1. No category

Bessembinder / Zhang (2013): Firm characteristics and
long-run stock returns after corporate events
Discussion by Henrik Moser
April 24, 2015
Motivation of the paper
Authors review the connection of corporate events and
long-run stock returns
Previous studies commonly used two approaches to measure
long-run abnormal returns following corporate events
I
I
Calendar time portfolio
Buy-and-hold abnormal return (BHAR)
However, BHARs often show large abnormal returns, while the
calendar time method does not
I Authors try to reconcile both approaches
Four types of corporate events
I
I
I
I
IPOs
SEOs
merger biddings
dividend initiations
3
Related literature
Figure 1: Selection of related studies.
4
Calendar time portfolio
Focuses on mean abnormal returns to portfolios of event firms
(calculated using the Fama-French Three-factor model)
Rpt − Rft = αi + βi (Rmt − Rft ) + si SMBt + hi HMLt + it
Advantages
I
Method eliminates the problem of cross-sectional dependence
among sample firms because the returns on sample firms are
aggregated into a single portfolio1
Disadvantages
I
I
I
Approach is misspecified in non-random samples
Method is subject to rebalancing bias
Approach has low power to detect abnormal returns, because it
effectively weights each period equally, while corporate events
tend to cluster in certain time periods
1
Lyon, John D., Brad M. Barber, and Chih-Ling Tsai (1999): Improved Methods for Tests of
Long-Run Abnormal Stock Returns, The Journal of Finance 54 (1): 165-201.
5
Buy-and-hold abnormal return (BHAR)
Based on the difference between buy-and-hold returns to event
firms (e) as compared to matched firms (m) after event date
t=0
BHAReT =
T
Y
(1 + ret ) −
t=1
= exp
T
Y
(1 + rmt )
t=1
"
T
X
t=1
6
#
ln(1 + ret ) − exp
"
T
X
#
ln(1 + rmt )
t=1
Alternative (but equivalent) approach: Wealth relative
" T
#
QT
h
i
X
(1
−
r
)
et
WReT = QTt=1
= exp
ln(1 + ret ) − ln(1 + rmt )
(1
−
r
mt )
t=1
t=1
Evaluation of BHARs
Advantages
I
I
Free of rebalancing bias
Resembles investors’ actual experience, as opposed to periodic
rebalancing required by other methods
Disadvantages
I
I
I
Matching based on particular firm characteristics, such as size
or book-to-market ratio
Ignoring other firm characteristics, such as, e.g., liquidity, beta,
momentum, and capital investment
Matching is typically done at a particular point in time, while
the matching criteria might actually move apart over longer
time periods
7
Difference between sample firms and their matches
Median size
Median BM
0.9
0.55
600
500
0.5
BM
Size
Beta
0.8
400
0.7
0.45
300
200
Month
Match
Bidder
Median momentum
0.1
0.05
0
.06
0.36
.05
0.32
.03
.01
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
4
-1
8
-1
2
-6
0
6
12
18
24
30
36
42
48
54
60
54
60
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
4
-1
8
-1
2
-6
0
6
12
18
24
30
36
42
Month
.04
.02
0.3
0.28
48
Median illiquidity
0.38
0.34
Month
Match
Match
Bidder
Illiquidity
Idiosyncratic volatility
0.2
Momentum
Month
Match
Median idiosyncratic volatility
0.15
Bidder
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
4
-1
8
-1
2
-6
0
6
12
18
24
30
36
42
48
54
60
Month
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
-14
8
-1
2
-6
Bidder
0.4
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
4
-1
8
-1
2
-6
0
6
12
18
24
30
36
42
48
54
60
0
6
12
18
24
30
36
42
48
54
60
-6
0
-5
4
-4
8
-4
2
-3
6
-3
0
-2
4
-1
8
-1
2
-6
0.6
Bidder
0
6
12
18
24
30
36
42
48
54
60
Median beta
8
Month
Match
Figure 2: Characteristics of bidding firms and their size- and
book-to-market matched comparable firms.
Bidder
Match
Sample selection
U.S. public companies from Thomson Financial’s SDC
database (IPO, SEO, and M&A samples) or CRSP (dividend
sample)
Time span from 1980 to 2005, allowing for a five-year period
to measure post-event stock returns
Excluding certain categories of events (e.g., issue of depository
receipts, minority and non-control mergers)
Special filters of the merger sample
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I
Transaction size (in terms of market value of the bidder)
greater than 5%
Transaction value greater than $5m
9
Matching samples
SEO, M&A, and dividend samples
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Matched firm is the company with the closest book-to-market
ratio among firms with market capitalization between 70% and
130% at the end of year t − 1
Match must not be in original sample during then ten years
around the event
IPO sample
I
I
Matched firm is the company with the closest but greater
market capitalization at the end of December following the IPO
Match must have been publicly traded for more than five years
10
Sample
11
Table 2
Number of event rms.
This table presents the number of mergers and acquisitions (M&As),
seasoned equity offerings (SEOs), initial public offerings (IPOs), and
dividend initiations in our sample, by year.
Year
M&A
SEO
IPO
Dividend
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total
1
9
1
0
5
73
98
91
90
101
66
90
106
142
219
283
351
325
409
336
288
205
151
168
187
177
3,972
184
186
210
500
94
152
189
115
55
103
82
240
188
261
171
256
332
265
198
205
209
163
171
196
240
166
5,131
99
263
94
588
274
265
570
443
222
194
178
367
536
692
508
511
795
535
344
498
364
93
79
67
199
188
8,966
25
26
13
17
24
20
29
24
47
46
40
32
28
30
36
53
22
29
15
24
17
18
26
119
70
57
887
Figure 3: Sample sizes by year.
Variables
Market beta
I
Firm size
I
Market capitalization
Book-to-market ratio
I
Estimated using the market model with monthly stock returns
during years t − 5 to t + 5
Ratio of the book value to the market value of common equity
Momentum
I
Cumulative return over months −12 to −2
12
Variables (cont’d)
Illiquidity
I
Idiosyncratic volatility
I
Average ratio of daily absolute stock return to dollar trading
volume, relative to market average illiquidity during the same
period
Annualized standard deviation of the residuals of a
Fama/French three factor regression implemented in daily
returns in month −2
Investment
I
Annual change in gross PPE plus annual change in inventory,
divided by assets
13
Regression approach
14
ln(1 + ret ) − ln(1 + rmt ) = α ∗ β1 ∆Betaet + β2 ∆Sizeet + β3 ∆BMet
+ β4 ∆Momet + β5 ∆Illiquidityet
+ β6 ∆IdioVolet + β7 ∆Investmentet + et
where ∆ denotes the normalized difference in the associated firm
characteristics across the event firm and the matching firm (in
order to make coefficients comparable across characteristics)
General idea
In any regression, the intercept measures the mean of the
dependent variable, conditional on outcomes of zero for each
independent variable
In this particular case, the intercept estimates the mean
abnormal log return to event firms, conditional on no
difference in firm characteristics across event and control firms
Testing the hypothesis that the intercept is zero is equivalent
to testing whether the BHAR is zero
15
Regression output: SEO sample
Figure 4: Regression outputs for the SEO sample.
16
Regression output: IPO sample
Figure 5: Regression outputs for the IPO sample.
17
Comparison to calendar time portfolio
Figure 6: Regression outputs for the calendar time portfolio method.
Outcomes of the calendar time portfolio method show no
abnormal returns (α insignificant)
No conflict of BHARs and calendar time portfolios found (in
this sample)
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Concluding remarks
Many previous studies have found significant abnormal returns
The abnormal returns are attributed to the events themselves
There might be a different explanation of the abnormal returns
rooted in known return regularities
The authors show that typical matching algorithms are
imperfect in addressing a number of company characteristics
Variation in the characteristics completely explains abnormal
returns
Results reconcile confounding results of BHAR and calendar
time portfolio studies
Apparently abnormal returns reflect characteristics of the firms
rather than event-specific effects
19