow when markets are incomplete.
Bhattacharya (1979), Miller and Rock (1985), John and Williams (1985), among others,
postulate theories based on the notion of asymmetric information. In their theories,
dividend changes are explicit signals about future earnings and they act as a mechanism
for management to convey information. Empirical tests yield mixed results. Healy and
Palepu (1988) and Aharony and Dotan (1994) support these theories, the former
analyzing dividend initiations and omissions and the latter investigating dividend
changes. However, recent work by DeAngelo, DeAngelo and Skinner (1996) and
Benartzi, Michaely and Thaler (1997) do not support the signaling hypothesis.
In contrast, relatively few researches relate stock splits and dividends. Fama, Fisher,
Jensen, and Roll (1969) point out that positive returns associated with split
announcements are actually due to concurrent and future dividend increases. An
extensive study by Grinblatt, Masulis and Titman (1984) document that dividend-paying
firms have smaller price reaction to split announcement than non-dividend payers do.
This stylized fact is further examined by Nayak and Prabhala (2001) in a conditional
event-study framework. Their work supports the notion that dividend and split are
information substitutes. They also propose a method for separating the dividend effect
from other unspecified effects using a split sample that has simultaneous dividend
announcement.
Campbell (1991) provides an explanation for the relation among realized abnormal
returns, expected future dividends, and expected future returns, which is in line with the
intuitions behind Fama, Fisher, Jensen, and Roll (1969) and Nayak and Prabhala (2001).
Campbell uses a loglinear approximation to demonstrate that an increase in expected
future dividends is associated with a positive abnormal return today, while an increase in
expected future returns is associated with a negative abnormal return today. Put
differently, the positive split abnormal returns found in previous literature can be
attributed to two sources: changes in expected future dividends and changes in expected
future returns. In this regard, the “other effects” left unspecified by Nayak and Prabhala
(2001) could be factors that influence expectation of future returns.
We postulate leverage to be a natural candidate for the “other effects” that may influence
expected future returns. To the extent that a split is a signal of prospects of favorable cash
flows (Nayak and Prabhala, 2001), such cash flows can be deployed for debt reduction.
Conditional on a higher expected future cash flow, the trade-off theory of capital
structure predicts that a “highly” leveraged firm will pay off its outstanding debt to
decrease its leverage ratio to reduce the probability of bankruptcy. In contrast, the tradeoff theory has no clear-cut answer for the direction of leverage ratios for firms that have
relatively smaller amount of debt. Given that the gearing REITs tends to be high, we
expect the split decision to be negatively correlated, on average, with future leverage
changes. A change in capital structure through de-leveraging will in effect decrease the
risk premium or expected return of a particular split stock. This, in turn, results in a
positive announcement effect following the mechanism in Campbell (1991). Our
hypothesis here is that if split signals changes in future leverage, then the market will
react by pricing upwards the split security accordingly.
In this paper, we relate split and dividend decisions in the context of REIT stocks. REITs
are interesting in that they are required to pay out at least 90 or 95 percent of their
earnings in the form of dividends in order to qualify for tax-exempt status.1 This is in
contrast to general stocks, among which there are many that do not pay dividend at all. A
number of studies have documented the special features of REIT dividend. Bradley,
Capozza, and Seguin (1998) relate to the dividend signaling literature by analyzing the
relation between dividend policy and firms’ cash flow uncertainty. Kallberg, Liu and
Srinivasan (2003) examine dividend pricing models for the aggregate REIT index. By
contrast, our work focuses on this dividend pricing relation on individual securities in an
event-study context. A recent paper by Hardin, Liano, and Huang (2005) analyzes REIT
split to test the market efficiency hypothesis, providing the first comprehensive short-run
and long-run event study for REIT split. However, to the best of our knowledge, there is
no existing paper that relates REIT dividends with REIT splits.
REITs also provide an interesting case study in capital structure. 2 The existence of
leverage in REITs is not attributable to tax reasons since there is no tax advantage.
Further, the high REIT dividend payout requirements should mitigate the pecking order
hypothesis. Brown and Riddiough (2003) suggest that REIT issues debt in order to target
long-run debt ratios to maintain investment-grade credit rating.
Another issue we address in this paper is the problem of self-selection in stock splits.
Traditional cross-sectional regressions that examine the determinants of event-date
abnormal returns are subject to self-selection biases. We opt for a conditional event-study
framework that corrects for this problem. There are many papers that employ conditional
event-study methods to investigate corporate events and decisions. Acharya (1988)
develops a generalized econometric model to test the signaling hypothesis. He points out
that event studies that estimate the market price responses to signals have to condition on
a rational decision rule. The work by Eckbo, Maksimovic, and Williams (1990) defines
what is called “conditional event-study” in recent years. They use nonlinear Maximum
Likelihood method to correct for the self-selection bias. Another important paper is
Prabhala (1997), in which he proposes the idea that only the unexpected part of a
corporate decision announcement should influence stock price reaction on event. He also
develops an econometric model based on some assumptions about the information
structure between management, investors, and market. Nayak and Prabhala (2001) is an
experiment for the model proposed in Prabhala (1997). Please see Appendix for details.
In summary, this paper addresses four questions. First, do firms with different dividend
policies have different price reactions to stock split? For example, do firms with high
dividend payout ratios have the same price reaction to splits as firms with low dividend
payout ratios? Given that REITs are required to pay dividends regularly, the analysis will
be much cleaner and easier than that for stocks in general.
Second, can a REIT split actually predict future dividend changes? This is of interest for
investors who value long-term investments. We call this the “dividend predictability”
hypothesis. In relation to this hypothesis is our third question that we term the “leverage
predictability” hypothesis. If a split provides a signal for future dividend increases and
cash flow increases, we should expect some firms to change their capital structure, too.
Our analysis is not so much concerned with the validation of alternative capital structure
theories than with the basic premise that REITs with excess future cash flow, as
manifested by stock splits, should consider debt reduction that is consistent with the
trade-off theory and to the extent that high debt levels imply higher bankruptcy risks. We
seek to establish whether positive abnormal return associated with split announcement
can be partially explained by an expected change in future capital structure.
Finally, dividend and split both can be seen as management signals to the market, but are
they substitutes or complements? Our priors are that they are substitutes. Management
can signal good cash flow prospects by setting a high dividend payout ratio, increasing
dividends or issuing a split. We divide the whole sample into firms that increase
dividends before split and those that maintain or decrease dividends. We also segment by
dividend payout ratios - high versus low dividend payout ratio. Using the conditional
method, we test whether these two kinds of firms have different price reactions to
unexpected information arising from a split. The rationale is that firms that increase
dividends or that have high dividend payout ratio are already communicating private
information. Therefore, these firms should have lower price reactions to a given amount
of unexpected information in splits. We term this, following Nayak and Prabhala (2001),
the “information substitute” hypothesis.
The contributions of this paper are twofold: First, we extend the Nayak and Prabhala
(2001) study by utilizing REIT data to better examine the dividend predictability
hypothesis. Nayak and Prabhala (2001) exclude non-dividend firms in their test for
dividend predictability. The exclusion of non-dividend firms raises a self-selection
problem, which means we can only observe firms that choose to pay dividends. The selfselection problem in the context of REITs is minimal. In addition, the fact that REITs
generally have high payout ratios and are perceived to possess higher transparency would
provide a downward bias to detecting information substitutability as compared to the use
of dividend and non-dividend paying firms in Nayak and Prabhala (2001). Put differently,
the use of REIT data eliminates the “differential expectations” effect (Nayak and
Prabhala, 2001) for non-dividend paying firms. Second, we extend the cash flow
predictability argument underlying the dividend predictability hypothesis to examine
leverage predictability. The rationale is that debt reduction arising from prospects of
favorable cash flows is an alternative to increasing REIT dividends. Furthermore, this
subsequent change in capital structure may partially explain split announcement
abnormal returns by way of changing expected future returns. This will add to our
understanding about the signaling content of a split announcement.
The remainder of this paper is structured as follows. Section 2 provides the methodology
used for the calculation of abnormal returns. We also more clearly outline how we test
the “dividend predictability”, “leverage predictability”, and the “information substitute”
hypothesis. Section 3 describes our sample choice criteria. Section 4 presents the
empirical results. Section 5 concludes.
2.
Research Methodology
2.1
Calculation of Abnormal Returns
Researchers often use the market model as a benchmark to calculate abnormal returns.
However, as Brown, Goetzmann, and Ross (1995) point out, stock splits are conditioning
on large price runups. Therefore, the calculation of abnormal returns based on market
model that utilizes price data not long before split, will possibly bias the abnormal returns
downward. We employ a more reliable size-matching method, which is popular in longrun event-studies. We only use size as the dimension to match because there is little
efficiency gain by adding other matching dimensions with a REIT population of about
100. Following Nayak and Prabhala (2001), we calculate the cumulative daily abnormal
returns from 1 day before to 1 day after the declaration date in reference to a benchmark
return based on size matching:
CARi =
t 0 +1
∑ (R
t = t 0 −1
i ,t
− Rs ,m ,t ) ,
where CARi is the cumulative abnormal return of security i from t0 - 1 to t0 + 1, Ri ,t is
return on date t, and Rs ,m,t is the equal-weighted return of size decile s to which security i
belongs at the beginning of the calendar year containing t, and t0 is the date that firm
announces split.
The size deciles are constructed as follows. We first choose REITs that have no splits
within a (-240, 240) window around all announcement dates determined by the split
sample. At the beginning of each year, we divide all the REITs into 5 size deciles based
on their market values of equity at the end of last year. If the market value of equity is
missing for a firm, we go back 5 months till July of the previous year to find a substitute.
If we still cannot find a market value, this REIT is dropped from our reference portfolios.
Next, we calculate daily return for a particular size decile till the end of a year assuming
equal weighting among all firms in this decile. At the end of each year (or at the
beginning of the next year), these 5 portfolios are rebalanced to reflect the fact that some
firms are delisted and some new firms enter into CRSP. Finally, we compute the daily
returns for 5 size deciles during the period 1981-2001.
2.2 Tests of “dividend predictability” hypothesis and “leverage predictability” hypothesis
The “dividend predictability” hypothesis views splits as signals for future dividend
increase, and it is this inherent nature of split that drives up the prices of split stocks
around the announcement date. Fama, Fisher, Jensen, and Roll (1969) suggest that future
dividend increase is the main reason for the positive abnormal returns around split. An
econometric framework to measure quantitatively the contribution of the future dividend
increase to announcement date abnormal returns is provided by Nayak and Prabhala
(2001). Due to the relatively small number of REIT splits, we cannot decompose
abnormal returns in this manner. In spite of this, we are still able to test “dividend
predictability” hypothesis directly using REIT splits.
The difference between our work and Nayak and Prabhala (2001) is that we use the entire
REIT sample since all REITs pay dividend, hence we minimize the self-selection
problem. We directly run an ordered probit regression below to test the hypothesis. The
regression is as follows:
FUTURE 4 DIVi = θ d 0 + θ d 1 ⋅ SPLITi + θ d 2 ⋅ CURRENTDIVi + θ d 3 ⋅ LAST 1DIVi + θ d 4 ⋅ PR _ PRICEi
+ θ d 5 ⋅ LSIZEi + θ d 6 ⋅ VOLUMEi + θ d 7 ⋅ RUNUPi + θ d 8 ⋅ VOLATLi + θ d 9 ⋅ AGEi + ψ di
The dependent variable in the ordered probit model is FUTURE4DIV, which is firm i’s
four-quarter forward dividend announcement. FUTURE4DIV takes the value -1, 0, and
+1, depending on whether the one-year forward dividend was decreased, maintained, or
increased, respectively. The explanatory variables are:
•
SPLIT: this takes the value 1 if a REIT announces a split and 0 otherwise.
•
CURRENTDIV: this takes the value 0 or 1, depending on whether the
contemporaneous dividend around the split date was unchanged or increased.
•
LAST1DIV: this takes the value 0 or 1, depending on whether the previous
quarter’s dividend was unchanged or increased, respectively. 3
•
PR_PRICE: price of the REIT five trading days before split.
•
LSIZE: the natural logarithm of the market value of the REIT’s equity five trading
days before split.
•
VOLUME: trading volume, computed as the ratio of the average number of units
traded in the month (approximated by 20 trading days) prior to the split to the
total number of outstanding units five trading days before the split.
•
RUNUP: the ratio of the stock price five trading days prior the split to the price
one year (approximated by 240 trading days) before split.
•
VOLATL: return volatility, computed as the standard deviation of returns over the
six month (approximated by 120 trading days) prior to the split.
•
AGE: the number of years from when a REIT was listed on a stock exchange to
the split date.
The SPLIT coefficient is of interest to us. A positive and statistically significant
coefficient supports the “dividend predictability” hypothesis. CURRENTDIV is included
to control for the information contained in current dividend about future dividend
prospect. The other variables, which will also appear in the probit regression for
predicting split, are included to control for the public information about future cash flows.
The “leverage predictability” hypothesis predicts that a split signals future leverage
changes, which effectively influences expected future returns. As a result, split signal
drives up the prices of split stocks around the announcement date. In order to test the
“leverage predictability” hypothesis, we introduce CURRENTLEV, the current leverage
ratio for a REIT in the set of explanatory variables as follows:
FUTURE 4 LEVi = θ l 0 + θ l1 ⋅ SPLITi + θ l 2 ⋅ CURRENTDIVi + θ l 3 ⋅ CURRENTLEVi + θ l 4 ⋅ LAST 1DIVi
+ θ l 5 ⋅ PR _ PRICEi + θ l 6 ⋅ LSIZEi + θ l 7 ⋅VOLUMEi + θ l 8 ⋅ RUNUPi + θ l 9 ⋅VOLATLi
+ θ l10 ⋅ AGEi +ψ li
The dependent variable observed is FUTURE4LEV: this takes the value -1, 0, and +1,
depending on whether the one-year forward leverage ratio was decreased (by more than
5%), unchanged (within the range of [-5%, 5%]), or increased (by more than 5%)
compared with current leverage ratio. 4
CURRENTDIV and LAST1DIV are included to control for current and one-quarter back
dividend changes which also have information content for future cash flow.
CURRENTLEV is included to control for the current leverage level, because we expect
high leverage firms to have higher propensity to decrease their leverage. We define
current leverage ratio as long term debt (COMPUSTAT quarterly DATA 51) divided by
common equity (COMPUSTAT quarterly DATA 59). As before, other control variables
are used to reflect the public information before the split. The coefficient on SPLIT is
expected to be negative and statistically significant if the “leverage predictability”
hypothesis holds.
2.3
Test of “information substitute” hypothesis
The “information substitute” hypothesis addresses the relation between dividends and
splits from the management’s perspective. Essentially, dividends and splits are both
viewed as signals about future cash flows. These two methods are substitutes to each
other, so managers should choose between them, or choose a mix of the two. When
choosing a mix, the effect of split will be subsumed by dividend signals. In other words,
investors regard the split signal sent by firms that have not sent previous dividend signals
as more valuable information. Conditional on the same set of variables, such as price
increase, price level, and trading activities, etc., firms that only use split as an information
transmission mechanism should have a larger price reaction to a unit of unexpected
information revealed by the split announcement.
We classify all REIT splits in two ways. First, we divide them into REITs that increased
dividend before split and those that did not, labeled “dividend increasing” and “dividend
non-increasing” REITs, respectively. Second, we classify REITs into those with
relatively high dividend payout ratios and those with relatively low dividend payout
ratios. The “information substitute” hypothesis predicts that dividend non-increasing
REITs and low dividend payout ratio REITs should have larger price response to a stock
split.
There are several ways to control for the self-selection problem. Here, we employ the
Heckman two-stage technique to analyze the effect of unexpected private information on
announcement date abnormal returns (please refer to Appendix for details). The basic
idea is that only the unexpected information revealed by split will have a material effect
on abnormal returns (Nayak and Prabhala, 2001). In order to get an estimate for the
unexpected information, we specify a rational decision rule for split decisions. Suppose
that firm i announces a split if a latent variable SPi is positive, where SPi can be
interpreted as the benefit of announcing a split. A portion of SPi is known by the public,
who use publicly available information before split to derive their estimate of SPi. In
addition to the public information, represented by a vector of variables Xsi, management
also has private information ψsi that is related to SPi but is unknown to the market.
Formally,
SPi = θ s′ ⋅ X si + ψ si ,
and firm i announces a split if
SPi = θ s′ ⋅ X si +ψ si > 0 ,
where E(ψsi) can be set to zero without loss of generality.
The announcement of a split reveals the private information ψsi to the market, that is
θ s′ ⋅ X si + ψ si > 0 . Based on this observation, investors can form updated expectations
about the splitting firm’s private information. The revised expectation of ψsi represents
information revealed by the split. If splits have positive valuation effects, we should find
that split announcement effects are positively related to the information revealed in the
split. Thus coefficient bs in the regression
E ( ARi SPi > 0 ) = γ s + bs ⋅ E (ψ si θ s′ ⋅ X si + ψ si > 0 )
should be positive. In the above equation, ARi denotes the announcement effect of firm i's
split, SPi denotes split decision, and the expectation notations stand for market’s belief
about announcement effect and firm i's private information correspondingly.
Based on the split decision model, we can build an empirical model for market
expectations of forthcoming splits with variables in public information set prior to the
split. Empirically our model for SPLITi, the binary variable for a split decision, is
SPLITi = θ s 0 + θs1 ⋅ PR_PRICEi + θ s 2 ⋅ LSIZEi + θs 3 ⋅ VOLUMEi
+ θs 4 ⋅ RUNUPi + θ s 5 ⋅ VOLATLi + θ s 6 ⋅ AGEi + ψ si
.
SPLIT takes the value 1 if a REIT announces a split, 0 otherwise. We include PR_PRICE
as firms with high share prices are more likely to split, if the trading range hypothesis
holds. LSIZE is a control variable to allow for the possibility that small firms and large
firms have different propensities to split. Firms that have a large price runup (RUNUP)
are more likely to split. AGE is included as a measure of a REIT’s maturity, because we
expect mature REITs and new REITs to have different split propensities. We also control
for trading activity before split using VOLUME and VOLATL.
The second-stage regression is
′
CARi = γ s + β s ⋅ λs ⎛⎜θˆs X s ⎞⎟ + ηi
⎝
⎠
′
where CAR is the cumulative abnormal returns around split announcement, λ ⎛⎜θˆs X s ⎞⎟ is
⎝
⎠
the inverse mills ratio for split announcement estimated using the above probit model. It
is an estimate for the revised expectation about firm’s private information. βs is the
coefficient of interest. The “information substitute” hypothesis is supported if βs of
dividend increasing REITs is lower than that of dividend non-increasing REITs and if βs
of high payout REITs is lower than that of low payout REITs.
3.
Samples
Our split sample consists of all REIT splits during the period from January 1981 through
December 2001 as contained on the Center for Research in Security Prices (CRSP) files
that meet the following criteria: (1) splits have distribution code 5523 (CRSP description:
stock splits, non-taxable) in CRSP tapes; (2) the splitting shares are REIT shares with
CRSP share code 18, 48, or 78; (3) at least 240 trading days of returns before and after
split date are available; (4) the split factor is positive. Because the focus is on REIT
dividend payout and leverage information, we also require that REITs have dividends,
earnings, long-term debt, and equity values in COMPUSTAT. 5 We also drop REITs that
have negative payout ratios (negative earnings and positive dividends). The resulting allsplit sample consists of 45 splits. Among them, 25 splits occurred before 1990. More
specifically, there are 14 splits from 1981 to 1985, 11 splits from 1986 to 1990, 7 splits
from 1991 to 1995, and 13 splits from 1996 to 2001.
The non-split sample is collected in the following ways. For each split REIT, we know
the split announcement date, which is used to identify REITs (stocks that have share code
18, 48, or 78 in CRSP) that do not split around a window from 240 trading days before to
240 trading days after this “event” date. Suppose, for example, a split is announced in
July 2000. We will sample all the existing REITs in the market and track their returns and
event information in the window (-240, 240), which spans from about July 1999 to July
2001. A REIT that does not split is included in the non-split sample.
Our sampling method differs from Nayak and Prabhala (2001). They take a randomly
chosen dividend date as the above mentioned “event” date for non-splitters paying
dividends. For non-dividend paying non-split firms, they use June 30 of the relevant year
as the “event” date. Our method is better in the sense that we match each split
observation with non-splitters and use the split date as the “event” date for the matching
firms. This “event” date is more relevant compared to a randomly chose dividend date or
a specific date in a year. Additionally, we require: (1) at least 240 trading days of returns
before and after the “event” date are available; (2) dividends, earnings, long-term debt,
and equity values in the COMPUSTAT database; (3) positive dividend payout ratio. The
final non-split sample comprises 3115 REITs. The number of REITs in non-split sample
is large because there is some overlap among the analysis windows for different split
events. This is not a serious problem because all that we require are cross-sectional
variables for our regressions.
In addition to the sampling of split REITs and non-splitters, we divide all the REITs in
both samples into two groups in order to test the “information substitute” hypothesis. We
have two criteria at hand: dividend payout ratio and dividend change before split. Using
the variable PAYOUT1 (the dividend payout ratio of a REIT in the year before the split
year), we classify all REITs into high payout REITs and low payout REITs using a
simple median demarcation. Ideally we should divide our sample into deciles and focus
on the highest and lowest payout ratio deciles. Since the sample contains only 45 REIT
splits, we resort to a coarser classification. Further, we employ the break point from the
split REITs to divide non-splitters. Using the dividend change before split as a criterion,
we define LAST4DIV, which takes the value of -1, 0, or +1, depending on whether the
dividend one-year ago was decreased, unchanged, or increased, respectively. We then
classify all REITs into dividend increasing REITs and dividend non-increasing REITs
using this variable.
4.
Results
4.1
Descriptive statistics and short-run effects of REIT split
Panel A of table 1 shows that split and non-split REITs have quite different crosssectional characteristics. A group means test rejects the null at 1% level that split REITs
and non-split REITs have the same means within the stipulated variables. Dividend
increasing (henceforth DI) REITs are different from dividend non-increasing (henceforth
DNI) REITs in several respects: they have statistically different (at 5% level) mean
values in PR_PRICE, LSIZE and VOLATL. There is also more variation in the variables
listed for DNI REITs than those for DI REITs. However, the group means test cannot
reject the null hypothesis that they have equal means. High dividend payout (henceforth
HD) REITs are different from low dividend payout (henceforth LD) REITs in several
respects: they have statistically different (at 5% level) mean values in PAYOUT1 and
AGE. In addition, a group means test rejects the null that the two groups have same
means in the list of variables at 1% level. These facts provide some evidence to support
our two classification schemes.
For all split REITs, the median6 payout ratio is 0.9930, while the median payout of nonsplit REITs is 1.1508. Their difference in means on PAYOUT1 is statistically significant
at 1% level. The median value of PAYOUT1 for HD and LD REITs is 1.2715 and 0.6733
respectively. HD and LD REITs also have statistically different means in payout ratios.
In addition, REITs that split are relatively more mature firms. Within the split sample,
LD REITs are statistically more mature than HD REITs. Therefore, there is a clear link
between REIT maturity and its payout ratio given that more mature REITs have lower
payout ratios. A possible reason is that mature REITs have relatively smaller amount of
depreciation items than new REITs; hence a relatively larger denominator (earnings) and
a smaller numerator (cash dividends) in the calculation of payout ratio. This relation also
motivates us to include maturity measure in our model for predicting split decision.
Panel B reports additional information on split factors and multiple splits as well as
overlaps in the sub-samples. Only one REIT split has a split factor less than 0.25 7; so our
results will not change dramatically if we follow the sample selection criterion of Hardin,
Liano, and Huang (2005), in which they dropped splits with split factor less than 0.25.
Further, 10 out of 28 REITs have multiple splits, therefore multiple splits consists more
than half of our sample. 8 When we decompose our split sample using both dividend
changes and dividend payout ratios, we obtain a clearer picture of the relationship of our
two classification schemes. 11 out of 18 (or two-thirds) DI REITs are HD REITs, while
16 out of 27 (or approximately two-thirds) DNI REITs are LD REITs.
Table 2 presents the short-run market reaction to REIT split announcements. The shortrun abnormal return around announcement date within a (-1, +1) window is about 3.17%
and statistically significant at the 1% level. These results are consistent with Hardin,
Liano, and Huang (2005) that document an average announcement date abnormal return
of 4.31% for the (-2, +2) window. An interesting result not captured in Hardin, Liano,
and Huang (2005) is that the average abnormal return for HD REITs is lower than that for
LD REITs and the difference is statistically significant at 5% level. However, the average
abnormal return for DI REITs is lower than DNI REITs but not statistically significant at
5% level. HD REITs are analogous to dividend paying firms analyzed in Nayak and
Prabhala (2001) in the sense that they all use dividend as a device to communicate
information to the market, while LD REITs are similar to non-dividend firms. Therefore,
our results are consistent with the findings of Grinblatt, Masulis, and Titman (1984) and
Nayak and Prabhala (2001), which document bigger split announcement effects for nondividend firms than dividend paying firms. Our results using REITs data underscore the
robustness of these findings as the difference is not only between dividend and nondividend paying firms. The inference is that information contained in split
announcements for HD REITs is less valuable because HD REITs have utilized higher
dividend as an information transmission mechanism. We defer an analysis of the
“information substitute” hypothesis as a possible explanation for this observation to
section 4.3.
Table 2 also reports the short-run abnormal returns for split REITs over different periods.
We use 1990 as a cutting point because of the common belief that there was a structural
change in REIT industry around that time (Clayton and MacKinnon 2003). We can see
that after 1990, the scale of this abnormal return have decreased substantially —from
4.62% to 1.35%. This is probably due to two facts in the 1990s: (1) REIT market became
more dominated by institutions (Chan, Leung and Wang 1998), therefore more liquid; (2)
Higher analyst coverage (Gentry, Kemsley and Mayer 2003) and news coverage (Chui,
Titman and Wei 2003) for REITs, which increased the transparency of REIT market.
4.2
Tests of “dividend predictability” and “leverage predictability” hypotheses
Table 3 presents estimates of an ordered probit regression to evaluate dividend
predictability. In our sample, 23 REITs increase dividends 4 quarters after they split, 20
leave dividends unchanged, and only 2 decrease dividends. This pattern persists
regardless of the classification scheme we use – it is rare for split REITs to decrease their
4-quarter forward dividends.
As motivated earlier, we must control for the effect of current dividend changes. Current
dividends should have two opposing effect on future dividends. On the one hand, an
increase in current dividends signals higher future cash flows which imply future
dividend increases. Therefore a positive correlation may exist between current and future
dividend changes. On the other hand, current dividend increases may diminish the
likelihood of future dividend increases if some optimal payout ratio target has been
achieved. In addition to current dividend changes, we further include a lagged measure
for dividend changes in order to control for lagged effect. Included also are variables that
have predictive power for split decision.
SPLIT is positive and significant at 1% level, which supports the “dividend
predictability” hypothesis. This result validates the earlier study by Hardin, Liano, and
Huang (2005), which suggests that dividends increase after splits. The coefficients on
dividend changes are hard to explain due to their mixed effect, although for current
dividend changes the signaling effect seems to prevail. There is also a lagged effect from
LAST1DIV, but the coefficient is negative, suggesting a reverse effect. Our model also
shows that larger REITs are more likely to increase dividends, while more mature and
higher price REITs are less likely to increase dividends, other things being equal. 9
Table 4 reports an ordered probit model that evaluates the “leverage predictability”
hypothesis. Of all 45 split REITs, 23 decrease leverage after splits, 17 increase leverage
instead, and the remaining 5 left leverage unchanged. Following the same rationale as
before, we must include an additional variable CURRENTLEV because firms’ capital
structure decisions are closely related with current leverage levels. As shown in column 2,
the SPLIT coefficient is negative and statistically significant at the 5% level, hence
supporting the “leverage predictability” hypothesis. The coefficients on current dividend
changes (CURRENTDIV) are negative because current dividend increases signal good
prospect for future cash flows. But the effect is not statistically significant. The
coefficient on CURRENTLEV is negative and consistent with our expectation, but it is not
statistically significant. In addition, larger REITs are less likely to decrease their leverage
after splits. REITs that have a larger price runup, a higher price or a longer history are
more likely to decrease their leverage after splits.
To explore the relation between future leverage changes and future dividend changes, we
re-estimated the ordered probit model for two sub-samples: REITs that increase fourquarter forward dividends and REITs that do not increase their four-quarter forward
dividends. We see from table 4 column 3 that the split announcement has no predictive
power for future leverage changes for REITs that increase their future dividends. Almost
all the variables in the model have no partial effect on the probability of future leverage
changes. But for REITs that do not increase their future dividends (shown in column 4 of
table 4), a split has a statistically significant negative effect on future leverage increase.
Other variables are, in contrast, statistically significant. In particular, the coefficient on
CURRENTLEV is negative and statistically significant, consistent with the capital
structure tradeoff theory. In addition, larger REITs are less likely to decrease their
leverage after splits. REITs that have a larger price runup, a higher price or a longer
history are more likely to decrease their leverage after split.
While it is clear that a REIT split increases the probability of leverage reduction when
future dividends are not increased, this likelihood is reduced (i.e., SPLIT coefficient is not
significant) when the REIT do in fact raised dividends subsequently. One possible
explanation for this result is that dividend increases and leverage decreases are two forms
of good news for a REIT. A split signals good prospect for the REIT, attributable to
either dividend increase or leverage decrease that is related with bankruptcy costs as
implied by tradeoff theory.
We should note, however, that the conflicting results for the two sub-samples do not
undermine the leverage predictability hypothesis. From the perspective of an investor,
when she observes a split announcement she cannot tell a priori which firm will increase
its future dividend and determine the likelihood of a firm decreasing its future leverage.
In this regard, the model reported in column 2 is more appropriate for the test of leverage
predictability hypothesis.
4.3
Tests of “information substitute” hypothesis
We observe in section 4.1 that HD REITs experience smaller announcement effects than
LD REITs. Similarly DI REITs experience smaller announcement price effects than DNI
REITs. What are the underlying reasons for these empirical facts? Can the “information
substitute” hypothesis explain these facts? Table 5 reports the Heckman 2-stage analysis
of the “information substitute” hypothesis using DI and DNI REITs over the period 19812001. Column 2 presents the probit estimates for the pooled sample. The coefficients on
PR_PRICE, LSIZE and AGE imply that higher price, smaller and more mature REITs are
more likely to split. In the second stage regression, the coefficient on the inverse mills
ratio is positive and significant: the unexpected information revealed by split has a
positive effect on announcement date abnormal return. The estimated intercept is not
statistically different from zero, suggesting that only unexpected information should
influence split date abnormal returns. This is consistent with our basic model.
The specification in column 2 relies on the assumption that all REITs including DI and
DNI REITs have the same underlying expectation model for split decision, which may be
incorrect. Colume 3 and column 4 provide different probit estimations for DI and DNI
REITs. The coefficients on PR_PRICE for DI and DNI REITs are different. For a given
level of price increase, DI REITs are more likely to split compared with DNI REITs,
other things being equal. The coefficients on LSIZE are both negative, but the coefficient
of DI REITs is lower. An increase in maturity increases the likelihood of issuing a split
for both DI and DNI REITs.
The effect of λs (the inverse mills ratio or unexpected information revealed by split) for
both DI and DNI REITs is positive and statistically significant at 10% level. The
estimated intercepts for these two sub-samples are both statistically insignificant. The
coefficient for DI REITs is smaller than that for DNI REITs but the difference is not
statistically significant at 5% level. 10 This result lends some support to the “information
substitute” hypothesis.
Table 6 reports a similar analysis using HD and LD REITs. The results in the first-stage
probit model are basically the same as those in table 5. PR_PRICE, LSIZE and AGE have
statistically significant effect on the probability of splits. In the second-stage regression,
the estimated intercepts for the two groups are both statistically insignificant. However,
we find strong evidence to support the “information substitute” hypothesis from these
two sub-samples. The coefficients on λs are positive and statistically significant for LD
REITs but not statistically significant for HD REITs. In fact, the coefficient for LD
REITs is much larger than that on HD REITs (0.0497 versus 0.0099) and a difference test
between these two coefficients rejects the null at 1% level that the two coefficients are
equal. 11 The “information substitute” hypothesis is thus supported in that HD REITs
already signal their performance by setting a high payout ratio, so that the effect of
unexpected information revealed by split should be less than the same amount of
information from a LD REITs, which did not provide a dividend signal. Thus, dividend
and split are two alternatives for REITs to communicate private information.
5.
Concluding Remarks
This paper analyzes the relation between dividends and splits by investigating REITs’
dividend streams around their split announcements. Our results suggest that the positive
abnormal returns associated with REIT split announcement are due to revealed
information about REITs’ future dividend and leverage changes. Three hypotheses are
tested: (1) the “dividend predictability” hypothesis: split has information about future
cash flows and they are positively related; (2) the “leverage predictability” hypothesis: a
split can be viewed as a signal on the likelihood of de-leveraging for highly leveraged
REITs; (3) the “information substitute” hypothesis: dividends and split are information
substitutes and they can both be used to signal future cash flows; hence REITs that send
dividend signals to the market should have smaller price response to split event compared
with REITs that do not send dividend signals.
We find that REITs are likely to increase dividend payments after a split, but future deleveraging is more likely only for highly leveraged firms. Our empirical results also
support the idea that dividends and split are information substitutes for each other and
management does use split or dividends to signal their private information about firms’
earnings.
While the use of REITs as a test bed for dividend predictability and information
substitutability allows a cleaner test compared to that in Nayak and Prabhala (2001), the
relatively small sample of REIT splits is recognized as a limitation. In addition, it would
be interesting to extend the test of the leverage hypothesis to general stocks as well.
Appendix
Private information about firm i arrives on the information arrival date.
1. Truncated normal distribution
Suppose that Y is a normally distributed random variable, that is Y ~ N ( μ , σ 2 ) . The
truncated mean can be calculated by
φ ([a − μ ] / σ )
E(Y | Y > a ) = μ + σ
1 − Φ ([a − μ ] / σ )
= μ + σλ ([a − μ ] / σ ),
where φ (⋅) is the pdf of a standard normal variable and Φ (⋅) is the cdf. The
φ ([a − μ ] / σ )
function λ ([a − μ ] / σ ) = −
is called the inverse Mills ratio, or hazard
Φ ([a − μ ] / σ )
function.
2. Conditional event-study
Private information τi arrives at firm i on the information arrival date. This private
information is partially revealed to markets on the event date. In the conditional eventstudy literature, different assumptions about what markets know about τi before the event
date lead to three kinds of models.
Assumption 1: Prior to the event date, the market knows that information τi has arrived.
However, it does not know its content until it is partially revealed at the event date.
Acharya (1993) adopts this assumption.
Suppose the market’s pre-event expectation of τi is given by E −1 (τ i ) = θ ′X i , where Xi is a
vector of firm-specific variables in the pre-event public information set, and θ is a vector
of parameters. The firm’s private information can be normalized as ψ i = τ i − E −1 (τ i ) so
that E −1 (ψ i ) = 0 .
On the “event date”, the firm must choose between two mutually exclusive actions. It
chooses C ∈ {E , NE}, where E denotes “event” and NE denotes “nonevent”. The choice is
made according to
C = E if τ i = θ ′X i + ψ i > 0 ;
C = NE otherwise.
Observing C = E or C = NE, investors update beliefs about private information. The
unexpected information on the event date is thus
E (ψ i | C ) .
We add another two assumptions now to derive a linear relationship between abnormal
event date returns and the information innovation.
Assumption 4: Investors are risk-neutral toward the event, so that they only care about
their revised expectations about the mean of τi.
Assumption 5: Returns are linear in information.
Assumptions 1, 4, and 5 imply a linear relation
E ( ARi | C ) = bE (ψ i | C ),
where ARi is the event date abnormal return. Assume ψ i ~ N (0, σ ψ2 ) , we can rewrite the
above relationship as
φ (θ ′ X i / σ ψ )
′
E ( ARi | E ) = bE (ψ i | θ X i + ψ i > 0) = bσ ψ
= bσ ψ λe (θ ′ X i / σ ψ ) .
′
Φ (θ X i / σ ψ )
A test of the information effect is just a test of whether b is zero.
Assumption 2: Prior to the event date, the market does not know that information τi has
arrived. Eckbo, Maksimovic, and Williams (1990) implicitly make this assumption.
If market does not know that private information τi has arrived, the market has not formed
expectation about τi, too. Therefore, the market’s prior expectation about the mean of τi is
0. If the firm chooses E, the following relation must hold under A.4 and A.5
E ( AR | E ) = bE (τ | E ) = bE (τ | τ > 0) = b θ ′ X + σ λ (θ ′ X / σ ) .
i
i
i
i
[
i
ψ
e
i
ψ
]
A test of the information effect is just a test of whether b is zero.
Assumption 3: Prior to the event date, the market assesses a probability p ∈ (0,1) that
information τi has arrived. Prabhala (1997) suggests this approach.
Under this assumption, A.4, and A.5, it is not hard to show that the announcement effect
is E ( AR | E ) = bE (τ | τ > 0) = b (1 − p )θ ′ X + σ λ (θ ′ X / σ ) . In this case the market
i
i
[
i
i
ψ
e
i
ψ
]
form an expectation of τi based on the assessed probability that τi has arrived, so we write
E −1 (τ i ) = pθ ′X i . The firm’s private information can be expressed as
ψ i' = τ i − E−1 (τ i ) = ψ i + (1 − p )θ ′X i because firm has an additional layer of information
advantage due to investors’ uncertainty about the existence τi , so E−1 (ψ i′) = (1 − p)θ ′X i .
Here we are assuming that if investors know exactly that τi has arrived, E−1 (τ i ) = θ ′X i and
E−1 (ψ i ) = 0 .
3. Nayak and Prabhala (2001) model setup
Prior to the split date, investors know that the firm has private information τi. However, it
doesn’t know its content until it is partially revealed at the event date. They form
expectation about the value of τi based on public observable variables Xsi, that is
′
E−1 (τ i ) = θ s X si .
Firm i announces a split if and only if a latent variable
SPi = θ s′ ⋅ X si +ψ si > 0 ,
2
where ψ si ~ N (0, σ ψ ) . The announcement of a split reveals the private information ψsi to
the market, that is θ s ⋅ X si + ψ si > 0 . Based on this observation, investors can form
updated expectations about the splitting firm’s private information. Using the results for
truncated normal distribution, we get
E(ψ si | θ s′ X si +ψ si > 0) = E(ψ si | ψ si > −θ s′ X si )
φ (−θ s′ X si / σ ψ )
= σψ
1 − Φ (−θ s′ X si / σ ψ )
= σψ
φ (θ s′ X si / σ ψ )
Φ (θ s′ X si / σ ψ )
= σ ψ λ (−θ s′ X si / σ ψ ).
If splits have positive valuation effects, the abnormal return should be positively
correlated to the private information revealed by the split announcement. Therefore,
coefficient bs in the regression
E ( ARi SPi > 0 ) = γ s + bs ⋅ E (ψ si θ s′ ⋅ X si + ψ si > 0 ) = γ s + β s λ (− θ s′ X si / σ ψ ) should be
positive. They allow an intercept in the equation, but it should be zero under the
assumption they make.
4. Heckman two-stage estimation
1. Estimate the probit equation for split decision by maximum likelihood to obtain
estimates of θ. For each observation in the split sample, compute the estimated inverse
φ (θˆs′ X si / σˆψ )
.
mills ratio as λˆ (−θ s′ X si / σ ψ ) =
Φ (θˆ′ X / σˆ )
s
si
ψ
2. Estimate βs=bsσψ and γs by least squares regression of CAR on 1 and λ̂ . The estimated
intercept is just γˆs , and the estimated coefficient on λ̂ is the partial effect of unexpected
information revealed by split on split date abnormal returns times some positive constant.
σ̂ψ .
Acknowledgement
We wish to thank Stan Hamilton, research seminar participants at NUS and two
anonymous reviewers for their suggestions and invaluable comments.
Notes
1. The minimum distribution requirement is reduced from 95% to 90% under the REIT
Modernization Act 1999.
2. There is a vast literature on capital structure research in general (Modigliani and Miller, 1963;
Miller, 1977) and pertaining to REITs in particular (Howe and Shilling, 1988; Brown and
Riddiough, 2003). The trade-off theory is predicated on an optimal capital structure where firms
actively target debt ratios. In contrast, the pecking order theory (Myers and Majluf, 1984; Myers,
1984) suggests that firms follow a pecking order when choosing their financing sources.
Empirical tests have sought to evaluate the two competing theories (Shyam-Sunder and Myers,
1999; Murray and Goyal, 2003).
3. When calculating this variable and LAST1DIV, we find that some REITs only have two
quarters of dividends per year for earlier years in our sample period. This could either mean that
these REITs pay dividends semiannually or that these REITs pay no dividends periodically. We
assume that the dividend stream is regular and spread the semiannual dividends over two
quarters.
4. Because firms’ leverage ratios are constantly changing, a 5% criterion is set to capture
situations when there is no significant change in leverage. 5% is not the absolute change in the
leverage ratios, but a relative change computed from the absolute change of leverage ratio divided
by the leverage ratio in previous quarter.
5. We also examined the period from 1976 to 1980, but we did not find any usable stock split
events.
6. We present the median value for payout ratio and leverage ratio because of our concern about
outliers.
7. A split factor of 0.25 means that one share is ‘split’ into four shares.
8. REITs that have multiple splits averaged 2.7 splits over the sample period. We do not detect
any consistent pattern in the payout ratios of REITs that undertook multiple splits.
9. The results are unchanged when we removed subsequent observations of REITS that split more
than once, including only the first split.
10. We also performed the second stage regression for the combined sample (DI and DNI
REITs), introducing an interaction term between inverse mills ratio and a dummy that takes the
value 1 if a REIT increases its dividend before split. We cannot reject the null that the interaction
term coefficient is equal across the two groups.
11. A regression for the combined sample (DI and DNI REITs) with an interaction term as
detailed in the previous note rejects the null that the interaction term coefficient is equal across
the two groups.
References
Acharya, S. (1988). “A Generalized Econometric Model and Tests of a Signaling
Hypothesis with Two Discrete Signals,” Journal of Finance 43(2), 413-429.
Aharony, J., and A. Dotan. (1994). “Regular Dividend Announcements and Future
Unexpected Earnings: an Empirical Analysis,” Financial Review 29(1), 125-151.
Benartzi, S., R. Michaely, and R. Thaler. (1997). “Do Changes in Dividends Signal the
Future or the Past?” Journal of Finance 52(3), 1007-1034.
Bhattacharya, S. (1979). “Imperfect Information, Dividend Policy, and ‘the Bird in the
Hand’ Fallacy,” Bell Journal of Economics 10(1), 259-270.
Bradley, M., D.R. Capozza, and P.J. Seguin. (1998). “Dividend Policy and Cash-Flow
Uncertainty,” Real Estate Economics 26(4), 555-580.
Brennan, M. J., and T. E. Copeland. (1988). “Stock Splits, Stock Prices, and Transaction
Costs,” Journal of Financial Economics 22(1), 83-101.
Brown, D. T., and T. J. Riddiough. (2003). “Financing Choice and Liability Structure of
Real Estate Investment Trusts,” Real Estate Economics 31(3), 313-346.
Byun, J., and M. S. Rozeff. (2003). “Long-run Performance after Stock Splits: 19271996,” Journal of Finance 58(3), 1063-1085.
Campbell, J. (1991). “A Variance Decomposition for Stock Returns,” Economic Journal
101(405), 157-179.
Chan, S. H., W. K. Leung, and K. Wang. (1998). “Institutional Investment in REITs:
Evidence and Implications,” Journal of Real Estate Research 16(3), 357-374.
Chui, C. W. A., S. Titman, and K. C. J. Wei. (2003). “Intra-Industry Momentum: The
Case of REITs,” Journal of Financial Markets 6(3), 363-387.
Clayton, J., and G. MacKinnon. (2003). “The Relative Importance of Stock, Bond and
Real Estate Factors in Explaining REIT Returns,” Journal of Real Estate Finance &
Economics 27(1), 39 – 60.
DeAngelo, H., L. DeAngelo, and D. J. Skinner. (1996). “Reversal of Fortune Dividend
Signalling and the Disappearance of Sustained Earnings Growth,” Journal of Financial
Economics 40(3), 341-371.
Eckbo, E., V. Maksimovic, and J. Williams. (1990). “Consistent Estimation of CrossSectional Models in Event Studies,” Review of Financial Studies 3(3), 343-365.
Fama, E., L. Fisher, M. Jensen, and R. Roll. (1969). “The Adjustment of Stock Prices to
New Information,” International Economic Review 10(1), 1-21.
Fama, Eugene. (1998). “Market Efficiency, Long-term Returns, and Behavioral Finance,”
Journal of Financial Economics 49(3), 283-306.
Frank, M., and V. Goyal. (2003). “Testing the Pecking Order Theory of Capital
Structure,” Journal of Financial Economics 67(2), 217-248.
Gentry, W. M., D. Kemsley, and C. J. Mayer. (2003). “Dividend Taxes and Share Prices:
Evidence from Real Estate Investment Trusts,” Journal of Finance 58(1), 261-282.
Grinblatt, M. S., R. W. Masulis, and S. Titman. (1984). “The Valuation Effects of Stock
Splits and Stock Dividends,” Journal of Financial Economics 13(4), 461-490.
Hardin, W., III, K. Liano, and G. Huang. (2005). “REIT Stock Splits and Market
Efficiency,” Journal of Real Estate Finance and Economics 30(3), 297-315.
Healy, P. M., and K. G. Palepu. (1988). “Earnings Information Conveyed by Dividend
Initiations and Omissions,” Journal of Financial Economics 21(2), 149-176.
Heckman, J. (1979). “Sample Selection Bias as a Specification Error,” Econometrica
47(1), 153-161.
Howe, J. S., and J. D. Shilling. (1988). “Capital Structure Theory and REIT Security
Offerings,” Journal of Finance 43(4), 983 – 993.
Ikenberry, D., G. Rankine, and E. Stice. (1996). “What Do Stock Splits Really Signal?”
Journal of Financial and Quantitative Analysis 31(3), 357-377.
Ikenberry, D., and S. Ramnath. (2002). “Underreaction to Self-selected News Events: the
Case of Stock Splits,” Review of Financial Studies 15(2), 489-526.
Jagannathan, M., C. P. Stephens, and M. S. Weisbach. (2000). “Financial Flexibility and
the Choice between Dividends and Stock Repurchases,” Journal of Financial Economics
57(3), 355-384.
John, K., and J. Williams. (1985). “Dividends, Dilution, and Taxes: a Signaling
Equilibrium,” Journal of Finance 40(4), 1053-1070.
Kallberg, J. G., C. H. Liu, and A. Srinivasan. (2003). “Dividend Pricing Models and
REITs,” Real Estate Economics 31(3), 435-450.
Lakonishok, J., and B. Lev. (1987). “Stock Splits and Stock Dividends: Why, Who and
When,” Journal of Finance 42(4), 913-932.
Lamoureux, C. G., and P. Poon. (1987). “The Market Reaction to Stock Splits,” Journal
of Finance 42(5), 1347-1370.
McNichols, M., and A. Dravid. (1990). “Stock Dividends, Stock Splits, and Signaling,”
Journal of Finance 45(3), 857-879.
Miller, M., and F. Modigliani. (1961). “Dividend Policy, Growth, and the Valuation of
Shares,” Journal of Business 34(4), 411-433.
Miller, M. H. (1977). “Debt and Taxes,” Journal of Finance 32(2), 261-276.
Miller, M., and K. Rock. (1985). “Dividend Policy under Asymmetric Information,”
Journal of Finance 40(4), 1031-1051.
Mitchell, M., and E. Stafford. (2000). “Managerial Decisions and Long-term Stock Price
Performance,” Journal of Business 73(3), 287-329.
Modigliani, F., and M. H. Miller. (1963). “Corporate Income Taxes and the Cost of
Capital: A Correction,” American Economic Review 53(3), 443-453.
Myers, S.C., and N. Majluf. (1984). “Corporate Financing and Investment Decisions
When Firms Have Information that Investors Do Not Have,” Journal of Financial
Economics 13(2), 187-222.
Myers, S. C. (1984). “The Capital Structure Puzzle,” Journal of Finance 39(3), 575-592.
Nayak, S., and N. R. Prabhala. (2001). “Disentangling the Dividend Information in Splits:
A Decomposition Using Conditional Event-Study Methods,” Review of Financial Studies
14(4), 1083-1116.
Prabhala, N. R. (1997). “Conditional Methods in Event-Studies and an Equilibrium
Oriented Justification for Using Standard Event-Study Procedures,” Review of Financial
Studies 10(1), 1-38.
Shyam-Sunder, L., and S. C. Myers. (1999). “Testing Static Tradeoff Against Pecking
Order Models of Capital Structure,” Journal of Financial Economics 51(2), 219-244.
Table 1
Descriptive Statistics of Split and Non-Split REITs
over the Period 1981-2001
This table reports the cross-sectional characteristics of split and non-split REITs over the period 1981-2001. Panel A presents the crosssectional characteristics of all split REITs, non-split REITs and of four different sets of split REITs. PR_PRICE is the price level on
date [-5], where date [0] denotes the announcement date. VOLUME is computed as the ratio of the average number of REIT units
traded in the month (approximated by 20 trading days) prior to the split to the total number of outstanding REIT units five trading days
before split. VOLATL is return volatility computed as the standard deviation of returns over the six month (approximated by 120 trading
days) prior to the split. PAYOUT1 is the dividend payout ratio in the year before split. CURRENTLEV is current leverage ratio
calculated as the ratio between long term debt and common equity. AGE is the number of years from when a REIT was listed on a stock
exchange to the split date. Panel B presents the number of REITs that have split factor ≥ 0.25, that issue more than one split, and that
are both dividend increasing and with high payout, dividend non-increasing and with high payout, and so on. Dividend increasing
REITs and dividend non-increasing REITs are defined by the variable LAST4DIV, which takes the value 1, 0, and -1 if 4-quarter back
dividend is increased, maintained, or decreased. High payout REITs and low payout REITs are classified using the variable PAYOUT1.
The Null hypothesis for mean test and group mean test is that the means of two groups are equal. Numbers in parenthesis are standard
deviations.
Number of observations
Dividend
increasing
REITs
Dividend
nonincreasing
REITs
High
payout
REITs
Low
payout
REITs
All Split
REITs
All nonsplit REITs
18
27
22
23
45
3115
Panel A: Cross-sectional characteristics of split REITs
PR_PRICE
Mean test (p-value)
RUNUP
34.47
26.32
32.89
26.41
29.58
18.05
(10.82)
(12.09)
(11.74)
(11.94)
(12.16)
(9.82)
0.0234
1.2462
1.2248
0.0735
1.2601
1.2162
0.0000
1.2285
1.2378
(0.1573)
(0.2090)
(0.2139)
Mean test (p-value)
(0.2470)
0.7240
(0.2209)
0.4970
(2.2187)
0.8570
LSIZE
12.84
11.77
12.53
11.89
12.20
(1.35)
(1.49)
(1.57)
(1.43)
(1.52)
Mean test (p-value)
0.0173
2.0877
1.8360
0.1617
2.1479
1.8332
0.0769
2.2333
1.9870
(1.1330)
(1.1901)
(1.4438)
VOLUME
Mean test (p-value)
VOLATL
Mean test (p-value)
PAYOUT1[1]
Mean test (p-value)
CURRENTLEV[2]
Mean test (p-value)
AGE
(1.6316)
0.5447
(1.6633)
0.4682
1.21
1.57
1.28
1.56
1.42
(0.72)
(0.52)
(0.70)
(0.63)
(5.33)
Mean test (p-value)
Group means test (p-value)
(7.62)
0.1388
1.2715
0.6733
0.0000
0.8411
0.8693
0.2798
8.23
12.52
(6.72)
0.4305
0.1491
(1.66)
(2.1872)
0.2660
(0.36)
0.0331
1.0799
0.9040
0.0777
0.7584
0.8735
0.3209
11.04
9.5
11.79
(6.26)
1.74
(1.44)
0.0018
1.1508
0.9930
0.0000
0.7971
0.8660
0.7066
8.08
10.42
(6.56)
(6.77)
0.0322
0.0005
0.0260
0.0000
Panel B: Other statistics
Split factor ≥ 0.25
More than one split
Intersection with high
payout REITs
Intersection with low
payout REITs
Note:
[1]
18
-
26
-
21
-
23
-
44
10/28
-
11
11
-
-
-
-
7
16
-
-
-
-
PAYOUT1 is the median value instead of the mean value.
[2]
CURRENTLEV is the median value instead of the mean value.
Table 2
Announcement Cumulative Abnormal Returns (CARs)
for REIT splits over the Period 1981-2001
Table 2 reports the average split announcement effects (CARs) for REIT splits over the period 1981-2001. It also presents the results
based on different classification schemes of the whole sample, such as dividend changes, dividend payout ratios, and time. Dividend
increasing REITs and dividend non-increasing REITs are defined by the variable LAST4DIV, which takes the value 1, 0, and -1 if 4quarter back dividend is increased, unchanged, or decreased. High payout REITs and low payout REITs are classified using the
variable PAYOUT1, which is the dividend payout ratio in the year before split. CAR is calculated as
CAR i =
t 0 +1
∑ (R
t = t 0 −1
i ,t
− R s , m ,t )
where CARi is the cumulative abnormal return of security i from t0 - 1 to t0 + 1, Ri,t is return on date t, and Rs,m,t is the equally weighted
return of size decile s to which security i belongs at the beginning of the calendar year containing t, and t0 is the date that firm
announces split. The Null hypothesis for mean test is that the means of two groups are equal. Numbers in parentheses denote standard
errors. *** denotes CARi is statistically different from 0 at the 1% level, ** denotes CARi is statistically different from 0 at the 5%
level, and * denotes CARi is statistically different from 0 at the 10% level.
N
CAR
All REIT splits 1981-2001
45
3.17***
(0.50)
REITs that increase dividends before splits
18
2.47***
(0.68)
27
3.64***
(0.69)
REITs that do not increase dividends before
splits
Mean test (p-value)
0.2347
REITs with high dividend payout ratio
22
REITs with low dividend payout ratio
23
Mean test (p-value)
0.0198
All REIT splits 1981-1990
25
All REIT splits 1991-2001
20
Mean test (p-value)
2.00***
(0.54)
4.28***
(0.77)
4.62***
(0.71)
1.35***
(0.43)
0.0003
Table 3
Ordered Probit Analysis of Split Decision and Future Dividend Changes
for REITs over the Period 1981-2001
Table 3 presents estimates of an ordered probit regression that explores the predictability of future dividend changes by split
announcement using a sample of REITs over the period 1981-2001. The dependent variable is the four-quarter forward dividend
changes FUTURE4DIV, which takes the value 1, 0, or -1 if four-quarter forward dividend is increased, unchanged, or decreased
respectively.
FUTURE 4 DIV i = θ d 0 + θ d 1 ⋅ SPLIT i + θ d 2 ⋅ CURRENTDIV
+ θ d 5 ⋅ LSIZE
i
+ θ d 6 ⋅ VOLUME
i
i
+ θ d 3 ⋅ LAST 1 DIV i + θ d 4 ⋅ PR _ PRICE
+ θ d 7 ⋅ RUNUP i + θ d 8 ⋅ VOLATL
i
i
+ θ d 9 ⋅ AGE i + ψ
di
SPLIT equals 1 if a firm announces a split, 0 otherwise. CURRENTDIV equals 1 or 0, depending on whether current dividend is
increased or maintained. LAST1DIV equals 1or 0 depending on whether one-quarter back dividend is increased or maintained.
PR_PRICE is the price level on date [-5], where date [0] denotes the announcement date. LSIZE is the natural logarithm of the market
value of equity at date [-5]. VOLUME is computed as the ratio of the average number of REIT units traded in the month
(approximated by 20 trading days) prior to the split to the total number of outstanding REIT units five trading days before split.
RUNUP is the ratio of the price at date [-5] to the price at [-240]. VOLATL is return volatility computed as the standard deviation of
returns over the six month (approximated by 120 trading days) prior to the split. AGE is the number of years from when a REIT was
listed on a stock exchange to the split date. Numbers in parentheses are standard errors. *** denotes a coefficient is statistically
different from 0 at the 1% level, ** denotes a coefficient is statistically different from 0 at the 5% level, and * denotes a coefficient is
statistically different from 0 at the 10% level.
Dependent variables: Four-quarter forward dividend changes
Explanatory variables
SPLIT
CURRENTDIV
0.7235***
(0.1874)
1.0751***
(0.0526)
LAST1DIV
-0.1373***
PR_PRICE
-0.0095***
LSIZE
0.1151***
VOLUME
-0.0048
RUNUP
0.0076
VOLATL
-0.6559
AGE
-0.0087**
χ2
Number of Obs.
545.16
3160
(0.0518)
(0.0033)
(0.0200)
(0.0107)
(0.0101)
(1.7749)
(0.0035)
Table 4
Ordered Probit Analysis of Split Decision and Future Leverage
Changes for REITs over the Period 1981-2001
Table 4 presents estimates of an ordered probit regression that explores the predictability of future leverage changes by split
announcement using a sample of REITs over the period 1981-2001. Column 2 reports the results for all splits. Column 3 and column 4
report the results for two sub-samples: REITs that increase four-quarter forward dividend and REITs that do not increase their fourquarter forward dividend. The basis for this classification is the variable FUTURE4DIV, which takes the value 1, 0, or -1 if fourquarter forward dividend is increased, unchanged, or decreased respectively. The dependent variable in these models is the fourquarter forward leverage changes FUTURE4LEV, which takes the value 1, 0, or -1 if four-quarter forward leverage is increased,
unchanged, or decreased respectively.
FUTURE 4 LEV i = θ l 0 + θ l 1 ⋅ SPLIT i + θ l 2 ⋅ CURRENTDIV
+ θ l 6 ⋅ LSIZE
i
+ θ l 7 ⋅ VOLUME
i
i
+ θ l 3 ⋅ LAST 1 DIV i + θ l 4 ⋅ CURRENTLEV
+ θ l 8 ⋅ RUNUP i + θ l 9 ⋅ VOLATL
i
i
+ θ l 5 ⋅ PR _ PRICE
i
+ θ l 10 ⋅ AGE i + ψ li
SPLIT equals 1 if a firm announces a split, 0 otherwise. CURRENTDIV equals 1 or 0, depending on whether current dividend is
increased or maintained. LAST1DIV equals 1 or 0, depending on whether one-quarter back dividend is increased or maintained.
CURRENTLEV is the current leverage ratio calculated as the ratio between long term debt and common equity. PR_PRICE is the price
level on date [-5], where date [0] denotes the announcement date. LSIZE is the natural logarithm of the market value of equity at date
[-5]. VOLUME is computed as the ratio of the average number of REIT units traded in the month (approximated by 20 trading days)
prior to the split to the total number of outstanding REIT units five trading days before split. RUNUP is the ratio of the price at date [5] to the price at [-240]. VOLATL is return volatility computed as the standard deviation of returns over the six month (approximated
by 120 trading days) prior to the split. AGE is the number of years from when a REIT was listed on a stock exchange to the split date.
Numbers in parentheses are standard errors. *** denotes a coefficient is statistically different from 0 at the 1% level, ** denotes a
coefficient is statistically different from 0 at the 5% level, and * denotes a coefficient is statistically different from 0 at the 10% level.
Dependent variables: Four-quarter forward leverage
Future dividend
Future dividend nonAll REITs
increasing REITs
increasing REITs
Explanatory variables
SPLIT
-0.3948**
(0.1828)
(0.2692)
(0.2589)
CURRENTDIV
-0.0753
-0.1025
-0.0302
(0.0508)
(0.1005)
(0.0732)
LAST1DIV
0.0332
-0.0592
0.0608
(0.0520)
(0.1001)
CURRENTLEV
-0.0034
0.0004
(0.0021)
(0.0025)
PR_PRICE
-0.0059*
-0.0030
(0.0033)
(0.0079)
LSIZE
0.0609***
0.0296
0.0622***
(0.0196)
(0.0465)
(0.0219)
VOLUME
0.0082
-0.0055
0.0169
(0.0203)
(0.0130)
RUNUP
(0.0109)
-0.0265*
(0.0152)
0.0588
-0.0293*
(0.0822)
(0.0165)
VOLATL
0.8371
-10.9593*
1.9827
AGE
-0.0199***
χ2
Number of Obs.
(1.7603)
-0.3324
(6.3151)
-0.0117
-0.5348**
(0.0616)
-0.0313***
(0.0064)
-0.0077**
(0.0037)
(1.8873)
-0.0222***
(0.0034)
(0.0080)
(0.0038)
71.98
3160
10.38
739
96.38
2421
Table 5
Heckman 2-Stage Analysis of the “Information Substitute” Hypothesis
Using Dividend Increasing and Dividend Non-increasing REITs over
the Period 1981-2001
Table 5 reports Heckman 2-stage analysis of the “information substitute” hypothesis using dividend increasing and dividend nonincreasing REITs over the period 1981-2001. Dividend increasing REITs and dividend non-increasing REITs are defined by the
variable LAST4DIV, which takes the value 1, 0, and -1 if 4-quarter back dividend is increased, maintained, or decreased. Panel A
reports probit estimates of REITs’ split decisions on a set of explanatory variables. Empirically our model for SPLITi, the dummy
variable for split decision, is
SPLITi = θs 0 + θs1 ⋅ PR_PRICEi + θs 2 ⋅ LSIZEi + θs 3 ⋅VOLUMEi + θs 4 ⋅ RUNUPi + θ s 5 ⋅VOLATLi + θ s 6 ⋅ AGEi +ψ si
SPLIT takes on the value 1 or 0 depending on whether there is a split announcement or not respectively. PR_PRICE is the price level
on date [-5], where date [0] denotes the announcement date. LSIZE is the natural logarithm of the market value of equity at date [-5].
VOLUME is computed as the ratio of the average number of REIT units traded in the month (approximated by 20 trading days) prior
to the split to the total number of outstanding REIT units five trading days before split. RUNUP is the ratio of the price at date [-5] to
the price at [-240]. VOLATL is return volatility computed as the standard deviation of returns over the six month (approximated by 120
trading days) prior to the split. AGE is the number of years from when a REIT was listed on a stock exchange to the split date. Panel B
reports estimates of the regression:
CARi = γ s + β s ⋅ λs + η i
where the dependent variable CARi is the cumulative abnormal return of security i from t0 – 1 to t0 + 1, t0 is the date that firm
announces split. Λs is the unexpected information revealed by split and is computed as the inverse mills ratio based on the probit
model reported in panel A. Numbers in parentheses are standard errors. *** denotes a coefficient is statistically different from 0 at the
1% level, ** denotes a coefficient is statistically different from 0 at the 5% level, and * denotes a coefficient is statistically different
from 0 at the 10% level.
All REITs
Dividend increasing
REITs
Panel A: First-stage regression (Probit)
Dividend nonincreasing REITs
Dependent variable: SPLIT
Explanatory variables:
Constant
PR_PRICE
LSIZE
-1.2884**
-0.8424
-1.1135*
(0.5919)
0.0636***
(0.0077)
-0.2146***
(0.0556)
(1.6105)
(0.6711)
0.1016***
0.0534***
(0.0193)
(0.0090)
-0.3109**
-0.2197***
(0.1340)
(0.0645)
-0.1521
0.0162
VOLUME
-0.0263
(0.0375)
(0.1052)
(0.0406)
RUNUP
-0.0227
-0.0157
-0.0217
(0.0872)
(0.5982)
(0.0784)
VOLATL
1.9721
2.9490
1.2699
(5.5627)
AGE
0.0312***
0.0288**
(0.0098)
(23.9694)
0.0406*
(0.0211)
3160
836
Number of Obs.
(5.8896)
(0.0115)
2324
Panel B: Second-stage regression
Dependent variable: CAR
Explanatory variables:
Constant
-0.0280
-0.0046
(0.0235)
(0.0185)
(0.0413)
λs
0.0276***
0.0170*
0.0342**
(0.0105)
(0.0098)
(0.0173)
45
18
27
Number of Obs.
-0.0434
Table 6
Heckman 2-Stage Analysis of the “Information Substitute” Hypothesis
Using High Dividend Payout Ratio and Low Dividend Payout Ratio
REITs over the Period 1981-2001
Table 6 reports Heckman 2-stage analysis of the “information substitute” hypothesis using high dividend payout and low dividend
payout REITs over the period 1981-2001. High payout REITs and low payout REITs are classified using the variable PAYOUT1,
which is the dividend payout ratio in the year before split. Panel A reports probit estimates of REITs’ split decisions on a set of
explanatory variables. Empirically our model for SPLITi, the dummy variable for split decision, is
SPLITi = θs 0 + θs1 ⋅ PR_PRICEi + θs 2 ⋅ LSIZEi + θs 3 ⋅VOLUMEi + θs 4 ⋅ RUNUPi + θ s 5 ⋅VOLATLi + θ s 6 ⋅ AGEi +ψ si
SPLIT takes on the value 1 or 0 depending on whether there is a split announcement or not respectively. PR_PRICE is the price level
on date [-5], where date [0] denotes the announcement date. LSIZE is the natural logarithm of the market value of equity at date [-5].
VOLUME is computed as the ratio of the average number of REIT units traded in the month (approximated by 20 trading days) prior
to the split to the total number of outstanding REIT units five trading days before split. RUNUP is the ratio of the price at date [-5] to
the price at [-240]. VOLATL is return volatility computed as the standard deviation of returns over the six month (approximated by 120
trading days) prior to the split. AGE is the number of years from when a REIT was listed on a stock exchange to the split date. Panel B
reports estimates of the regression:
CARi = γ s + β s ⋅ λs + η i
where the dependent variable CARi is the cumulative abnormal return of security i from t0 - 1 to t0 + 1, t0 is the date that firm
announces split. λs is the unexpected information revealed by split and is computed as the inverse mills ratio based on the probit model
reported in panel A. Numbers in parentheses are standard errors. *** denotes a coefficient is statistically different from 0 at the 1%
level, ** denotes a coefficient is statistically different from 0 at the 5% level, and * denotes a coefficient is statistically different from
0 at the 10% level.
All REITs
High payout ratio
REITs
Panel A: First-stage regression (Probit)
Low payout ratio
REITs
Dependent variable: SPLIT
Explanatory variables
Constant
-1.2884**
0.6895
-2.3269***
(0.5919)
0.0636***
(0.0077)
-0.2146***
(0.0556)
(1.0933)
0.1119***
(0.0171)
-0.5096***
(0.1104)
VOLUME
-0.0263
-0.0794
(0.0375)
(0.0562)
(0.0581)
RUNUP
-0.0227
0.3742
-0.1258
(0.0872)
(0.2580)
(0.1964)
VOLATL
1.9721
3.6100
0.5993
(5.5627)
(13.0501)
(7.3612)
AGE
0.0312***
0.0140
0.0310**
(0.0098)
(0.0174)
(0.0139)
3160
2157
1003
PR_PRICE
LSIZE
Number of Obs.
(0.8103)
0.0442***
(0.0099)
-0.0635
(0.0751)
-0.0036
Panel B: Second-stage regression
Dependent variable: CAR
Explanatory variables:
Constant
-0.0280
0.0001
-0.0616
(0.0235)
(0.0180)
(0.0404)
λs
0.0276***
0.0099
0.0497***
(0.0105)
(0.0085)
(0.0184)
45
22
23
Number of Obs.