1. Repurchases and Recession: Did the 2008
financial crisis change how markets perceive
repurchase signals?
Cameron Melville∗
Department of Economics, Warwick University
Research in Applied Economics
Abstract
This paper adopts an event study methodology to estimate the abnormal returns to
share repurchase authorisation announcements during normal and recessionary periods.
It investigates the impact of recession on how investors respond to repurchase signals,
with an emphasis on two popular hypotheses cited in the literature: Jensen’s [1986] Free
Cash Flow Hypothesis, and the Undervaluation hypothesis. The study utilises a unique
dataset of Nasdaq repurchase announcements along with firm-specific characteristics from
2004 to 2013, implementing three estimation techniques: OLS regression, propensity score
matching, and multinomial logistic regression. Similarly to past empirical results, this
paper finds positive post-announcement cumulative abnormal returns of 4.9% and 4.3% for
normal and recessionary periods respectively, and extends previous findings by suggesting
that firms matched on fundamental characteristics are rewarded to a greater extent in
recessionary periods by 1.6%. This is reconciled with the hypothesis that investors move
towards safe haven stocks in times of crisis. Furthermore, support is confirmed for the Free
Cash Flow hypothesis in normal periods. Moreover, new findings are presented suggesting
that this relationship also holds during recessionary periods.
Keywords: Share Repurchases; Payout Policy; Signalling; Asymmetric Information; Recession.
JEL classification: G32, G35, D82, D83, E32.
Words: 5,100 including Footnotes and Tables.
∗
Many thanks to Alexander Karalis Isaac for his invaluable guidance and feedback throughout this project.
1
4. 1 Introduction
Open market share repurchases (OMRs) make up 97% of all repurchases and are a mecha-
nism by which a firm buys back its own publicly-traded shares or equity from the marketplace,
reducing the number outstanding. Repurchases1
have major implications for the wealth of firms,
shareholders and potential investors.
This paper stems from the existence of asymmetric information between firms and investors,
where managers are assumed to know more about the true quality of their firm. Repurchase
authorisation announcements are a signalling mechanism by which information is revealed to
the market. In the aftermath of the 2008 financial crisis, repurchase authorisations declined by
58% in the US (see Appendix Table 11). This paper examines how recession affects the way
investors’ expectations respond to the informational revelation of a repurchase announcement2
.
There is an extensive literature studying repurchases and their empirics, and several theories
have been developed to explain them in ‘normal’ periods. However, the implications of a recession
for the abnormal returns to repurchase announcements are not well documented.
In normal periods, reactions to repurchases are positive on average. The two most cited
theories are the Free Cash Flow (FCF) and Undervaluation hypotheses. Free Cash Flow hy-
pothesis: paying out to shareholders is better than investing in value-destroying projects, and
mitigate the agency costs of equity (Jensen [1986]). Undervaluation hypothesis: managers would
only rationally repurchase if they believed their stock was ‘cheap’ (undervalued). The market
should therefore correct the mis-priced shares after this information is revealed. Repurchases
are also a costly but affordable signal for good quality firms indicate higher earnings potential
than previously acknowledged.
However, these mechanisms may not be valid in periods of recession. The rationale is that
firms, knowing repurchases are associated with positive increases in firm value, will choose to
repurchase in recession when their share price is falling. However, investors may realise that
these repurchases lack credibility, and subsequently become more risk averse, dampening their
reactions.
This paper aims to answer the following questions and hypotheses:
Is there a difference in abnormal returns to repurchase authorisation announcements between
recession and normal periods?
Hypothesis 1: H0 : No difference, investors’ expectations are unaffected.
1
OMRs are subsequently referred to as ‘repurchases’.
2
Recession is defined by the fall and recovery of the Nasdaq stock market from the 1st
August 2008 to the 1st
January 2010.
3
5. Further, do the popular explanations for abnormal returns to repurchases, the FCF and
undervaluation hypotheses still hold in recession? Is there a difference in the positive relationship
between free cash flow and abnormal returns in recession?
Hypothesis 2: H0 : No difference, the mitigation of agency costs of equity is unaf-
fected in recession.
Is there a difference in the positive relationship between undervaluation and abnormal returns
in recession?
Hypothesis 3: H0 : No difference, repurchase authorisation announcements still
signal undervaluation in recession.
These hypotheses should expose whether investors mute their positive responses for recession-
ary repurchases, and simply perceive firms as trying to boost their falling stock prices. Further,
whether this is because firm fundamentals do not match the required criteria to secure positive
abnormal returns in normal periods.
This paper finds that repurchase announcements are associated with positive cumulative
abnormal returns (CARs) of 4.9% and 4.3% in normal and recessionary periods respectively.
Interestingly, firms of the same quality as represented by firm characteristics are associated with
higher CARs in recession, and this is linked to behavioural economics and a safe haven effect.
Further, support is confirmed with various techniques for the FCF hypothesis in recession and
normal periods. In contrast, undervaluation is not supported by the results as a motivation for
higher CARs in either period.
2 Literature Review
2.1 What Happens When Firms Repurchase? - Empirical Observa-
tions
2.1.1 Short Run
In the short-run in the US, it is universally accepted that repurchase announcements are
rewarded by a significant increase in share price of around 4% (Allen and Michaely [2003],
Bargeron et al. [2012], Manconi et al. [2014], Rau [2002]). Furthermore, Bargeron et al. [2012]
show that suspending uncompleted programs is associated with abnormal returns of -1.35%.
4
6. 2.1.2 Long Run
Manconi et al. [2014] find that long-run abnormal returns around repurchase announcements
are positive, and related to the undervaluation index (See Peyer and Vermaelen [2009]), and
further that these are greater outside the US. The positive return is consistent with the findings
of Ikenberry et al. [1995] and Peyer and Vermaelen [2009] who show that these returns are in
the order of 30%.
2.2 Firm-Specific Characteristics
Bargeron et al. [2012] find that returns are greater with increased volatility and FCF. Manconi
et al. [2014] also find them positively related to corporate governance quality. Conversely, returns
are negatively related to market capitalisation and growth options. They also find that leverage
has no statistical significance in explaining abnormal returns.
2.3 Why Do Firms Repurchase? - Theoretical Explanations
2.3.1 Asymmetric Information, Signalling Models and Undervaluation
Ofer and Thakor [1987], Rees [1996], Rau [2002], Myers and Majluf [1984] and Grullon and
Michaely [2004] all use signalling as one explanation for repurchases. It is commonly accepted
that managers have more information about firms’ fundamentals than outside investors, and
that repurchases signal better prospects by revealing information about future earnings and
profitability to the market. Vermaelen [1981, 1984] links returns observed during repurchase
activity to insider shareholding and the size of the firm, supporting this signalling hypothesis.
Grullon and Ikenberry [2000], Manconi et al. [2014] and Bargeron et al. [2012] also suggest
that managers are expressing their disagreement with how the market is pricing their current
performance, and so repurchases signal to outside investors that managers believe the firm is
undervalued. Manconi et al. [2014] and Peyer and Vermaelen [2009] find that firms which expe-
rienced a larger drop in share price prior to an announcement experienced a higher cumulative
abnormal return, consistent with this undervaluation hypothesis; that managers time the market
and repurchase when the stock price is below its ‘true’ value.
Bhattacharya [1979a,b, 1980] shows, using a non-dissipative signalling model that can equally
be applied to repurchases, that dividends can lead to the advancement of the timing of infor-
mation transmittal from insiders to the outside market about a firm’s earnings prospects. In a
more generalised model, the link between dividends and share repurchases was made by Ofer
5
7. and Thakor [1987], who looked at the conditions under which a firm was inclined to initiate a
dividend payout, repurchase or both. However, these models do not provide quantitative pre-
dictions that can be tested via econometric techniques. This therefore leaves a gap for further
research, where it is clear that repurchases have an information content.
2.3.2 Agency Costs of Free Cash Flow
In his seminal paper, Jensen [1986] described the agency costs resulting from a firm having
large quantities of FCF, the cash above that which is needed to undertake all positive net present
value projects. Since managerial compensation is correlated with the size of a firm, managers
have incentives to grow firms sub-optimally by undertaking value-destroying projects. Further,
these costs are higher for organisations with “low growth prospects, and even more important
in organisations that must shrink”.
Grullon and Michaely [2004], Rau [2002] and Grullon and Ikenberry [2000] suggest that repur-
chases are one method that firms can use to reduce FCF and the associated agency costs, which
is subsequently rewarded in the market. Grullon and Michaely [2004] show that “repurchasing
firms significantly reduce their cash reserves over the 3 years following” an announcement. They
reinforce Jensen [1986] by showing that “profitability declines after this significant [repurchase]
payout” along with the cost of capital (which on average falls from 15.8% to 14.4%), consistent
with the fact that a firm’s investment opportunity set is contracting whilst moving from a high
growth phase to a low growth phase.
2.3.3 Other Motivations
Vermaelen [1981] concluded that managers who hold Executive Stock Options are more likely
to repurchase, as the positive stock price movement increases the value of these options. This
is consistent with Grullon and Ikenberry [2000], who also present dividend substitution as a
repurchase motivation, where repurchases are “more flexible” than dividends.
Grullon and Ikenberry [2000] and Rees [1996] further theorise that firms can use repurchases
to adjust their leverage ratios to increase the value of the tax shield.
6
8. 3 Data
3.1 Event Study Dataset
To undertake an event study, dates of repurchase authorisation announcements along with
firm returns and market returns were required. High frequency daily-data allows short-term
abnormal returns to be measured precisely, hugely increasing the power of any inferences drawn
from the results, as shown by Manconi et al. [2014]3
. The period of interest for this paper is
1st July 2004 to 30th June 2013, resulting in an equal split pre- and post-recession. Only firms
trading on the Nasdaq4
stock exchange were examined.
Authorisation Announcement Dates. Similarly to Bargeron et al. [2012], dates of repur-
chase announcements were acquired from Factiva. Initial searches resulted in 1,645 articles,
however obtaining valid dates required screening the content of each article against certain re-
quirements5
:
1. Announcements must concern to Open Market Repurchases. Tender Offers and Privately
Negotiated Repurchases were excluded.
2. Other press releases surrounding repurchases, namely: completions, extensions and can-
cellations cannot be used.
3. The press release cannot contain a confounding announcement, e.g. a change in dividend
policy or a change to firm governance.
4. Announcements can only be used if the corresponding returns data (see below) is available.
Filtering announcements on the above criteria resulted in a 510 announcements (see Appendix
Table 11 for the distribution over the period).
Returns Data. Corresponding adjusted daily stock price and composite index data was ob-
tained from Datastream and matched to announcements in the following steps:
1. Daily stock price for each of the current Nasdaq firms available on Datastream (1,972/3,058
firms) was obtained as well as the composite index.
3
Manconi et al. [2014] also provide useful guidance on cleaning data from Datastream.
4
The Nasdaq is an American stock exchange and is the second-largest in the world by market capitalisation.
5
See Appendix Section A for search details and examples of articles.
7
9. 2. Stock price and composite index values were converted into daily returns:
Rit,it−1 =
Pit − Pit−1
Pit−1
. (1)
3. Announcement dates were matched to this data and firms with no announcements removed.
3.2 Additional Data for Cross Sectional Inference
After carrying out the event study (see Section 4.1), a second, unique quarterly-sampled
dataset was created containing abnormal and cumulative abnormal returns from announcements
estimated from the event study dataset. Additionally, firm-specific characteristics were obtained
from Datastream, and these variables form the basis upon which inference can be drawn to test
this paper’s hypotheses.
These characteristics are: dividend yield, earnings per share, market value, share price, price-
earnings ratio, free cash flow, gearing (% debt), market-to-book ratio, return on equity, value of
shares repurchased and volatility of share price6
.
3.2.1 Undervaluation
Undervaluation is not a perfectly observable firm characteristic. However, this paper develops
a proxy for undervaluation based on return on equity (ROE) and market-to-book value (MTBV).
MTBV measures how the market prices a stock relative to the book or fundamental value of its
assets. Firms with a high ROE are expected to have a high MTBV, and vice-versa. Assuming
a linear relationship between ROE and MTBV, a proxy for undervaluation can be obtained
by predicting MTBV using the following OLS regression and examining the ratio between the
actual and predicted values:
MTBVi = α + β1ROEi + β2volatilityi + i (2)
Undervaluation =
MTBVi
MTBVi
(3)
As the ratio increases, the market is pricing the stock increasingly less than expected, based
on ROE, and it becomes increasingly undervalued.
6
Definitions of these variables can be found in Table 15 of the Appendix.
8
10. 3.3 Problems and Concerns
Data availability for a study of this nature was naturally a significant problem, e.g hav-
ing firm returns data for only 64% of Nasdaq stocks and missing values for many of the firm
characteristics. This limited sample size considerably.
Survivorship Bias is inherently present. Having data for current Nasdaq firms, firms which
filed for bankruptcy over the period 2004-2013 are excluded. Consequently, firm quality will be
upwardly biased which may impact the generalisability of results.
The proxy for undervaluation (see Section 3.2.1) is only a prediction based on empirical
observations. Subsequently, it is not likely to be equal to a true measure of undervaluation; if
undervalued stocks were easily identifiable, arbitrage traders would quickly act as market makers
to remove any pricing discrepancies.
Another possible issue is human error; reading 1,645 articles was a time-consuming task, and
not one which can be easily verified.
4 Methodology
4.1 Event Study
This paper will broadly adopt an event study methodology similar to MacKinlay [1997] as
follows:
Specify an Event Window. Define the event window, [τ = T1, τ = T2], where L2 = T2 − T1,
to be the period either side of the announcement date, τ = 0, over which anticipation and
reaction to the announcement takes place. This study uses 30 days either side of the event date:
[T1 = −30, T2 = +30].
Market Model for ‘Normal’ Returns. MacKinlay [1997] and Khotari and Warner [2006]
suggest using the one-factor market model, which assumes a stable linear relationship between
9
11. the market return, Rmt, and the security return, Rit for each firm:
Rit = αi + βiRmt + it. (4)
This follows from the assumed joint normality of asset returns:
E[ it = 0], var[ it] = σ2
i
. (5)
Estimation of the Market Model. The model is estimated over the estimation window,
[τ = T0, τ = T1], where L1 = T1 − T0, the period prior to the event window, using Ordinary
Least Squares (OLS) which is a consistent estimator under general conditions. This study uses
the 30-day period before the event window begins: [T0 = −60, T1 = −30]. For the ith
firm in
event time, the OLS estimators of the market model parameters for an estimation window of
observations are:
βi =
−30
τ=−60(Riτ − µi)(Rmτ − µm)
−30
τ=−60(Rmτ − µm)2
(6)
αi = µi − βiµm (7)
σ2
i
=
1
(−30) − (−60) − 2
L1
−30
τ=−60
(Riτ − αi − βiRmτ )2
(8)
where,
µi =
1
(−30) − (−60)
L1
−30
τ=−60
Riτ and µm =
1
(−30) − (−60)
L1
−30
τ=−60
Rmτ . (9)
The ‘statistical’ market model eliminates biases introduced by the sensitivity of economic
models such as the CAPM to their parameters (see MacKinlay [1997]). Multi-factor models (see
Fama [1998]) were also considered. However, data availability and the ease of implementation
did not warrant their adoption, where the ‘marginal explanatory power of additional factors
above the market factor is small’ (MacKinlay [1997]).
10
12. Statistical Properties of Abnormal Returns. Given the parameters estimated in the mar-
ket model above, define abnormal returns for firm i in the event window as:
ARiτ = Riτ − αi − βiRmτ , τ = −30, ..., +30. (10)
The abnormal return is therefore the disturbance term of the market model calculated on an
out of sample basis. Under the null hypothesis that the announcement has no impact on returns
(mean or variance), conditional on the event window market returns, the abnormal returns will
be jointly normally distributed with a zero conditional mean and conditional variance, σ2
(ARiτ ):
ARiτ ∼ N(0, σ2
ARiτ ) (11)
where:
σ2
(ARiτ ) = σ2
i
+
1
(−30) − (−60)
L1
1 +
(Rmτ − µm)2
σ2
m
. (12)
Aggregation of Abnormal Returns. To draw inferences, abnormal returns must be aggre-
gated through time and across securities. Through time for an individual security, define the
sample cumulative abnormal return, CARi(τ1,τ2) as the sum of the abnormal returns7
:
CARi(τ1,τ2) =
τ2
τ=τ1
ARiτ . (13)
Aggregating abnormal returns of individual securities8
from (10), define the average abnormal
return as:
ARτ =
1
N
N
i=1
ARiτ . (14)
These estimates allow the abnormal returns for any event period to be analysed.
Aggregating average abnormal returns over the event window, define the cumulative average
abnormal return as:
CAR(τ1,τ2) =
τ2
τ=τ1
ARτ . (15)
This paper aggregates average abnormal returns over 15 separate periods in order to capture
various aspects of the reaction and anticipation9
.
7
Under H0, the distribution of the cumulative abnormal return is: CARi(τ1,τ2) ∼ N(0, σ2
i(τ1,τ2)).
8
This aggregation assumes there is no clustering, that is, there is no overlap of event windows of the included
securities, and so raises a concern.
9
See Appendix Table 13 for details.
11
13. 4.2 Inference
4.2.1 Preliminary
Having estimated abnormal returns (ARs) and cumulative abnormal returns (CARs) and
aggregated versions of each: average abnormal returns (AARs) and cumulative average abnor-
mal returns (CAARs)10
, initial analysis can be undertaken across the event window and across
the sampling period (see Section 5.2 for results). Various significance tests will be conducted,
namely: the cross sectional t-test, the standardised residual test, the standardised cross-sectional
test and the generalised sign test (see Section 5.1 for results and Appendix Section C for test
specifications). These collectively aim to determine whether repurchase announcements result
in significant stock-price reactions.
4.2.2 Cross Sectional
To test my hypotheses, three main techniques will be used: OLS Regression, Propensity
Score Matching, and Multinomial Logit Regression.
Ordinary Least Squares. Firstly, three Ordinary Least Squares (OLS) regression specifica-
tions are estimated. These provide intuitive analysis, and are specified in the following forms:
• Model A: This includes all explanatory variables of interest:
car0 30i = α + β1undervaluationi + β2undervaluation2
i + β3ln(free cash flow)i +
β4recessioni + β5ln(market value)i + β6ln(volatility)i + β7ln(EP ratio)i +
β8ln(gearing)i + β9dividend yieldi +
(16)
• Model B: This specification omits insignificant variables that are not directly linked to
the hypotheses:
car0 30i = α + β1undervaluationi + β2undervaluation2
i + β3ln(free cash flow)i +
β4recessioni + β5ln(market value)i + β6ln(volatility)i +
(17)
• Model C: To perform Chow’s 1st
test for structural change between recession and normal
10
See Appendix Table 19 for definitions.
12
14. times, interaction terms11
are now included:
car0 30i = α + β1undervaluationi + β2undervaluation2
i + β3ln(free cash flow)i +
β4ln(market value)i + β5ln(volatility)i + δ1recession ∗ undervaluationi +
δ2recession ∗ undervaluation2
i + δ3recession ∗ ln(free cash flow)i +
δ4recession ∗ ln(market value)i + δ5recession ∗ ln(volatilty)i +
(18)
The above specifications will be unbiased, E[β] = β, subject to the standard OLS assump-
tions:
• Errors are mean zero: E[ ] = 0.
• Errors and regressors are uncorrelated: cov(X, ) = 0 or E[X ] = 0.
• A rank condition12
that all regressors must provide new information: rankE[X X] = k,
where k is the number of covariates.
For efficiency:
• Errors must be homoscedastic13
: var[ ] = σ2
.
• Errors must be serially uncorrelated: cov( i, j) = 0, i, j = 1, ..., N, i = j, ∼ N(0, σ2
).
Propensity Score Matching. However, there is reason to believe that there is selection
bias; firms repurchasing during recession have different characteristics than those repurchasing
in non-recessionary periods. There may exist non credible repurchases which are not supported
by fundamental firm characteristics.
Define Di ∈ {0, 1} as the treatment status for firm i:
• Define y1i as the potential outcome if firm i is treated i.e. repurchased during recession.
• Define y0i as the potential outcome if firm i is not treated i.e. repurchased during normal
times.
The parameter of interest is the average treatment effect on the treated (ATT), δ = E[y1i −
y0i|Di = 1]14
, that is, the expected average increase in one-month post-announcement cumulative
11
These allow the slope coefficients to vary between recessionary and non-recessionary periods. See Appendix
Table 24 for results.
12
This is equivalent to having no multicollinearity bias. See Appendix Table 17 for cross-correlations.
13
See Appendix Table 20 for Breusch-Pagan/Cook-Weisberg test.
14
This corresponds to β4 in Model A and B above.
13
15. abnormal returns (car0 30) from a repurchase that takes place in recession as opposed to in
normal times for recessionary firms, holding all else constant.
Table 1: The Evaluation Problem
Treated Not Treated
(recession) (non-recession)
Observed y1i y0i
Unobserved y0i y1i
a. yi = car0 30i.
There is an evaluation problem: we do not observe E[y0i|X, Di = 1] or E[y1i|X, Di = 0]15
.
A na¨ıve estimator, which is similar to OLS above, estimates the ATT as:
δ = E[y1i|X, Di = 1] − E[y0i|X, Di = 0]. (19)
This effectively assumes that E[y0i|X, Di = 0] is an appropriate counterfactual outcome
for E[y0i|X, Di = 1]. However, this assumption is violated if firm characteristics vary between
recessionary and non-recessionary periods. Adding and subtracting E[y0i|X, Di = 1] from the
na¨ıve ATT above (19):
E[y1i|X, Di = 1] − E[y0i|X, Di = 1]
Average Treatment Effect on the Treated (ATT)
+ E[y0i|X, Di = 1] − E[y0i|X, Di = 0]
Bias
(20)
The na¨ıve estimator will therefore only produce an unbiased estimator of the effect of recession
on CARs if:
E[y0i|X, Di = 1] − E[y0i|X, Di = 0] = 0, (21)
or equivalently:
E[Di i|X] = 0. (22)
The aforementioned selection bias leads to the violation of (22). This stems from different dis-
tributions of observables, that is, the fact that firm characteristics differ between treatment and
control groups, and so OLS effectively compares incomparable firms. Imbens and Rubin [2015]
provide normalised differences as a method of comparing characteristics between treatment and
15
Where X is a vector of firm characteristics.
14
16. control groups and suggest > 0.25 as a cause for concern:
(x1i − x0i)
s2
1i + s2
0i
, (23)
where xgi is the sample mean and sgi the sample standard deviation of covariate i for group
g = 0, 1. From Table 2 it is clear that there are indeed significant differences, and so the ATT
is likely biased.
Table 2: Normalised Differences Between Recessionary and Non-Recessionary
Repurchases
Variable E[Xi|Di = 0] E[Xi|Di = 1] Normalised Difference Concern
ln(market value) 7.310 6.781 -0.146 Medium
ln(free cash flow) 11.346 11.127 -0.047 Low
ln(volatility) 3.449 3.544 0.228 High
ln(undervaluation) 1.136 2.053 0.359 High
Propensity score matching reduces selection bias, by re-weighting the control group to look
like the treatment group. Each treated firm i is matched with a comparable non-treated firm.
The outcome of firm i is then compared to the weighted outcome of all units in this comparison
group, C0(pi):
y0i =
j∈C0(pi)
wijy0j, (24)
where:
C0(pi) = j : |pi − pj| = min
k (D=0)
[|pi − pj|] , wim =
1/k if m ∈ C0(pi)
0 otherwise
. (25)
Firms are matched based on the likelihood of participation or propensity score, the fitted
values from a binary response index model e.g. probit model:
Pr(recessioni = 1|X) = Φ(α + β1ln(market value)i + β2ln(free cash flow)i + β3ln(volatility)i +
β4undervaluationi + )
(26)
The main assumption is the Conditional Independence Assumption:
y0i ⊥ Di|p(Xi). (27)
15
17. This should hold based on propensity score, and hence will give an unbiased estimate of the
ATT. Different distributions of observables can be graphically demonstrated by looking at the
distributions of propensity scores (see Fig. 1).
Figure 1: Graphing the Distributions of Propensity Scores for Treatment and
Control Groups
Multinomial Logit Model. Multinomial logistical regression allows the hypotheses to be
examined in a broader sense; section 5.3 shows that variation in CARs is unpredictable, and
so a more general model could be useful. It is also attractive as it does not assume normality,
linearity or homoscedasticity, and its assumptions are likely satisfied (see Table 3). Specification
used:
ηij = log
πij
πiJ
log−odds
= αj + β1jln(free cash flow)i + β2jln(market value)i+
β3jln(volatility)i + β4jln(undervaluation)i + β5jrecessioni + ji,
(28)
for j = 1, 2, 3.
By creating a categorical variable for CARs based on quartiles (see Appendix Table 18), this
method looks at the relative probabilities of being in a particular quartile compared to the base
quartile (the lowest quartile).
16
18. Table 3: Multinomial Logistical Regression Assumptions
Assumption Satisfied Explanation
Independence amongst dependent Yes Perfectly independent since
variable choices CARs split into quartiles
Outcomes not perfectly separated Yes Explanatory variables do
by predictors not explain 100% of
variance in CARs
Independence of Irrelevant Not relevant Not modelling choices
Alternatives
5 Empirical Results
5.1 Significance Tests
Undertaking significance tests of CAARs (See Appendix Section C), H0 is rejected in almost
every case indicating that CAARs are statistically significant. It can be reasonably concluded
that an announcement of a repurchase has a significant impact on stock price.
Table 4: Significance Tests of Cumulative Average Abnormal Returns
Test H0 Test Statistic (p-value) Outcome
Overall Normal Recession
Cross-Sectional CAAR = 0 54.36 51.56 5.78 Reject
t-Test (0.000) (0.000) (0.0166) H0
Standardised Residual CAAR = 0 8.76 8.62 2.00 Reject
Test (0.000) (0.000) (0.027) H0
Standardised Cross- CAAR = 0 8.59 8.57 3.06 Reject
Sectional Test (0.000) (0.000) (0.002) H0
Generalised Sign Test CAAR = 0 7.49 7.40 -1.61 Reject H0 overall
(0.000) (0.000) (0.053) but not for recession
17
19. 5.2 Graphical Analysis
5.2.1 Over Event Window
Graphically analysing AARs over the event window (Fig. 2), it is apparent that over all
periods, firms experience on average, negative AARs before the announcement date and positive
AARs after.
Figure 2: Abnormal Returns Over the Event Window
Separating event window AARs between recessionary and non-recessionary periods (Fig. 3),
there is a striking disparity: recessionary repurchases experience much larger negative AARs
prior to an announcement, while volatility is also significantly greater.
Figure 3: Average Abnormal Returns Over the Event Window: Recession vs.
Normal
Plotting CAARs incrementally over the event window (Fig. 4) clarifies the trend. The pre-
announcement drop in share price for firms in recession is highlighted, and interestingly, post
18
20. announcement returns look similar.
Figure 4: Cumulative Average Abnormal Returns Over the Event Window
Plotting post-announcement CAARs incrementally over the event window (Fig. 5) shows
that these are very similar but potentially higher for recessionary repurchases. This finding goes
directly against hypothesis 1.
Figure 5: Cumulative Average Abnormal Returns Over the Event Window:
Post Announcement Only
5.2.2 Over Time: 2004 to 2013
Examining post announcement CARs over the sample period (Fig. 6), there is a clear
volatility increase during recession.
19
21. Figure 6: Post Announcement CARs From 2004 to 2013
Figure 7: Post Announcement CARs
vs. Free Cash Flow: Overall
Figure 8: Post Announcement CARs
vs. Free Cash Flow: Recession vs.
Normal
5.2.3 Free Cash Flow Hypothesis
Plotting post announcement CARs over free cash flow (Fig. 7), there is a clear positive
relationship between the two, supporting the FCF hypothesis. When separating recessionary
and normal repurchases (Fig. 8), this free cash flow hypothesis is supported in normal periods,
however there is a strange convex relationship in recession, going against hypothesis 2.
5.2.4 Undervaluation Hypothesis
Plotting post announcement CARs over undervaluation (Fig. 9), there is a positive rela-
tionship between the two. When separating recessionary and normal repurchases (Fig. 10), the
undervaluation hypothesis is not supported in normal periods. Undervaluation is only rewarded
at extreme levels in recession, where there again exists a strange convex relationship. This goes
against hypothesis 3.
20
22. Figure 9: Post Announcement CARs
vs. Undervaluation: Overall
Figure 10: Post Announcement
CARs vs. Undervaluation: Reces-
sion vs. Normal
5.3 OLS Regression Analysis
Table 5 shows estimates for the three robust16
OLS regression specifications laid out in
Section 4.2.2.
Results:
• The dummy variable recession is insignificant in Models A & B. The 1st Chow Test for
structural change on all slope coefficients in Model C fails to reject H0 with a p-value of
0.2839. That is, the interactions terms are all individually and jointly insignificant. This
suggests that recession has no impact on CARs.
• The FCF variable is positive and significant in all models, providing support for the FCF
hypothesis.
• For undervaluation, although mostly insignificant, undervaluation2
is positive and signifi-
cant in Model B, providing weak support for the undervaluation hypothesis.
5.4 Propensity Score Matching
However, as argued in depth in section 4.2.2, supported graphically by distributions of
propensity scores (Fig. 1) and statistically by calculating normalised differences (Table. 2),
there is likely to exist selection bias in the OLS models. Table 6 provides the results from
matching firms based on propensity scores calculated on the firm-specific characteristics market
value, free cash flow, volatility and undervaluation using 3 nearest neighbours with replacement17
.
16
See Appendix Tables 17, 20, 21, 22 and 23.
17
Firms matched to the 3 closest control-group firms where each control group firm can be used more than
once.
21
23. Table 5: OLS Regression Results on 30 Day Post-Announcement Cumulative
Abnormal Returns
Variable Model A Model B Model C
Coef. (Std. Err.) Coef. (Std. Err.) Coef. (Std. Err.)
Undervaluation 0.003 (-0.040) -0.035 (-0.022) -0.03 (-0.028)
Undervaluation2
-0.003 (-0.008) 0.006∗∗
(-0.002) 0.007 (-0.005)
ln(Free Cash Flow) 0.021∗
(-0.009) 0.02∗∗
(-0.007) 0.026∗∗
(-0.007)
Recession -0.043 (-0.051) -0.022 (-0.046) - -
ln(Market Value) -0.027∗
(-0.011) -0.022∗
(-0.009) -0.03∗∗
(-0.009)
ln(Volatility) 0.034 (-0.036) 0.037 (-0.033) 0.042 (-0.033)
ln(Earnings/Price) -0.006 (-0.022) - - - -
ln(Gearing) -0.032 (-0.043) - - - -
Dividend Yield -0.000 (-0.012) - - - -
Recession×Underval. - - - - -0.023 (-0.072)
Recession×Underval.2
- - - - 0.001 (-0.007)
Recession×ln(FCF) - - - - -0.028 (-0.029)
Recession×ln(MV) - - - - 0.048 (-0.038)
Recession×ln(VOL) - - - - -0.006 (-0.058)
Intercept 0.012 (-0.244) -0.119 (-0.134) -0.152 (-0.131)
R2
0.048 0.063 0.089
AIC -168 -201 -199
N 199 230 230
a. Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
b. Robustness checks: Correlations for multicollinearity, Ramsey RESET test for functional form, Breusch-Pagan test for
heteroscedasticity and skewness tests for normality of the error term: see Appendix Tables 17, 20, 21 and 22.
c. Robust standard errors are used.
Table 6: Propensity Score Matching Results
Variable Sample Treated Controls Difference
car0 30 Unmatched .0427 .0460 -.0032
ATT .0427 .0269 .0158
22
24. Results:
• ATT: The average expected CAR for a firm announcing a repurchase during recession is
1.58% higher compared to a firm repurchasing in normal times. This is is an interesting
and striking result, and goes against hypothesis 1.
Tables 7 & 8 show how unmatched firm characteristics differ significantly between the treat-
ment and control groups. Looking at the reductions in bias, is it clear that propensity score
matching has created an appropriate counterfactual outcome, improving upon the OLS results.
Table 7: Reductions in Bias: Firm Characteristics
Variable Unmatched Mean % Bias % Reduction T-test
Matched Treated Control |bias| p > |t|
ln(Market Value) U 6.78 7.31 -28.0 0.137
M 6.78 6.89 -5.7 79.6 0.828
ln(Free cash flow) U 11.13 11.35 -10.2 0.601
M 11.13 10.95 8.2 20.1 0.785
ln(Volatility) U 3.54 3.45 36.6 0.083
M 3.54 3.56 -6.7 81.8 0.788
Undervaluation U 2.05 1.14 55.9 0.000
M 2.05 1.80 15.7 72.0 0.601
Table 8: Reductions in Bias: Overall
Mean Bias U 32.7% M 9.1% Reduction |bias|: 72.2%
Median Bias U 32.3% M 7.4% Reduction |bias|: 77.1%
5.5 Multinomial Logit Analysis
Table 9 shows multinomial log-odds from the multinomial logistical model robustly specified
in section 4.2.2. However, interpretations cannot be made directly since the model non-linear;
Table 10 shows the marginal effects (at means).
Results:
• A recessionary compared to a normal repurchase is associated with a 21.2% point de-
crease in probability of being in a low returns relative to the lowest returns quartile. This
23
26. Table 10: Marginal Effects (only variables relevant to hypotheses - at means)
Variable dy/dx (Std. Err.)
Low Return: 2nd quartile
ln(Free Cash Flow) -0.034 (-0.023)
Undervaluation -0.021 (0.046)
Recession -0.212∗
(0.121)
Medium Return: 3rd quartile
ln(Free Cash Flow) 0.013 (0.023)
Undervaluation -0.002 (0.035)
Recession -0.095 (0.102)
High return: top quartile
ln(Free Cash Flow) 0.057∗∗
(0.026)
Undervaluation -0.002 (0.031)
Recession 0.154∗
(0.087)
a. Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
goes against hypothesis 1, and suggests recessionary repurchases are associated with lower
CARs.
• However, a recessionary compared to a normal repurchase is associated with a 15.4% point
increase in probability of being in the high returns relative to the lowest returns quartile.
This also contradicts hypothesis 1, but in the opposite direction.
• A 1% increase in free cash flow is associated with a 5.7% point increase in probability of
being in the high returns relative to the lowest returns quartile. This supports the free
cash flow hypothesis.
• No coefficients on undervaluation are significant, and so the undervaluation hypothesis is
not supported.
6 Discussion
In terms of announcement effects, this paper finds significant 30-day post-event CAARs in
both normal and recessionary periods of 4.9% and 4.3% respectively. Furthermore, these findings
are robust to event-induced variance increases and to the fact that the event-window ARs are
25
27. an out of sample prediction. Moreover, despite a large jump in share-price of around 1.37% on
the announcement date, ARs continue to be positive even after one month. Allen and Michaely
[2003], Bargeron et al. [2012], Manconi et al. [2014] and Rau [2002] all have similar findings in
normal periods, however this study extends previous empirical literature with the new finding
that CARs remain positive in recession.
Graphical analysis initially suggested that there is little difference in CARs between recession
and normal periods. Furthermore, OLS analysis found the recession dummy variable and Chow
test for structural change insignificant, indicating a lack of a recessionary effect on CARs. Based
on the rational expectations hypothesis, investors should not form expectations about the value
of a stock based on recession, only firm characteristics, supporting these findings, consistent with
hypothesis 1.
However after controlling for selection bias, propensity score matching found that recessionary
repurchases for firms with matched characteristics were rewarded with greater returns than non-
recessionary repurchases by 1.58%. This violates hypothesis 1, and interestingly can be reconciled
with Prospect Theory as proposed by Kahneman and Tversky [1979], where investors may have
lowered their reference point of firm quality in recession. Indeed, it appears there is a safe haven
effect, where investors move towards firms which have signalled their ability to remain strong in
recession. Multinomial logit analysis also provides evidence conflicting with hypothesis 1, where
recessionary repurchases are more likely to be in either the lowest quartile or highest quartile of
CARs. This suggests a bimodal distribution of recessionary repurchases, where investors appear
to be exaggerating positive and negative reactions. The safe haven effect again reconciles these
findings. Increases in volatility found in recessionary CARs also support this theory.
The FCF hypothesis is strongly supported in normal periods and OLS regressions do not
find this relationship contradicted during recession, consistent with hypothesis 2. The fact that
the FCF hypothesis still holds in recession is unsurprising and consistent with equity valuation
methods such as the discounted cash flow model proposed by Fisher [1930] and Williams [1938].
Over-investing by undertaking negative NPV projects erodes firm value and so foregoing this
possibility by paying out in the form of a repurchase generates a positive signal.
Finally, the undervaluation hypothesis is not supported by the results. Although weakly sup-
ported by overall graphical analysis, decomposing the relationships between normal and reces-
sionary periods finds no positive relationship between CARs and undervaluation. Furthermore,
undervaluation terms are on the whole insignificant in OLS and multinomial logit analysis.
Market timing theory does not explain why assets may be mis-priced in the first place, but
simply describes the behaviour of firms under the assumption that they can detect mis-pricing
26
28. better than markets can. Whether rational or behavioural, it explains that managers would
benefit from repurchasing when their shares are undervalued, as this results in a transfer of
wealth from outside investors to insiders, where the former do not realise they are giving up
their shares at a discount. The issue with a study of this nature therefore, is that it lacks
the inside data necessary to make judgements on whether a stock is undervalued. Insignificant
results likely stem from the difficulty in calling out a mis-priced stock. There are therefore
no conclusive results regarding hypothesis 3. These findings contrast with authors such as
Manconi et al. [2014] who used different methods to test the undervaluation hypothesis, namely
by computing the average EPS forecast in the six months prior to the repurchase announcement,
and subsequently obtained support for the undervaluation hypothesis in normal periods.
7 Conclusions
Main Results. This paper adopts an event study methodology and finds significant post-
announcement abnormal returns in non-recessionary periods to share repurchase authorisations,
consistent with past literature. Moreover, it extends the existing literature by confirming that
these persist in times of recession and further, that abnormal returns are higher for firms of
the same quality as opposed to appropriate counterfactual repurchases in normal periods. This
finding, along with the bimodal distribution of recessionary repurchases found using multinomial
logit techniques can be reconciled by hypothesising that in times of crisis, investors move towards
safe haven stocks, where a larger disparity between the CARs from good and bad quality firms
announcing repurchase authorisations ensues.
Robust OLS regression and Multinomial Logit analysis found the Free Cash Flow Hypothesis
stemming from agency cost theory supported in normal periods, and again this paper extends
previous empirical findings by suggesting it is also present in recession.
Limitations. The main limitation of this study was data availability. Not only was the sample
of repurchase announcements limited, but much matching firm specific data was missing; amount
of free cash flow was only available for 54% of firms. Further, potentially useful variables were
unusable, such as the percentage of equity repurchased, which was only available for 27% of firms.
With regards to hypothesis 3, the results regarding undervaluation are ambiguous and un-
convincing, but this likely stems from the difficulty in determining the relative undervaluation
of a stock.
27
29. Potential Extensions. This study focuses on short-run reactions to repurchase announce-
ments. Extending this to longer-term horizons would provide further insight into recessionary
repurchases, where the persistence of significant CARs could be tested.
Another area of interest is the fall in stock price before a repurchase announcement. Although
this is more likely a cause rather than a consequence of an announcement, robust methods should
be developed to explain the causes of the pre-announcement drop, where this was especially
pronounced in recession.
This paper found CARs from repurchase announcements to be more volatile during recession-
ary periods (monthly volatility increased significantly from 0.0169 to 0.0239), where investors
seemed to be exaggerating positive and negative reactions. This area could be researched further
by developing models to explain this volatility.
Implications for Firms. This paper finds that paying out to shareholders in the form of
a repurchase is generally rewarded positively in the market. However, this depends to a high
extent on the ‘quality’ of the firm in terms of its level of FCF. During recession, firms which
are of good quality should be encouraged to repurchase, as they have the possibility of being
rewarded to a greater extent than would be the case in non-recessionary periods, where they
may be viewed as a safe haven for investors.
28
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30
32. A Factiva Search Details and Announcement Example
Search terms and sources: Search terms for authorisation announcements: “(share or shares
or equity or stock) and (repur* or buyback* or buy-back* or buy* back) and (NASDAQ) NEAR5
(annou*)”. Sources: “Publication: Business Wire or Publication: PR Newswire (U.S)”. Region:
“U.S”. Subject: “Share Buyback”. Dates: “01/07/04” to “30/06/13”.
Authorisation announcement example: Excerpt from January 31, 2005 Business Wire
article “Direct General Corporation Announces $20 Million Share Repurchase”:
NASHVILLE, Tenn. - (BUSINESS WIRE) - Jan. 31, 2005 - Direct General Cor-
poration (Nasdaq: DRCT) today announced that its Board of Directors approved
the repurchase of up to $20 million of its outstanding common stock. The shares
may be repurchased in accordance with Rule 10b-18 under the Securities Exchange
Act of 1934 and is expected to commence after February 11, 2005 and continue over
the next 12 months. At its earliest opportunity, the Company intends to adopt a
formal 10b5-1 purchase plan to implement the repurchase program. The Company
expects to develop the purchase plan considering a variety of factors, including po-
tential stock acquisition price, cash requirements, acquisition opportunities, strategic
investments and other market and economic factors.
B Additional Tables
Table 11: Frequency of OMR Authorisation Announcements by Year
Year Authorisations
(Including Outliers) (Excluding Outliers)
2004 5 3
2005 41 32
2006 57 49
2007 111 96
2008 85 75
2009 33 29
2010 59 52
2011 44 39
2012 51 45
2013 24 19
Total 510 439
a. Years 2004 & 2013 are only half years.
b. This table shows the frequency of share repurchase announcements over time for the sample of announcements used in this
study. It is clear that repurchases were becoming more popular up to 2008, and fell dramatically after the financial crisis.
31
33. Table 12: Event Window Abnormal and Cumulative Abnormal Returns Using
The Market Model
Event Overall Normal Recession
Day xxxARxxx xxxCARxxx xxxARxxx xxxCARxxx xxxARxxx xxxCARxxx
-30 .002 .002 .001 .001 .007 .007
-29 .000 .002 .000 .001 .003 .010
-28 -.003 -.001 -.002 -.001 -.005 .005
-27 .001 .000 .002 .000 -.007 -.002
-26 -.003 -.003 -.002 -.001 -.016 -.018
-25 -.002 -.005 -.002 -.004 .003 -.015
-24 .001 -.004 .001 -.003 .003 -.012
-23 -.002 -.005 -.001 -.004 -.007 -.019
-22 .000 -.005 .000 -.004 .000 -.019
-21 -.001 -.006 .000 -.004 -.007 -.026
-20 -.002 -.008 -.001 -.005 -.010 -.036
-19 .001 -.007 .001 -.004 .001 -.035
-18 -.002 -.010 -.002 -.006 -.006 -.042
-17 .001 -.008 .001 -.005 .002 -.039
-16 -.001 -.009 .000 -.005 -.006 -.045
-15 -.001 -.010 -.001 -.006 .005 -.040
-14 -.003 -.012 -.002 -.008 -.005 -.045
-13 .000 -.013 .000 -.008 -.002 -.048
-12 -.003 -.015 -.003 -.011 -.004 -.051
-11 -.002 -.017 -.003 -.014 .003 -.048
-10 -.002 -.019 -.001 -.014 -.012 -.060
-9 .000 -.019 .001 -.014 -.003 -.063
-8 -.001 -.020 -.001 -.014 .000 -.063
-7 -.001 -.021 .000 -.015 -.009 -.072
-6 .002 -.019 .002 -.013 -.002 -.074
-5 -.002 -.021 -.001 -.014 -.008 -.082
-4 -.003 -.024 -.003 -.017 -.003 -.086
-3 -.002 -.027 -.003 -.020 .001 -.084
-2 -.003 -.030 -.002 -.021 -.017 -.101
-1 .002 -.028 .002 -.019 .000 -.101
0 .013 -.016 .012 -.007 .018 -.083
1 .012 -.004 .011 .004 .014 -.069
2 -.001 -.005 .000 .004 -.004 -.073
3 .004 -.001 .004 .008 .006 -.068
4 .004 .004 .003 .011 .008 -.060
5 .000 .003 .001 .012 -.010 -.070
6 .004 .007 .002 .015 .014 -.056
7 .002 .009 .001 .016 .006 -.049
8 .002 .011 .002 .018 .003 -.046
9 .002 .013 .004 .021 -.007 -.053
10 .002 .015 .001 .022 .009 -.044
11 .003 .018 .003 .024 .001 -.043
12 .002 .019 .002 .027 -.003 -.046
13 .000 .020 .001 .028 -.004 -.050
14 .000 .020 .001 .029 -.005 -.056
15 .003 .023 .002 .031 .007 -.049
16 .001 .024 .001 .032 .002 -.047
17 .000 .024 .000 .032 -.002 -.049
18 .002 .025 .002 .034 .001 -.048
19 .003 .028 .002 .036 .011 -.037
20 .001 .029 .002 .038 -.003 -.039
21 .003 .032 .002 .040 .006 -.034
22 .003 .035 .003 .044 .000 -.034
23 .001 .036 .001 .044 .002 -.032
24 .000 .036 -.001 .043 .002 -.030
25 .000 .035 .001 .044 -.010 -.040
26 .003 .038 .002 .046 .008 -.031
27 -.002 .036 .000 .046 -.011 -.042
28 .001 .037 .000 .046 .006 -.036
29 .000 .038 .000 .046 -.001 -.037
30 .001 .039 .002 .049 -.006 -.043
a. This table gives the abnormal returns for an event study of the information content of share repurchase authorisation
announcements. The market model is used as the model for normal returns using the Nasdaq composite index as the market
return. AR is the sample average abnormal return for the specified day in event time and CAR is the sample average cumulative
abnormal return for day -30 to the specified day. Event time is in days relative to the announcement date. ARs and CARs are
shown for the whole sample (overall) and are then shown calculated separately for normal and recessionary periods.
32
34. Table 13: Mean CARs Between Recession and Normal Periods Over Different
Event Windows
Variable Event CAAR
Window Normal Recession
Pre- and post-announcement
car3 3 ±3 days 2.30% 2.61%
car5 5 ±5 days 2.29% 2.20%
car10 10 ±10 days 2.98% 4.23%
car15 15 ±15 days 3.10% 3.86%
car30 30 ±30 days 4.09% 2.70%
Pre-announcement
car3 0 −3 to 0 days 1.10% 1.33%
car5 0 −5 to 0 days 0.82% 0.94%
car10 0 −10 to 0 days 0.92% -0.17%
car15 0 −15 to 0 days 0.30% -0.21%
car30 0 −30 to 0 days 0.05% -2.23%
Post-announcement
car0 3 0 to +3 days 2.42% 3.58%
car0 5 0 to +5 days 2.70% 3.56%
car0 10 0 to +10 days 3.28% 6.70%
car0 15 0 to +15 days 4.03% 6.37%
car0 30 0 to +30 days 5.26% 7.23%
a. This table shows the different event windows over which cumulative abnormal returns were calculated in this study and gives
the sample average CAR calculated over normal and recessionary periods.
b. It is clear that post-announcement CARs are higher than pre-announcement CARs, as expected. Further, recessionary
post-announcement returns are larger than normal period post-announcement returns.
c. The main event window used in this paper is the post-announcement CAR: 0 to +30 days, car0 30.
Table 14: Summary Statistics
Variable Obs Mean Std. Dev. Min Max
CARs over event window -0 to +30 days 439 .055 .157 -.667 .635
Dividend Yield (%) 439 .913 1.407 0 8.16
Earnings per share 439 1.159 1.329 0 10.44
Market Value (millions of $s) 439 3436.369 7888.576 7.23 62449.95
Share Price ($s) 439 22.55 14.828 .75 94.8
Free Cash Flow ($s) 235 383958.9 686187.8 78 3847500
% Debt (Gearing) 439 75.977 24.368 6.62 100
Market-to-Book Ratio 439 2.705 1.867 .22 10.62
Return on Equity (%) 439 10.609 11.491 -30.27 47.55
Shares Repurchased 120 5.80e+08 1.04e+09 0 5.83e+09
Volatility of share price 422 30.826 9.863 13.5 62.8
Undervaluation 422 1.4 1.007 .009 11.15
Recession dummy based on Nasdaq 439 .132 .339 0 1
a. This table shows the number of observations, mean, standard deviation and range of values for the main variables of interest
in this paper. Definitions of these variables are given in Table 15.
b. 510−439 = 71 outliers have been removed from this data to give these statistics, as they are then representative characteristics
of the variables actually used in this paper.
33
35. Table 15: Definitions of Variables
Abbreviation Variable (Source) Definition
id Assigned ID (G) Unique identifier for announcement observation
date Announcement Date (F) Day on which repurchase authorisation was announced
publicly
company id Nasdaq ticker symbol (F) Symbol corresponding to the specific firm
p Daily stock return (C) Daily stock return from τ = t − 1 to τ = t
ret Daily market return (C) Daily return on Nasdaq composite index
(market return)
dif Relative Date (G) Relative date to announcement date i.e 5 days before
announcement, dif = −5
event window Event Window (G) Window over which anticipation and reaction to the
announcement takes place: dif = −30 to dif = +30
estimation Estimation Window (G) Window over which normal returns are calculated
window using the market model: dif = −60 to dif = −30
predicted Predicted Return (C) Return predicted over the event window using the
return market model
recessionS Recession (G) Dummy variable equal to one between the dates
1st August 2008 to the 1st January 2010
UNDreg Undervaluation (C) Measure of undervaluation as detailed in Section 3.2.1
pscore Propensity Score (E) Predicted probability of being a recessionary as
opposed to a normal repurchase (treatment group)
DY Dividend Yield (D) Dividend expressed as a percentage of current share
price
EPS Earnings Per Share (D) Net income earned per share of stock outstanding
MV Market Value (D) Total dollar market value of a company’s outstanding
shares
P Share Price (D) Price of a single share of a number of saleable stocks
of a company
PE Price-Earnings Ratio (D) Ratio for valuing a company that measures its current
share price relative to its earnings per share
FCF Free Cash Flow (D) Cash flow in excess of that required to fund all pos-
itive NPV projects
DE Gearing (% Debt) (D) A company’s financial leverage, calculated by dividing
a company’s total liabilities by its stockholders’ equity
MBTV Market-to-Book Ratio (D) The market value of a company relative to its book or
accounting value
ROE Return-on-Equity (D) Net income returned as a percentage of shareholders’
equity
PSOUGHT Shares Repurchased (D) Market value of shares repurchased by the firm in
the last year
VOL Share Price Volatility (D) Stock’s average annual price movement to a high and
low from a mean price for each year
a. This table shows definitions of the main variables of interest in this study, along with the abbreviations used throughout the
paper and the source of each variable.
b. This does not include definitions of all variables. For cumulative abnormal returns variables see Appendix Table 13 and for
definitions of all estimated returns variables see Appendix Table 19.
c. Sources: C = Calculated, D = Datastream, E = Estimated, F = Factiva, G = Generated.
34
38. Table 19: Definitions of Abnormal Returns Variables
Variable Definition
Abnormal Return Difference between predicted return and actual return
(AR) for a given firm on a given day in the event window.
Average Abnormal Return Abnormal Return aggregated across all firms, 1, ..., N
(CAR) on a given day in the event window.
Cumulative Abnormal Return The sum of abnormal returns for a given firm over the
(AAR) event window.
Cumulative Average Abnormal Return Cumulative Abnormal Return aggregated across all
(CAAR) firms, 1, ..., N.
a. This table shows definitions of the main estimated variables in this study: abnormal returns, sample average abnormal
returns, cumulative abnormal returns, and sample average cumulative abnormal returns. These are estimated as described in
Section 4.1.
Table 20: OLS Robustness Check: Breusch-Pagan / Cook-Weisberg Test for
Heteroskedasticity
H0: var[ ] = σ2, errors homoscedastic
H1: var[ ] = σ2, errors heteroscedastic
Chi2 Statistic P-value Result
Model A 32.01 0.0002 R
Model B 36.25 0.0000 R
Model C 36.65 0.0001 R
a. This table shows the results from a Breusch-Pagan / Cook-Weisberg Test for heteroskedasticity. It strongly suggests that
Model A, B and C all have heteroscedastic errors. That is, the OLS assumptions are violated. Consequently, heteroscedastic-
robust standard errors are adopted for each of these models in this paper.
c. R = Reject H0, DNR = Do Not Reject H0.
37
39. Table 21: OLS Robustness Check: Ramsey RESET Test Using Powers of the
Fitted Values of car0 30
H0: model has no omitted variables
H1: model has omitted variables
F Statistic P-value Result
Model A 2.68 0.0485 R
Model B 2.67 0.0484 R
Model C 1.90 0.1310 DNR
a. This table shows the results from a Ramsey RESET test using powers of the fitted values of car0 30. It weakly suggests
there are omitted variables in Models A and B, however that there are no omitted variables in Model C. That is, there may be
some violation of the OLS assumptions for Models A and B, and so these are less robust specifications than Model C in terms
of inference drawn from them.
b. R = Reject H0, DNR = Do Not Reject H0.
Table 22: OLS Robustness Check: Skewness/Kurtosis Tests for Normality of
Residuals
H0: residuals normally distributed
H1: residuals not normally distributed
Obs Pr(Skewness) Pr(Kurtosis) adj chi2 Prob.>chi2 Result
Model A 199 0.2216 0.0000 17.3 0.0002 R
Model B 230 0.1077 0.0000 20.9 0.0000 R
Model C 230 0.0884 0.0000 21.1 0.0000 R
a. This table shows the results from a skewness and kurtosis test for normality where both tests are then combined into an overall
test statistic. They suggest that the residuals in Model A, B and C are non-normal. This implies that there are characteristics
which have not been captured in the current model specifications. However, this is to be expected since many potential variables
are unavailable.
b. R = Reject H0, DNR = Do Not Reject H0.
Table 23: OLS Robustness Check: Akaike’s information criterion and Bayesian
information criterion
Obs ll(null) ll(model) df AIC BIC
Model A 199 88.9 93.8 10 -167.60 -134.7
Model B 230 99.9 107.4 7 -200.8 -176.7
Model C 230 99.9 110.6 11 -199.2 -161.4
a. This table shows the values of the Akaike information criterion and Bayesian information criterion for Models A, B and C.
Comparing Models B and C, Model B is preferred to Model C by both criterion. This is likely due to the fact that the interaction
terms added between the explanatory variables and the recession dummy are insignificant, and so Model C is penalised (especially
by the BIC which puts more weight on penalising parameters). Model C is therefore preferred to A and B.
b. However, Model C is still necessary to perform Chow’s 1st test to evaluate hypothesis 1.
38
40. Table 24: Chow’s 1st
Test for Structural Change Between Normal and Reces-
sionary Periods: OLS Model C
Null Hypothesis:
(1) Recession × Undervaluation = 0
(2) Recession × Undervaluation2 = 0
(3) Recession × ln(FreeCashFlow) = 0
(4) Recession × ln(MarketV alue) = 0
(5) Recession × ln(V olatility) = 0
F(5, 219) = 1.26
Prob > F = 0.2839
Result: Do not reject H0
a. Chow’s 1st test for structural change corresponds to an F-test of the joint significance of all interaction terms between a
dummy variable and the other covariates of interest. In this case, it is testing whether the relationships between each of the
covariates and cumulative abnormal returns varies between normal and recessionary periods. The result suggests that recession
has no impact on these relationships, as it does not reject the null hypothesis.
C Significance Test Specifications
Cross-Sectional t-Test.
Tcross =
CAAR(τ1,τ2)
σCAAR(τ1,τ2)
, (29)
under H0 that the CAAR = 0.
Brown and Warner [1980] show that the cross-sectional t-test is robust to an event-induced
variance increase. Boehmer et al. [1991], however, argue that the standardised cross-sectional
test (see below) is more powerful.
Standardised Residual Test. Patell [1976] assumes that ARs are uncorrelated and variance
is constant over time. Each abnormal return is standardised by its estimated standard deviation,
which is adjusted to account for the fact that the event-window abnormal returns are an out-of-
sample prediction.
Standardised abnormal return:
SARiτ =
ARiτ
S(ARi)
, (30)
Cumulative standardised abnormal returns:
CSARi(τ1,τ2) =
T2
τ=T−1
ARiτ
S(ARi)
(31)
39
41. Test statistic:
TPatell =
1
√
N
N
i=1
CSARi(τ1,τ2)
S(CSARi)
, (32)
under H0 that the CAAR = 0.
Standardised Cross-Sectional Test. Boehmer et al. [1991] developed a test that is robust
to event-induced variance increases of stock returns by combining the standardised residuals
test with an empirical variance estimate based on the cross-section of event window abnormal
returns. Abnormal returns are standardised as above and then the average is taken:
CSAR(τ1, τ2) =
1
N
N
i=1
CSARi(τ1,τ2) (33)
Test statistic:
TBoehmer =
CSAR(τ1, τ2)
S(CSAR)
, (34)
under H0 that the CAAR = 0.
Generalised Sign Test. Cowan [1992] suggests a generalised sign test based on the ratio of
positive abnormal returns p+
0 over the event window. Under H0, the ratio should not deviate
from the ratio of positive abnormal returns over the estimation window p+
Est..
Test statistic:
tGS =
p+
0 − p+
Est.
p+
Est.(1 − p+
Est.)/N
. (35)
40
![Repurchases and Recession: Did the 2008
financial crisis change how markets perceive
repurchase signals?
Cameron Melville∗
Department of Economics, Warwick University
Research in Applied Economics
Abstract
This paper adopts an event study methodology to estimate the abnormal returns to
share repurchase authorisation announcements during normal and recessionary periods.
It investigates the impact of recession on how investors respond to repurchase signals,
with an emphasis on two popular hypotheses cited in the literature: Jensen’s [1986] Free
Cash Flow Hypothesis, and the Undervaluation hypothesis. The study utilises a unique
dataset of Nasdaq repurchase announcements along with firm-specific characteristics from
2004 to 2013, implementing three estimation techniques: OLS regression, propensity score
matching, and multinomial logistic regression. Similarly to past empirical results, this
paper finds positive post-announcement cumulative abnormal returns of 4.9% and 4.3% for
normal and recessionary periods respectively, and extends previous findings by suggesting
that firms matched on fundamental characteristics are rewarded to a greater extent in
recessionary periods by 1.6%. This is reconciled with the hypothesis that investors move
towards safe haven stocks in times of crisis. Furthermore, support is confirmed for the Free
Cash Flow hypothesis in normal periods. Moreover, new findings are presented suggesting
that this relationship also holds during recessionary periods.
Keywords: Share Repurchases; Payout Policy; Signalling; Asymmetric Information; Recession.
JEL classification: G32, G35, D82, D83, E32.
Words: 5,100 including Footnotes and Tables.
∗
Many thanks to Alexander Karalis Isaac for his invaluable guidance and feedback throughout this project.
1](https://image.slidesharecdn.com/6c172093-0f8c-4f8a-811f-5a2ba60c0c1c-160630164925/85/ec331a2-1-320.jpg)
