Essays on Corporate Governance and Investor
Disagreement
by
Tara Kumari Bhandari
B.S., Economics, University of Pennsylvania (2002)
B.A.S., Telecommunications, University of Pennsylvania (2002)
Submitted to the Alfred P. Sloan School of Management
in partial fulfillment of the requirements for the degree of
ARM
MA.SACHUSmTS INSTMUDt
Doctor of Philosophy
OF TECHNOLOGY
at the
DEC 0 3 2013
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
LIBRARIES
September 2013
@ Tara Kumari Bhandari, MMXIII. All rights reserved.
The author hereby grants to MIT permission to reproduce and to
distribute publicly paper and electronic copies of this thesis document
in whole or in part in any medium now known or hereafter created.
A u th or ..............
...........
....... / ...... ,.....................
lfred P. Sloan School of Management
-
August 7, 2013
...................
Antoinette Schoar
Michael Koernr '49 Professor of Entrepreneurial Finance
Thesis Supervisor
C ertified by ......
Accepted by ..... .
Ezra W. Zuckerman
Director, Ph.D. Program, Sloan School of Management
2
Essays on Corporate Governance and Investor Disagreement
by
Tara Kumari Bhandari
Submitted to the Alfred P. Sloan School of Management
on August 7, 2013, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Abstract
This thesis consists of two essays examining the roles of corporate governance and
investor disagreement, respectively, in the performance and stock market valuations
of firms. In the first chapter, I demonstrate that the relationship between corporate
governance and firm performance varies with industry performance cycles. Firms
with strong shareholder rights capture higher profits than poorly-governed firms in
the same industry during highly profitable periods for the industry, but both groups
have similar profits during weaker industry conditions. Analyst forecasts indicate
that the pattern is expected, suggesting that the higher valuations of well-governed
firms are due to this higher expected productivity in good times. Consistent with
such expectations and with an updating of valuations as anticipated industry conditions change, positive abnormal stock returns to good governance are concentrated
in periods of high industry returns, and are at least partially reversed during industry downturns. My results provide an alternative to learning and static risk theories
in explaining the apparent abnormal returns to governance and their disappearance
after 2001.
In Chapter 2, I consider the impact of heterogeneous shareholder beliefs on stock
prices, focusing on the context of corporate spin-offs and mergers. I extend theoretical work by Miller (1977) and Jarrow (1980) to show that when investors disagree
about the prospects of different businesses and at least some of them are restricted
from short-selling, the market price of an unseparable bundle of two enterprises will
often be lower from the sum of the prices at which they would trade as standalone
entities. Empirically, I construct a novel measure of observed disagreement that is
informed by the theory and is less open to alternative interpretations than existing
disagreement proxies. Consistent with the theory, I find that higher disagreement
about the two components being either separated or joined is related to a positive
return in the case of spin-offs and a negative return in stock mergers. Importantly,
since I focus on returns on the ex date of these transactions, on which no new business
information is released, these findings are unrelated to the expected business impact
of the transactions.
3
Thesis Supervisor: Antoinette Schoar
Title: Michael Koerner '49 Professor of Entrepreneurial Finance
4
Acknowledgments
I am very grateful to my advisors Antoinette Schoar, Stewart C. Myers and Xavier
Giroud for their guidance. I thank all of my colleagues, particularly Manuel Adelino,
Jack Bao, William Mullins, Felipe Severino, Yang Sun, Mary Tian, and Jialan Wang
for their friendship and many stimulating discussions.
I have also benefited from
advice and comments from many other members of the Sloan faculty in finance,
and Sharon Cayley, Hillary Ross, and Svetlana Sussman's contributions in organizing
various administrative matters were crucial. In addition, I would like to thank the
Sloan finance department and all of my doctors at MIT, Mount Auburn, and MGH
for their help and support during my medical leave from the program.
Finally, I want to acknowledge the love, inspiration and constant support I received
from all of my family and friends, especially my parents Laxmi Chand and Basanti
Bhandari, who were my first teachers, are my most steadfast supporters, and are my
greatest inspiration.
5
6
Contents
1
Making the Most of Good Times: Shareholder Rights and Performance Revisited
1.1
1.2
1.3
Data and Empirical Methodology . . . . . . . .
. . . . . . . . .
18
1.1.1
Data and Sample Characteristics
.
. . . . . . . . .
18
1.1.2
Identifying Good and Bad Times
. . . .
. . . . . . . . .
21
Governance and Equity Returns . . . . . . . . .
. . . . . . . . .
23
1.2.1
Methodology. . . . . . . . . . . . . . .
. . . . . . . . .
23
1.2.2
Results . . . . . . . . . . . . . . . . . . .
. . . . . . . . .
25
. . . .
. . . . . . . . .
28
Governance and Operating Performance
1.3.1
Methodology . . . . . . . . . . . . . . .
. . . . . . . . .
28
1.3.2
Profitability Results
. . . . . . . . . . .
. . . . . . . . .
29
1.3.3
Analyst Forecasts . . . . . . . . . . . . .
. . . . . . . . .
30
1.3.4
Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
Governance versus Non-Agency Characteristics . . . . . . . . . . . . .
32
1.4.1
Characteristics-Corrected Performance
. . . . . . . . . . . . .
33
1.4.2
Business Combination Laws Event Study . . . . . . . . . . . .
34
1.5
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
1.6
Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
1.4
2
13
The Impact of Shareholder Disagreement: Evidence from Spin-Offs
and Mergers
51
2.1
Theoretical Motivation . . . . . . . . . . . . . . . . . . . . . . . . . .
56
2.1.1
57
M odel Set-Up . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
2.2
2.3
2.1.2
Equilibrium Prices without Bundling
59
2.1.3
Equilibrium Prices with Bundling and Com parisons
61
2.1.4
Key Implications of Theory for Empirics .
64
Data and Empirical Methodology . . . . . . . . .
66
2.2.1
Data and Sample Characteristics
66
2.2.2
Empirical Methodology ............
. . . . .
69
Spin-Off Results . . . . . . . . . . . . . . . . . . .
70
2.3.1
Spin-Off Ex Date and Post Ex Returns . .
70
2.3.2
Spin-Off Announcement and Announcement
until Ex Date Re72
turns . . . . . . . . . . . . . . . . . . . . .
2.4
. . . . . . . . . . . . . . . . . . .
72
2.4.1
Merger Ex Date and Post Ex Returns . . .
72
2.4.2
Merger Announcement and Announcement u ntil
M erger Results
. . . . . . . . . . . . . . . .
75
. . . . . . . . . . . . . . . . .
76
. . . . . . . . . . . . . . . . . . . . . .
89
2.5
Concluding Remarks
2.6
Figures and Tables
2.7
Appendix
2.7.1
2.7.2
E x Date Returns 73
Sufficient Conditions for Non-Negative Pri ce In pact of Un-
bundling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
Numerical Example of Negative Price Impact of Unbundling .
94
8
List of Figures
1-1
Adj. Return of High and Low Governance Firms versus Industry Return 39
1-2
ROA of High and Low Governance Firms versus Industry ROA
2-1
Spin-Off Illustrative Timeline
. . .
39
. . . . . . . . . . . . . . . . . . . . . .
76
9
10
List of Tables
1.1
Governance Sample Summary Statistics . . . . . . . . . . . . . . . . .
40
1.2
Stock Returns of High vs. Low Governance Firms, 1990-2008 . . . . .
41
1.3
Stock Returns of High vs. Low Governance Firms - Robustness Tests I
42
1.4
Stock Returns of High vs. Low Governance Firms - Robustness Tests II 43
1.5
Operating Performance of High vs. Low Governance Firms, 1990-2008
1.6
Operating Performance of High vs. Low Governance Firms - Robust-
44
ness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45
1.7
Analyst Forecast Patterns, 1990-2008 . . . . . . . . . . . . . . . . . .
46
1.8
Valuation and Investment Patterns, 1990-2008 . . . . . . . . . . . . .
47
1.9
Lagged Investment vs. Profitable Periods, 1990-2008
48
. . . . . . . . .
1.10 Characteristic-Corrected Return and Operating Income Patterns, 19902008 .......
...
....................................
49
1.11 Event Study - Business Combination Laws . . . . . . . . . . . . . . .
50
2.1
Spin-Off Summary Statistics . . . . . . . . . . . . . . . . . . . . . . .
77
2.2
Merger Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . .
78
2.3
Spin-Off Ex Date Returns . . . . . . . . . . . . . . . . . . . . . . . .
79
2.4
Spin-Off Post Ex Date Returns
80
2.5
Spin-Off Announcement Date Returns
2.6
Spin-Off Announcement until Ex Date Returns
2.7
Stock Merger Ex Date Returns
. . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . .
81
. . . . . . . . . . . .
82
. . . . . . . . . . . . . . . . . . . . .
83
2.8
Merger Post Ex Date Returns . . . . . . . . . . . . . . . . . . . . . .
84
2.9
Merger Announcement Date Returns
85
11
. . . . . . . . . . . . . . . . . .
2.10 Merger Announcement until Ex Date Returns . . . . . . . . . . . . .
86
2.11 Attribution of Disagreement . . . . . . . . . . . . . . . . . . . . . . .
87
2.12 Predicted vs. Unpredicted Disagreement and Returns . . . . . . . . .
88
12
Chapter 1
Making the Most of Good Times:
Shareholder Rights and
Performance Revisited
A large literature in corporate governance has documented a positive relationship between governance and performance.1 Most empirical studies have focused on whether
there is a level effect of governance, while little attention has been paid to how governance interacts with the economic environment of the firm. Many agency theories,
however, suggest that the impact of governance would vary with business conditions.
Some models imply that manager and shareholder interests diverge the most in times
of operational slack, when managers can engage in inefficient empire building, skimming extra pay or perks, or shirking from making the effort to cut costs and invest
in new opportunities.2 On the other hand, it may be that bad times drive more of
a wedge between managers and shareholders. Managers may be slow to shut down
inefficient operations, or, as termination becomes more likely, they may engage either
in overly expensive measures to reduce risk or in excessive risk-taking to "gamble for
resurrection." 3 Depending on which of these agency problems are the most prevalent,
'See Bebchuk and Weisbach (2010) for a review of much of this literature.
2
See, e.g., Jensen (1986), Yermack (1997), and Bertrand and Mullainathan (2003).
3
See, e.g., Bertrand and Mullainathan (2003), Gormley and Matsa (2012), and Jensen and Meckling
(1976).
13
or which are the problems best addressed by the constraints and incentives imposed by
corporate governance mechanisms, governance might primarily improve performance
in either good times or bad times.
If these dynamics are not considered, we may incorrectly measure the effect of
corporate governance, which in turn can lead to suboptimal governance choices and
poor policy decisions. At the same time, examining these variations may provide further insight into the channels through which governance and performance are related.
In this paper, I study one dimension of governance - the firm-level provisions and
state statutes that weaken shareholder rights - and its relationship to performance
in different environments. 4 Shareholder rights are of particular interest in that they
are very widely used to measure corporate governance and yet the exact channel
through which they impact performance remains unclear. It is also important that
firms' shareholder rights provisions have been quite stable over the last two decades,
which allows me to isolate the interaction of business conditions with this aspect of
governance.
Focusing on the G Index, the measure developed by Gompers, Ishii and Metrick
(2003) ("GIM"), and exploiting within-industry variation in this index, I find that the
outperformance of well-governed firms is concentrated in good times. I first consider
good and bad times in the context of equity returns from 1990 to 2008. Categorizing
periods based on average industry stock returns, I find that the stocks of well-governed
firms outperform shares of poorly governed firms in the same industry on a riskadjusted basis when their industries are experiencing high returns. These positive
returns are at least partially reversed during industry downturns, when well-governed
firms earn negative risk-adjusted returns relative to poorly-governed firms. Next,
considering operating performance over the same period, I find that well-governed
firms outperform relative to poorly-governed firms in the same industry during highly
profitable periods, while the firms have similar operating performance during weak
industry conditions.
4
Hereafter, I will use the more general term "governance" interchangeably with shareholder rights,
but this is done for expositive simplicity, and not to diminish the importance of the many other
facets of governance.
14
These results help to explain puzzling findings with regard to the relationship
between shareholder rights and stock returns.
GIM find that well-governed firms
have about 20% higher valuations in terms of Tobin's
Q,
and that a governance hedge
portfolio would have generated average abnormal returns of an impressive 8.5% per
annum in the 1990's.
Others who have extended this analysis5 find no abnormal
returns to good governance in the 21st century, although the differential in valuation
persists. Since any expected differences in operating performance should already be
accounted for in the consistently higher valuations of well-governed firms, it is difficult
to explain why large return differentials persisted for a decade, and then why they
would disappear. One theory is that the G Index is measuring exposure to some form
of systematic risk that investors demand compensation for bearing and which is not
already captured by the model used by GIM to calculate abnormal returns.' Another
leading theory is that the pattern in returns to governance reflects learning. That
is, investors may have been consistently surprised by the outperformance of wellgoverned firms over the 1990's, but then adjusted their expectations going forward. 7
My results provide an alternative explanation.
If well-governed firms are more
profitable than poorly-governed industry peers only when an industry is doing particularly well, and investors understand this, current valuations of well-governed firms
should include a premium representing the net present value of this future outperformance, adjusted for the likelihood of such good times. Then, if new information
indicates that an industry boom is more likely than was previously thought, the stock
prices of all firms in the industry would adjust upwards in anticipation of higher
profits. However, well-governed firms would experience higher returns than poorlygoverned firms because of upward updating of the probability-adjusted premium for
'See, e.g., Core, Guay and Rusticus (2006) and Bebchuk, Cohen and Wang (2012).
Among the additional risk factors that have been suggested, none have been able to fully absorb the
alpha earned by the governance hedge portfolio. See Giroud and Mueller (2011), Cremers and Ferrell
(2009), and Bebchuk, Cohen and Wang (2012) for tests using the takeover factor of Cremers, Nair
and John (2009) as well as factors for liquidity, co-skewness, downside risk, and aggregate volatility.
7
While attention to corporate governance does seem to have increased significantly by 2001, evidence
is mixed regarding whether investors were surprised by the performance of well-governed firms until
that time. See, e.g., Core, Guay and Rusticus (2006), Cremers and Ferrell (2009), and Bebchuk,
Cohen and Wang (2012).
6
15
outperformance.
This positive relative return would be observed even though in-
vestors fully understood the implications of good governance for performance. On
the other hand, if an industry boom becomes less likely, well-governed firms would
experience more negative returns than poorly-governed firms as the expected value
of the premium that had already been impounded into their stock prices is adjusted
downwards.
These dynamics can explain the seemingly contradictory evidence in
GIM and the subsequent literature in that the observed returns to governance are
conditional on the type of period sampled.
I use analyst forecast data to confirm that investors understand the performance
implications of governance throughout my sample. For quarters in which analysts
forecast particularly high industry earnings, they also forecast well-governed firms to
be more profitable than poorly-governed firms, by a margin similar to the actual observed operating outperformance in good times. This is true both from 1990 to 2001
as well as from 2002 to 2008. Thus, my results are consistent with investors expecting
well-governed companies to earn extra profits in good times but being surprised by
news about future good times and updating valuations accordingly. Whether or not
we observe positive abnormal returns to governance then depends on the particular time sample selected and the outcomes or changes in outlook experienced during
that time. Whether the positive abnormal returns should exceed the negative abnormal returns in expectation (or over long time horizons that produce a range of
outcomes approaching the expected distribution) is less clear. The less idiosyncratic
the industry booms are, the more likely investors are to demand such a risk premium.
I also make a preliminary investigation into how well-governed firms manage to
outperform in good times. Others have linked weak shareholder rights to poor acquisition decisions, generally suboptimal use of cash holdings, and over-generous executive
compensation.8 I find that the higher operating income generated by well-governed
firms relative to poorly governed industry peers is driven by stronger revenue growth,
and possibly higher margins, in good times. Well-governed firms also invest more on
8
See Masulis, Wang and Xie (2007), Harford, Mansi, and Waxwell (2008), Dittmar and Mahrt-Smith
(2007), and Fahlenbrach (2009).
16
average than poorly-governed firms, and apparently even more so in advance of good
times, though it is unclear whether these firms invest more in anticipation of their
higher productivity or whether their higher productivity can be attributed to such
investment.
Overall, these patterns do not provide evidence of empire building or earnings
management, but are consistent with agency theories of shirking and possibly skimming. Such misbehavior might flourish at poorly governed firms either directly because of weak shareholder rights provisions or indirectly because of their relationship
to a lack of board discipline or other cultural factors. The patterns are also consistent
with non-agency theories, such as growth firms being more likely to adopt stronger
shareholder rights than value firms, but the governance patterns remain even after I
control for growth firm characteristics.
An agency explanation of outperformance in good times is further supported by
an event study around the passage of state anti-takeover laws between 1985 and 1991,
where this shock to governance is found to have a negative stock price impact only on
those firms with a high value of future prospects, as measured by Tobin's
Q.
Thus,
investors expected this negative shock to governance to have the greatest impact on
value derived from future good times. This result provides strong support not only for
the hypothesis that governance matters most in good times, but also that investors
have understood the implications of governance and over time have been surprised
by news of good outcomes rather than the observed outperformance conditional on
such outcomes.
This chapter is organized as follows. The next section presents details on the data
used and the sample analyzed. Then, Section 1.2 discusses the relationship between
returns and corporate governance in different business environments.
Section 1.3
analyzes operating performance, analyst forecast, and investment patterns, as well as
their implications for agency theories. Section 1.4 considers non-agency explanations
for the performance patterns. Concluding remarks are offered in Section 1.5.
17
1.1
1.1.1
Data and Empirical Methodology
Data and Sample Characteristics
The sample analyzed is drawn from firms for which the G Index is available, described
in Panel A of Table 1.1, and which in all sample years represent over 90% of the
market value of companies traded on the major US exchanges. This index, originated
by GIM, is constructed on the basis of a total of 24 provisions (or lack thereof) in
company charters and state law which weaken shareholder rights. The index value is
equal to the sum of these that apply to a given firm. The G Index can thus range
from 0 to 24, where higher levels of the index (which I will refer to as poor or low
governance) relate to weaker shareholder rights. In robustness tests, I also use the E
Index of Bebchuk, Cohen and Ferrell (2009), which ranges from 0 to 6 and is based
on a subset of 6 out of the 24 provisions included in the G Index.
G Index data is obtained from Andrew Metrick's website and is based on publications of the Investor Responsibility Research Center ("IRRC") dated September
1990, July 1993, July 1995, February 1998, November 1999, January 2002, January
2004, and January 2006. Later IRRC publications are not consistent with these in
terms of the definitions of variables as well as the variables provided, so G Index
classifications are not available beyond 2006. Consistent with the previous literature,
I assume G to remain constant in between these dates and from 2006 through the end
of 2008. As discussed by GIM, Cremers and Ferrell (2009), and others, classifications
of firms as well- or poorly-governed according to the G Index are very stable over this
sample period, making this quite a reasonable assumption. The stability of these categorizations is useful for my purposes in that there are fewer concerns about reverse
causation when exploring the relationship between governance and performance in
good and bad times.
My analyses are primarily at the industry level, based on the historical 3-digit SIC
code from CRSP. I also use the Standard & Poors 8-digit GICS industry classifications
from Compustat in robustness tests, but for the majority of firms I only have access
to history for this classification beginning in 1994 (through the Compustat variable
18
SPGIM) and therefore back-fill the earliest available GICS code when necessary. I
follow the literature in excluding firms with dual stocks from my sample. As shown
in Panel A of Table 1.1, there are then an average of 241 3-digit SIC industries which
have some G Index data in a given month in my sample.
IRRC coverage varies
from publication date to publication date, resulting in an unbalanced panel (with the
number of 3-digit SIC industries covered ranging from 222 to 268). To be included
in the sample for a given analysis, I require that an industry include both high and
low governance representation. High and low governance are either defined in the
terminology of GIM as Democracies and Dictatorships (G less than 6 and G greater
than 13 respectively, representing less than 10% of the sample at each extreme) or as
the extreme quartiles of the G Index (approximated as G less than 7 and G greater
than 11).
The resulting samples are described in Panels B and C of Table 1.1. While the
number of industries that include both high and low governance representation drop
sharply relative to the total that have G Index data, they represent some of the
largest 3-digit SIC industries and therefore a significant fraction of the market value
of the full sample. Thus, while the Democracies and Dictatorships sample includes
about 21 industries in a sample month, these represent 40% of the market value of
the firms in the 241 industries that, on average, have G Index data available. The
Democracy and Dictatorship firms in my sample represent 47% of the market value
of the Democracy and Dictatorship firms in the full sample in an average month.
The governance quartiles sample includes an average of 59 industries representing
73% of the market value of the full sample, with the high and low governance groups
representing 77% of the high and low governance firms in the full sample.
As in the case of the full sample, these panels are not balanced; as seen in the
distributions provided in Panels B and C of Table 1.1, the number of industries with
both high and low governance representation varies from month to month. This is
due to the variation in IRRC coverage as well as firms dropping out of the CRSP file,
changing industries, or moving out of the high and low governance groups. However,
industries generally stay in the sample for one or at most two continuous periods
19
(60% and 94% of the industries respectively), rather than moving in and out of the
sample multiple times. My results are robust to including only those industries that
are in the sample for only one continuous period or to only considering observations
that fall in a period of continuous industry coverage of at least 24 months. Industry
good and bad times are defined by considering industry performance over the full
time period, including periods when either of, or both, high and low governance are
not represented, as described in Section 1.1.2 below.
The 3-digit SIC industry classifications are relatively narrow, such that an average
industry includes 21 firms with G Index data in a given month for the Democracies
and Dictatorships sample, with about 2 firms in each of the governance extremes (and
an average of 12 firms per industry with 3 firms in the extremes for the governance
quartiles sample).
The average industry size is smaller in the case of governance
quartiles because the more moderate definitions of high and low governance mean
that many smaller industries fulfill the requirement of including representation of
both extremes. Thus, while the larger number of firms found on average in the high
and low categories might reduce the noise in the governance performance spread
relative to the Democracies versus Dictatorships sample, the smaller number of firms
that, on average, define the industry performance in the case of governance quartiles
will add noise to the classification of good and bad periods. More detail on the firms
included in industries for the purpose of calculating such industry performance is
provided in the Section 1.1.2.
For analyses of returns, I require a match to CRSP data. For operating performance analyses, I require a match to Compustat data. I analyze performance data
from September 1990, the first month for which G Index data is available, through the
end of 2008. I end the period of analysis in 2008 to be consistent with the lags between
updates of the G Index (the last such update is in January 2006) and to avoid stale
classifications. 9 All operating performance variables are winsorized at 1% in both
tails. Characteristics of high compared to low governance firms by industry across
the full time period and under each of the two definitions of the governance extremes
9
1n unreported robustness tests, results are consistent when extended through 2011.
20
are given in Panel D of Table 1.1. Consistent with GIM and the subsequent literature,
I find that high governance firms have higher valuations in the form of Tobin's
Q. In
my industry-matched sample, I find that high governance firms are also slightly less
levered and have many characteristics of growth firms; they are younger, spend more
on capital expenditures, and have stronger revenue growth. High governance firms
are associated with a higher return on assets, defined as operating income after depreciation to assets, though this relationship is not statistically significant across the
full sample period for the governance quartiles sample. The differences in operating
performance and other characteristics will be analyzed in more detail in Sections 1.3
and 1.4.
1.1.2
Identifying Good and Bad Times
I use the performance of an industry in a given period, relative to its performance
over the full sample, to characterize periods as good and bad times for firms in the
industry. Using individual industry performance as the baseline rather than economywide performance provides a more refined measure of good and bad times. Also, this
approach allows for a larger number of non-synchronous good and bad time observations, compared to, for example, only three economy-wide recessions over this time
period. High and low levels of the G Index have been found to cluster in industriesm
and so examining within-industry variation in performance by governance group over
the industry highs and lows also helps to separate governance from industry effects.
Thus, I measure industry performance in each month or quarter of the sample
period, then rank these periods and classify periods in the top quartile of performance
as good times and those in the bottom quartile of performance as bad times. In
measuring industry performance for a given period, I only include firms that either
(i) have a current G Index value; or (ii) do not have a current G Index value but
do at some point in the sample period and are not ever classified as high or low
10
Johnson, Moorman and Sorescu (2009) demonstrate this clustering and the impact on analyses that
do not address it. Lewellen and Metrick (2010) provide a further discussion of how best to control
for industry effects in analyses using the G Index.
21
governance. Further, in calculating the performance of an industry, I equally-weight
firm performance with an adjustment to the weights of high and low governance firms.
Specifically, I equally weight all explicitly mid-governance and unclassified but once
mid-governance firms, and rescale the weights that would have applied to the high
and low governance firms such that each of these governance groups receives the same
weighting as a whole in that period. If either one of the extremes of governance is
not available in that period, the other extreme of governance is not included in the
industry metric either.
The reason for this somewhat restrictive definition of the industry and the adjusted weighting scheme is to avoid biasing the industry metrics towards one of the
groups being analyzed, or else the results might not tell us anything about good and
bad times. For example, if the performance of well-governed firms was correlated in
some way unrelated to good and bad times for the industry, and such firms were overrepresented in the industry metric, well-governed firms experiencing a positive shock
would make it likely for the industry to be classified as being in good times. It would
look like these firms were outperforming in good times even if their outperformance
was unrelated to industry good times. Thus, the weights given to the groups of welland poorly-governed firms in an industry are equalized. Similarly, firms for which I
do not have governance data are excluded, since I cannot tell if high or low governance firms are overrepresented among such firms. While the 3-digit SIC codes that I
primarily use to identify industries already provide a relatively fine classification, and
excluding non-IRRC firms reduces the number of firms per industry further, any noise
from using relatively small industries should work against my finding a result. As
shown in Panels B and C of Table 1.1, there are some months for which the industry
performance is defined by only one (mid-governance) firm, but these cases are limited
to less than 1.5% of the sample months in the Democracies and Dictatorships sample
(and around 6% in the governance quartiles sample, which includes much smaller
industries on average).
Similarly, if an individual well- or poorly-governed firm is overrepresented in the
industry metrics, its independent shocks would also be more likely to impact the
22
classification of good and bad times and it thus would tend to make its own governance
group look more pro-cyclical. Equal weights are applied for this reason. In robustness
tests, I exclude high and low governance firms from the industry definitions altogether,
make such exclusions and also apply value weights, and alternatively include all firms
in the CRSP-Compustat matched database, whether or not they are assigned G Index
data, in the industry definitions.
1.2
1.2.1
Governance and Equity Returns
Methodology
In order to study the relationship of governance with equity returns in good and bad
times, I first generate a time-series of the adjusted return differential between high and
low governance firms in each industry. For this purpose, I calculate adjusted returns
by firm, using the three-factor Fama-French model augmented with the momentum
factor constructed by Kenneth French for performance attribution.
I obtain the size factor, the book-to-market factor, and the momentum factor
("SMB,"
"HML," and "UMD") from Kenneth French's website, and construct the
market factor ("RMRF," the return on the market factor minus the risk-free rate)
using the value-weighted market index and the 1-month risk-free rate from CRSP.
For robustness tests, I also construct the Carhart (1997) "PR1YR" momentum factor
from CRSP monthly data. To calculate a firm's adjusted return, I estimate its betas
on the four factors using monthly returns and an outside period of up to five years
before and up to five years after a given year, with a minimum of six months required
to use either the pre- or post-period. Estimates from the pre- and post-period are
given weights inversely proportional to the variance of these estimates to arrive at
the final beta estimates.
GIM and much of the subsequent literature assume static betas. I update the
beta estimates each year as described above since I am interested in dynamics over
time and thus would like to give the factors an opportunity to explain as much of the
23
variation as possible. For the same reason, I use a future as well as historical period
in the estimation of betas. Even though this approach means that I do not generate a
strictly tradable strategy, it better accounts for changes and secular trends in betas.
Thus, these more up-to-date estimates of the betas give the factors a better chance of
absorbing the dynamics of performance than betas calculated using only lagged data.
(In non-reported robustness tests, the results are very similar using static betas or
betas calculated only with lagged data.)
The adjusted return for a given firm (f) and a given month (t) is then calculated using the firm excess return (Rft, the return minus the risk-free rate) and the
estimated betas as:
aft = Rft - fRMRFRMRFt - /SMLSML, - !HMLHMLt - /UMDUMDt
These firm-level adjusted returns are then combined into equally weighted portfolios by industry-governance group (value weights are used in robustness checks). The
monthly adjusted returns of these high and low governance groups by industry are
plotted against the contemporaneous industry-level unadjusted return (calculated as
per Section 1.1.2) in Figure 1-1. Since high and low industry return periods likely reflect shocks that are at least somewhat idiosyncratic relative to the four-factor model
above, we expect the factor-adjusted returns of firms in these industries to be increasing in the unadjusted industry return. However, the graph also demonstrates
my main result, that well-governed firms earn positive abnormal returns relative to
poorly-governed firms in high industry return periods, but that these returns are
reversed in periods of negative industry returns.
In order to run more rigorous statistical tests, I will first normalize industry good
and bad times. In this process, I will also apply a non-linear specification of good
and bad times. About 50% of the observations in Figure 1-1 lie between -2.5% and
2.5% industry returns, where there is limited variation across governance groups, so
a linear specification would have less power to identify the performance differential.
24
Thus, I generate indicators for good and bad times by industry, representing
the top and bottom quartiles of unadjusted monthly industry returns, following the
methodology described in Section 1.1.2. Finally, I regress the monthly time-series of
the high-low governance differential adjusted return by industry (i) on these monthly
good, bad, and mid-quartile dummies to estimate:
rdiff,it =
/31 Dist + 32 ,3 D 2 ,3it + /34 D 4 it + Eit
This regression forms the basis of my main results.
1.2.2
Results
The main results regarding the relationship between stock returns and governance
in good times and bad times are given in Table 1.2. Results are presented for three
definitions of high and low governance - the Democracies and Dictatorships classification of GIM (G less than 6 or greater than 13), a broader definition using governance
quartiles (G less than 7 or greater than 11), and an even broader classification of
above median and below median governance (G less than 10 or greater than 9) - as
well as for both adjusted (for the four factor model) and unadjusted returns. I also
present results for the E index, though I do not use this measure of governance in
further analyses as the results are less robust than those for the G Index. Reported
T-statistics are based on heteroskedasticity robust standard errors that are clustered
at the month level."
Throughout Table 1.2, we see that high governance firms experience positive abnormal returns relative to low governance firms in good times, and that these are
at least partially reversed by negative abnormal returns in bad times, similar to the
pattern demonstrated in Figure 1-1. For example, for the Democracies versus Dictatorships classification, there is a significant 2.4% per month adjusted return to
good governance in months in which industries are experiencing high returns, and a
1
Cremers and Ferrell (2009) also find that the equity performance results for the E index are not as
robust as those for the G Index, with results becoming insignificant once they control for industry
or add firm fixed effects.
25
significant -1.0% monthly return to good governance in months of low industry returns. Across all periods, this translates into a net 0.33% monthly adjusted return,
or approximately 4% per annum. This net positive alpha may indicate that investors
require a risk premium for this variation, or it may simply reflect that the outcomes in
this sample period are tilted more towards good times than the distribution of good
and bad times expected by investors. Regardless of whether or not it is priced, this
pattern of returns addresses the puzzle of disappearing returns to governance in that
whether or not abnormal returns to governance are observed is clearly conditional on
the type of period sampled.
On first glance, the returns of high governance firms may appear to be a levered
version of the returns to low governance firms. However, as seen in Panel D of Table
1.1, high governance firms are actually less levered and hold more cash than low
governance firms. Another possibility is that firms that experience particularly large
positive or negative shocks tend to tighten their governance in advance of the price
impact.
If this were the case, we might expect to see a correlation between good
governance and these large positive and negative returns.
However, as mentioned
in Section 1.1.1, categorizations according to shareholder rights provisions are quite
static, so reverse causation is unlikely. To the extent that there are still some rare
changes in classification within my sample, my results are robust to dropping firms
if their governance categorization changes. Also, it is worth noting that Schoar and
Washington (2011) find that periods of abnormally good firm performance tend to
be related to a weakening of shareholder rights, and this primarily by already poorlygoverned firms, a pattern that, if anything, works against the direction of my results.
They find no changes in governance related to periods of abnormally bad performance.
Tables 1.3 and 1.4 present robustness tests. The first two columns of Table 1.3
demonstrate that the pattern of returns is similar from 1990 to 2001 and 2002 to
2008.12 The third column of this table sorts periods by market returns rather than
industry returns. The fact that the pattern remains may mean that at least a portion
12 Bebchuk, Cohen and Wang (2012) demonstrate that attention to corporate governance in the media and academic literature had increased significantly by the end of 2001, and that the apparent
disappearance of returns to governance coincided with this increased attention.
26
of the additional volatility in high governance stock prices is systematic in nature,
and likely to be a priced risk. However, since our abnormal returns already control
for a linear market beta, this result is non-linear and would probably imply a dynamic market beta, as in a conditional CAPM model. By splitting the sample period
between high and low market return periods, and returning to using industry good
and bad time dummies, columns (4) and (5) demonstrate that there remains a strong
industry effect incremental to the effect of the market.
Column (6) demonstrates
that the results are robust to using 8-digit GICS industries as the unit of analysis
rather than defining industries on the basis of 3-digit SIC codes. In column (7), all
firms in the CRSP database are included in their respective 3-digit SIC industries for
the purpose of defining the performance quartiles, demonstrating that the exclusions
(described in Section 1.1.2) that I make in order to be conservative are not driving
my results.
Results are generally consistent for the robustness checks in Table 1.4 as well,
which include using alternative weighting schemes, varying the factor model, and excluding particular industries. To address the theory that governance matters the most
in the presence of high free cash flow (e.g., Chi and Lee (2010)), columns (5) and (6)
of Table 1.4 separately consider industries with above and below median cash flows.
The fact that results are similar in both cases suggests that the underperformance of
poorly-governed firms in good times is not solely a function of inefficient uses of cash.
In general, though, the pattern of returns to governance, while it helps to address
the puzzle of disappearing returns post 2001, only demonstrate a difference in volatility and do not provide a value judgment. To further understand the source of the
variation in returns, I next consider operating performance patterns.
27
1.3
Governance and Operating Performance
1.3.1
Methodology
Given that returns are forward-looking with variable lags to the expected outcomes
that they reflect, it is difficult to directly tie good times in the sense of returns to good
times in the sense of profits. With the understanding that returns reflect expectations
of profits at some point in the future, in this section I thus directly consider good
times in the sense of profits. A preview of my results is provided in Figure 1-2, which
demonstrates that well-governed firms earn extra profits only in highly profitable
periods for the industry.
The approach to statistically analyzing operating performance is similar to that
used for returns, in that I generate a time-series of high-low governance differential performance and regress it on dummies for the type of period the industry is
experiencing, based on quartiles of industry performance. One key difference relative to stock returns is that operating performance is likely to be auto-correlated.
For this reason, I use two different approaches to calculating my standard errors for
these analyses. One approach is to apply a parametric AR(1)-process correction1 3 to
heteroskedasticity-robust standard errors, clustered at the quarter level. The second
approach is to cluster my standard errors by industry as well as by quarter.
In this section, I consider raw performance measures, un-corrected for other firm
characteristics which may impact performance. Since differences in such non-agency
characteristics could themselves result from differences in governance, it is useful to
first examine how the raw operating performance for high governance and low governance firms within the same industry differs. I consider the possible impact of
non-agency characteristics (which may or may not be affected by governance choices)
on the observed differences in performance between firms with different levels of governance in Section 1.1.4.
1I
adapt the standard AR(1) correction described in Bertrand, Duflo and Mullainathan (2004) to
allow clustering by quarter and to account for non-consecutive observations that are associated with
each industry-period-type.
28
1.3.2
Profitability Results
My main results for profitability measures are presented in Table 1.5. The measure of
profitability that I focus on is the return on assets ("ROA"), measured as operating
income after depreciation to total assets. I also consider net income to book equity,
but these results are more noisy, consistent with the findings of other researchers,
including GIM and Core, Guay and Rusticus (2006).
Similar to the pattern demonstrated in Figure 1-2, in Table 1.5 I find an asymmetric relationship of governance with operating performance, whereby high governance
firms outperform low governance firms in highly profitable times for an industry, but
both types of firms perform similarly in weaker industry conditions. The magnitude
of the outperformance is strongly significant statistically and economically. For example, the 1.08 percentage point higher quarterly operating income to assets in good
times represents over 50% of the mean quarterly ROA of a poorly-governed firm.
From an agency perspective, the differential profits in highly profitable times might
be the result of managers having access to rents, which the poorly-governed managers
capture or pass on to others, or might be driven by well-governed managers taking
advantage of real options while poorly-governed managers shirk from such opportunities. The results for the separate components of return on assets, namely revenue to
assets and operating income to revenue, are consistent with both possibilities as both
are higher for well-governed firms in good times (though only the result for revenue is
statistically significant, and this is the case only when using AR(1) corrected standard
errors).
Table 1.6 presents various robustness checks, similar to those considered in the case
of returns, for the ROA results. Results are generally consistent with the base case,
including for the two time period sub-samples. The pattern of relative performance
is somewhat less consistent across specifications for the broader definition of high
and low governance (i.e., governance quartiles), as shown in Panel B, than for the
more commonly used categorization of Democracies and Dictatorships. Also, valueweighted results are somewhat less similar to the base case, but much of the difference
29
is driven by not including high and low governance companies in the industry period
classifications (as demonstrated in the fifth column), which can result in a more noisy
measure of industry good and bad times.
The pattern of operating performance provides an explanation for the pattern in
returns demonstrated in Section 1.2. Namely, if investors understand that well governed firms capture more profits in good times, the current valuations for such firms
should incorporate the net present value of this future outperformance, adjusted for
current expectations of the likelihood of such good times. As new information arrives
regarding the likelihood of such good times, the expected value of this additional
premium changes, resulting in positive or negative returns relative to other firms.
1.3.3
Analyst Forecasts
In order to examine what investors may have understood about the impact of governance, I consider analyst forecasts as a measure of investor expectations. To calculate
the forecast income to book equity for a given quarter, I collect the monthly mean
estimated earnings per share from IBES within 31 days before the beginning of the
fiscal quarter. I then scale up the estimated earnings per share by applying the ratio
of this estimate to the actual earnings per share reported by IBES, winsorized at 1%
in both tails like my other operating performance variables, to the actual net income
to book equity for the quarter based on Compustat data. Observations for which the
sign of the actual earnings per share reported by IBES and the sign of the actual net
income reported by Compustat do not match are treated as missing.
Results of this analysis are presented in Table 1.7. In Panel A, I consider periods
that are forecast to be good and bad times for each industry, based on the industry
average (calculated as per Section 1.1.2) forecast income to book equity. I find that
analysts predict well-governed firms to generate higher incomes in times that they
predict to be highly profitable for the whole industry. This pattern is consistent over
the 1990-2001 and 2002-2008 subperiods.
Under a learning story, we would have
expected analysts to predict better performance for well-governed firms after 2001
than they did beforehand. Instead, we find that analyst predictions follow a similar
30
pattern to the actual operating performance patterns considered in Section 1.3.2.
In Panel B, I consider analyst forecast errors, which follow a symmetric pattern
similar to my return results, whereby analysts are more positively (negatively) surprised by well-governed firms relative to poorly-governed ones in times of large positive (negative) surprises. Again, the pattern is similar for both subperiods. Under a
learning story, we would have expected to observe stronger positive surprises to good
governance in the former period than in the latter.
This evidence is consistent with analysts understanding the asymmetric relationship of corporate governance with operating performance, and being surprised by the
type of period experienced rather than the differential performance of well- versus
poorly-governed firms in a particular type of period.
1.3.4
Investment
In Table 1.8, I consider the relative levels of investment of high and low governance
firms. In Table 1.8, we see that well-governed firms invest more than poorly-governed
firms, both in terms of capital expenditures as well as in terms of changes in net
property, plants and equipment. The difference in investment is higher in periods
of higher industry levels of Tobin's
Q,
which may measure industry investment op-
portunities, but it is unclear from Table 1.8 whether the incremental investment is
efficient or not. Since well-governed firms, while spending more on investment, also
demonstrate better operating performance, it is likely that this is good rather than
wasteful investment.
In an attempt to relate the difference in investment with the observed difference
in ROA in good times, I consider lagged investment in advance of such high industry
profit periods. Results are presented in Table 1.9. While not statistically significant,
for the Democracies versus Dictatorships sample, the investment differential in the
four quarters preceding a particularly profitable quarter is double the differential in
a year preceding either low or mid levels of industry profitability. The same pattern
is found for the eight quarters preceding these four quarters, which helps to address
the concern that the former result may be driven by anticipation of forthcoming high
31
cash flows that can be spent on investment. However, these patterns are not seen
in the results for the governance quartiles sample. Thus, these results provide only
suggestive evidence that the operating performance differential might be partially
explained by well-governed firms taking advantage of more growth opportunities than
poorly governed firms. It is also unclear whether these firms may invest more in
anticipation of their higher productivity or whether their higher productivity can be
attributed to such investment.
My results contrast with GIM, who find that poorly-governed firms spend more on
capital expenditures, and Philippon (2006), who finds that the investment of poorlygoverned firms is relatively more pro-cyclical.
Given the clustering of governance
types within industries, the reason that I reach opposite conclusions is likely driven
by my ability to control for more refined industry effects.
1.4
Governance versus Non-Agency
Characteris-
tics
A possible interpretation is that the equity and operating performance patterns identified in Sections 1.2 and 1.3 are driven by non-agency characteristics that are correlated with, but not caused by, our measure of corporate governance. For example,
as discussed in Section 1.1.1 and as seen in Panel D of Table 1.1, high governance
firms have many characteristics of growth firms. This may be an outcome of their
strong governance, or it may be that growth firms are simply likely to adopt fewer
anti-shareholder provisions. If the performance patterns discussed in this paper are
a result of growth characteristics or other omitted variables, they would still resolve
the puzzle of why high abnormal returns to governance in the 1990's seemed to disappear in the 21st century, but the interpretation of the differential performance would
change.
I make two attempts to address the alternative interpretations. First, I correct firm
performance for observable characteristics before running my performance regressions.
32
Secondly, I use an event study to explore an exogenous shock to governance and
thus separate the impact of governance from the potential impact of these other
characteristics.
1.4.1
Characteristics-Corrected Performance
In this analysis, I construct a two-stage, bias-corrected matching estimator to control
for differences in observable characteristics between well- and poorly-governed firms.
It is important to point out that differences in governance may cause some of the
variation in characteristics such as growth and investment.
Thus, controlling for
differences in such characteristics could absorb some or all of the effect of governance.
However, if there is a residual effect of governance even after controlling for such
factors, we can be more confident that the full performance differential related to
governance cannot be just as easily explained by a tendency of firms with particular
characteristics to choose a certain set of governance provisions.
In the first stage, I regress firm performance on a number of non-agency characteristics. In the second stage, I use the residuals from these regressions for high
and low governance firms to perform analyses of returns and operating performance
similar to those presented in Table 1.2 and Table 1.5.
For the first stage of the analysis, I follow GIM and Core, Guay and Rusticus
(2006) and include the book to market ratio and size, measured as the log of market
capitalization, as explanatory characteristics.
I also include 5-year revenue growth
and 3-year capital expenditures to assets as additional non-agency characteristics
that are likely to be related to performance and are also correlated with shareholder
rights. I interact each characteristic with the dummies for high, low, and mid industry
performance periods in order to allow a differential impact of these control variables
across good and bad times. This gives the non-agency characteristics a more fair
chance of explaining the differential performance results in such periods. In the first
stage regression, I include all firms with G Index data, including mid-governance
firms. To control for industry, I demean both the left-hand-side and right-hand-side
variables - performance and the non-agency characteristics - for the firms' industry-
33
period mean levels, so that I am explaining performance relative to the industry with
characteristics relative to the industry.
In the second stage of the analysis, I use the residual performance from these
regressions in my main stock returns and return on assets regressions. Table 1.10
presents the results of this characteristics-corrected performance analysis, including
my base-case results from Tables 1.2 and 1.5 for comparison. While correcting for
non-agency characteristics slightly dampens the effects, and results in a somewhat less
non-linear operating performance pattern, the overall patterns are consistent with my
previous results. Thus, the relationship between governance and outperformance in
good times is not easily explained by differences in observable characteristics. The
flattening of the pattern could mean that part of the performance differential related
to governance is in fact a function of differences in the types of firms that choose
particular governance levels, or it could mean that controlling for differences in characteristics such as growth and investment absorbs part of the impact of governance
that operates through these channels.
Of course, it is still possible that I am omitting additional variables or that wellgoverned and poorly-governed firms differ across an unobservable dimension. In the
next section, I try to address this concern by considering an exogenous shock to
governance.
1.4.2
Business Combination Laws Event Study
State-level business combination laws ("BC laws"), which reduce the threat of hostile
takeovers, were passed from 1985 to 1991 in 30 states.14 Being subject to such laws
is one of the provisions that is considered to be a weakening of shareholder rights in
the calculation of the G Index. Many other researchers, including Garvey and Hanka
(1999), Bertrand and Mullainathan (2003) and Giroud and Mueller (2010), have used
the passage of these laws as an exogenous source of variation in governance. Like
Giroud and Mueller (2010), I follow the event study methodology of Karpoff and
14
For a review of the details of these laws and the conditions surrounding their passage, see Bertrand
and Mullainathan (2003).
34
Malatesta (1989) to identify the firms that were expected to be negatively impacted
by the passage of such laws based on their stock price reactions to news of the laws.
If, as considered in this paper, governance matters the most in good times, and if
investors understand this, I would expect such laws to have the greatest immediate
price impact on the firms that had the highest expected value from future good times
at the time of the law's passage.
That is, since returns are forward-looking, the
event return should reflect investors' expectations of all future impacts of the event
on the firm's profitability. For this reason, it is not sufficient to look only at whether
firms are currently in good or bad times. In fact, short-term profitability might be
less likely to be affected by the change in law in that many of the decisions driving
current operations would have been made before the law was announced.
reason, I focus on Tobin's
Q
For this
as a proxy for the current expected value of future good
prospects.
Specifically, I study 2-day event returns for the last trading day prior to and the
first trading day on or after the first newspaper report of 19 of these state laws for
firms with different levels of Tobin's
Q. 1'
Giroud and Mueller (2010) demonstrate
that stock price reactions to the BC laws were concentrated in these 2-day periods.
My sample includes all firms in the CRSP-Compustat matched database that were
incorporated in these 19 states at the time of passage of the laws and which have
the required data to calculate returns and Tobin's
Q,
for a total of 3,921 firms (or
3,787 firms excluding utilities). For each of the 19 states for which I have an event
date, in addition to a single portfolio of all firms in the state, I also form 4 portfolios
of firms incorporated in the state representing the four quartiles of the distribution
of Tobin's
Q
in the state on the date 5 days prior to the event date. For each of
the resulting 19 state portfolios and 76 Tobin's-Q-sorted state portfolios, I regress
daily portfolio returns on the equally-weighted market index over the time period
from 241 trading days to 41 trading days prior to the event date in order to calculate
15
The 19 states studied are Arizona, Connecticut, Delaware, Georgia, Illinois, Kentucky, Maryland,
Massachusetts, Minnesota, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Tennessee, South
Carolina, Virginia, Washington, and Wisconsin. I would like to thank Xavier Giroud for kindly
sharing his data on the dates of first newspaper report of the BC laws in these states.
35
estimated market betas for each portfolio. These estimates are then used to calculate
2-day event cumulative abnormal returns (CARs). The standard errors of these CARs
are calculated as forecast errors, using the variance of the residuals and the standard
errors of the betas from the market model regressions.
My results are given in Table 1.11. I present results for the full sample as well
as a sample that follows Giroud and Mueller (2010) in excluding utilities. As shown,
across all firms, I find a -0.23% (-0.25% excluding utilities) negative event return
upon passage of the BC laws, similar to the impact measured by Giroud and Mueller
(2010).
However, considering the portfolios that are sorted by Tobin's
Q,
I find
that the negative event return is driven by a -0.61% (same excluding utilities) return
experienced by firms in the top quartile of Tobin's
Q, with
insignificant results for
any other quartile.
One possible concern with this interpretation is that sorting by Tobin's
Q may
in effect be sorting firms by their ex ante governance, given that good governance is
associated with a higher Tobin's
Q. It is logical
that firms with very few anti-takeover
provisions might be more affected by the passage of business combination laws than
firms that already have many takeover protections in place. I do not have data on
shareholder rights provisions during this time period in order to directly control for
this possibility. However, the sort here is much wider than the value differentials
Q in the fourth quartile is, on average, 4.83,
mean Q of 1.64 in the third quartile, compared to
attributed to governance. The mean
or almost 200% higher than the
a much smaller 20% improvement for Democracies relative to Dictatorships, from
1.46 up to 1.75. The much higher Q's of firms in the fourth quartile likely indicates
that their categorization is driven much more by their future prospects than by their
governance type.
Another concern is that the BC laws could have impacted acquirer opportunities
to generate value through hostile takeovers. Perhaps the acquirers that are most likely
to be negatively impacted in this way are high
Q firms because,
as posited by Shliefer
and Vishny (2003), such acquisitions allow them to "lock in" the overvaluation of
their equity. If this is the case, then the pattern of event returns might be driven
36
by high
Q firms
losing their takeover prey rather than the weakening of an external
governance mechanism. In order to address this possibility, I add an additional sort
on size, under the assumption that acquirers are likely to be relatively larger firms.
Specifically, I split the firms in each state into large and small firms based on their
market capitalization 5 trading days before the event relative to the median of such
capitalization for the state.
Since I am now splitting the firms incorporated in each state into a total of 8 portfolios, there is substantially more noise and statistical significance drops accordingly,
but the patterns are still revealing. Among larger firms, the largest event returns
are experienced by the next-to-highest Tobin's
Q quartile,
which experiences a mean
event return of -0.89% (-0.77% excluding utilities). This impact may be consistent
with an effect of governance in good times, though it is unclear why the top
Q quartile
is unaffected, or it may reflect the loss of takeover targets. However, among smaller
firms, the original result of the event return being driven by firms in the top quartile of
Tobin's
Q still
holds, with such firms experiencing an average event return of -0.99%
(-1.34% excluding utilities). Since these firms are less likely to be acquirers, I can
more confidently attribute this impact to the change in the governance environment.
Thus, consistent with other results in this paper, I find that investors expected
governance to have the greatest effect on firms with the greatest prospects for future
good times. Since this analysis exploits exogenous variation in governance, unlike
other analyses in this paper, it is not subject to the concern that the impact may
be driven by non-agency characteristics of firms. Instead, the event study provides
strong evidence that governance has the greatest impact on performance in good
times and that investors understood this even in the late 1980's and early 1990's.
1.5
Concluding Remarks
This paper documents that the outperformance of well-governed firms relative to
poorly-governed firms in the same industry, where governance is measured by the
G Index, is concentrated in periods when industries are experiencing particularly
37
good times. Well-governed firms generate significantly higher operating incomes than
poorly-governed firms in periods of very high industry profitability, but perform similarly to other firms under other business conditions.
This asymmetric operating
outperformance, which appears to be accounted for in the higher valuations of such
firms, likely explains the more symmetric pattern of abnormal returns. Well-governed
firms experience positive abnormal returns relative to poorly-governed firms in peri-
ods of high returns, but have negative relative abnormal returns in industry downturns. These returns likely reflect changing expectations of capturing the good times
premium that should be accounted for in the prices of stocks of well-governed firms.
Since the observed abnormal returns to governance thus depend on the nature of
the particular period being sampled, these patterns provide a reasonable explanation
for the apparent disappearance of returns to governance after 2001. The results are
robust to wide range of specifications and to controlling for non-agency characteristics.
An event study exploiting the exogenous passage of state-level anti-takeover laws
provides evidence in favor of a causal channel for the pattern. Also, analyst forecast
patterns and the event study provide strong support for the hypothesis that investors
have understood the implications of governance and, over time, have been surprised
by news of good outcomes rather than by observed outperformance conditional on
such outcomes.
Understanding that corporate governance may matter only in good times has
implications for measuring the impact of governance, for optimal governance choices,
and for policy decisions. Additional details of the channel through which shareholder
rights are related to performance, and the interaction of shareholder rights with other
governance mechanisms. should be further explored.
38
1.6
Figures and Tables
Figure 1-1: Adj. Return of High and Low Governance Firms versus Industry Return
15%
10%
-4
40
---
5%
Adjusted Return for
Given Industry Return,
Mean across Industries
-4e
0%0
-15%
-10%
-5%
0-
0*
High Governance Group
-410%
5%
15%
,0
Low Governance Group
Adjusted Return for
Given Industry Return,
Mean across Industries
10% I
Industry Return, Monthly, Unadjusted, Rounded to Nearest 2.5%
Figure 1-2: ROA of High and Low Governance Firms versus Industry ROA
6%
4%
~IA
--
fROA
.0
S600ROA,
9
$- High Governance Group
for Given Industry
Mean across
Industries
--
V/U
a)
'
;%
070
-2%
2%
4%
Industries
6%
2%
Industry Return on Assets,
Quarterly,
Rounded to Nearest
39
Low Governance Group
ROA for Given Industry
ROA, Mean across
0.5%
Table 1.1: Governance Sample Summary Statistics
Returns and operating performance data are analyzed from 1990 to 2008. The full sample of firms
is limited to those included in IRRC publicationsfrom 1990 to 2006, as reported by Gompers, Ishii
and Metrick in their Governance Index data, and which do not involve dual stocks. The samples
used for most analyses are limited to industries in which both the high and low governance groups
are represented (Panel B and C). The sample statistics in Panels A, B, and C have a distribution
because the sample changes over time.
Mean
Panel A. Full Sample
Number of Industries (3-Digit SIC)
Number of Firms per Industry
GIM Governance Index ("G")
BC Entrenchment Index ("E")
240.8
6.0
9.2
2.4
Distribution over Months in Sam~l
SamDle
Median St. Dev. in
240
3
9
2
9.3
11.3
2.7
1.3
Min
Max
222
1
2
0
268
148
19
6
Panel B. Democracy vs. Dictatorship Sample
Number of Industries
21.4
21
4.2
12
30
Number of Firms per Industry'
20.8
11
26.6
1
191
Low Governance Finns (G>13) per Industry
1.7
1
1.3
1
10
High Governance Firms (G<6) per Industry
2.4
2
2.5
1
19
Panel C. High vs. Low Governance Quartiles Sample
Number of Industries
59.4
60
6.2
46
75
Number of Firms per Industryl
11.7
6
18.4
1
182
Low Governance Firms (G>11) per Industry
3.1
2
3.7
1
39
High Governance Firms (G<7) per Industry
3.0
2
3.9
1
31
Panel D. High vs. Low Governance Characteristics
Democracy/Dictatorship
Governance Quartiles
High Gov. Low Gov. T-Stat2 for High Gov. Lov Gov. T-Stat2 for
Mean (at industry-gov. group level):
(G<6)
(G>13)
Difference
(G<7)
(G>11)
Difference
Market Capitalization ($Mil)
5,433
4,320
0.76
7,738
5,195
1.29
Assets ($Mil)
8,274
10,158
-0.93
9,408
11,418
-0.58
Market Beta
0.978
0.977
0.03
1.029
1.054
-0.91
Tobin's Q
1.750
1.457
4.00***
1.830
1.601
3.17***
Long-Term Debt / Assets
0.181
0.204
-1.26
0.188
0.210
-2.37 **
1 - (Book Equity / Assets)
0.590
0.638
-2.15 **
0.563
0.621
-3.79***
Cash & ST Investments / Assets
0.119
0.078
4.03 **
0.109
0.070
5.91***
Age (Years in Compustat)
19
33
-7.88***
22
32
-8.86***
Quarterly:
Cap. Ex. / Assets
0.017
0.014
2.72***
0.016
0.014
2.17 **
Revenue / Assets
0.242
0.228
0.89
0.293
0.299
-0.50
ROA (Oper. Income / Assets)
0.024
0.020
2.81***
0.025
0.024
1.05
ROE (Net Income / Book Equity)
0.026
0.027
-0.44
0.024
0.026
-0.46
5-Yr Revenue Growth
1.092
0.565
3.95***
1.129
0.558
6.33***
1 Includes only those firm-months used to calculate industry returns and thus set industry high/low
period dummies, which include mid-governance (or once mid-governance but never high/low
governance) firms and, only when both high and low governance a-e represented in a given month,
high and low governance firms. In some robustness tests, a narrower or broader group of firms is
used to define the industry returns.
2 Wald test based on full 1990-2008 sample, at industry level,
with robust standard errors clustered
by industry and by quarter. (*.10, **.05, ***.01)
40
Table 1.2: Stock Returns of High vs. Low Governance Firms, 1990-2008
This table reports the coefficients from a regression of the within-industry differential return between governance groups on dummies for the type of
return experienced by the industry in the same month. The left-hand-side variable is the monthly return (adjusted in Panel A, unadjusted in Panel
B) differential, by 3-digit SIC industry, of "high" versus "low" governance firms. Adjusted returns are excess returns adjusted for estimated betas on
market, SMB, HML, and UMD factors as described in Section 1.2.1. Returns of firms in the same industry-governance group are equally-weighted.
Low, mid, and high industry-return months are based on the quartiles of monthly returns specific to each industry from 1990 to 2008. See Section 1.1.2
for more detail on the construction of these industry return quartiles. Coefficients reported in percentage points (i.e., 0.01 is 1 basis point return).
Monthly Return Difference (high governance - low governance. by industry, in percent per month)
Industry Return Dummies
Governance Variable
N
Low
Mid
High
Low
High
(Industry-Month)
(1st quartile)
(2nd and 3rd)
(4th quartile)
PANEL A: Adjusted return difference by industry (high governance - low governance)
G> 13
G <6
4,712
-1.01
*
-0.02
2.38
*
(-3.11)
G > 11
G <7
13.076
-0.87
(-0.08)
*
(-3.42)
I,
G> 9
G < 10
28,443
-0.60
*
(-3.83)
E>4
PANEL B:
E< 1
4,170
E >3
E <2
17,033
E>2
E <3
28,110
-0.98
-0.02
1.81
(-0.11)
(4.62)
-0.03
(-0.43)
*
(-2.25)
-0.08
(-0.39)
-0.61 *
(-4.08)
(5.98)
1.12
*
*
(-0.60)
0.08
(0.67)
0.00
(-0.04)
(5.96)
0.91 *
(1.96)
0.75 *
(2.66)
0.90 *
(4.23)
-0.13
(-0.60)
(6.44)
-0.15
Un-adjusted return difference by industry (high governance - low governance)
G > 13
G< 6
4,712
-1.27
(-3.57)
G > 11
G< 7
13,076
-1.20
*
(-4.52)
G> 9
G < 10
28,443
-0.93
*
(-6.19)
E> 4
E< 1
4,170
-0.92
**
(-2.10)
E > 3
E<2
17,033
E> 2
E<3
Standard errors are robust (clustered at month level).
T-statistics in parentheses. (*.10, **.05, ***.01)
28,110
-0.28
-0.83
(-6.03)
-0.06
2.10
(-0.41)
(5.48)
-0.01
1.28
(-0.14)
(6.42)
-0.24
(-0.98)
0.02
(0.18)
(-1.30)
*
2.64
-0.03
(-a44)
1.09
*
*
**
(2.22)
0.84 *
(3.04)
0.91 *
(4.38)
Full Period
(All Industry Return Types)
0.33
(1.88)
*
0.22
(1.54)
0.11
(1.44)
-0.08
(-0.40)
0.20
(1.91)
0.07
(0.85)
0.27
(1.46)
0.19
(1.25)
0.08
(0.94)
-0.07
(-0.34)
0.14
(1.31)
0.00
(-0aol)
*
Table 1.3: Stock Returns of High vs. Low Governance Firms - Robustness Tests I
This table presents robustness tests of the results in Table 1.2. The approach parallels that in Table 1.2, with the following differences. (1) includes
observations only from 1990 to 2001; (2) includes observations only from 2002 to 2008. In both cases, industry quartiles are also set within these
subperiods. (3) uses market return dummies as the right-handside variables instead of industry returns. (4) is limited to periods of high (above median)
equally-weighted market return; (5) is limited to periods of low (below median) equally-weighted market return. (6) uses 8-digit GICS industries instead
of 3-digit SIC industries. (7) includes all firms available in CRSP for industry calculations rather than excluding those without G Index data. All
specifications use adjusted returns (adjusted for estimated firm betas on market, SMB, HML, and UMD factors, estimated in a pre and post period)
to calculate monthly high minus low governance returns. Coefficients reported in percentage points (i.e., 0.01 is 1 basis point return).
Monthly Adjusted Return Difference (high governance - low governance, by industry, in percent per month)
(5)
(4)
(3)
(2)
(1)
Low Market
High Market
Market Return
Mid Ind/Mkt Return Dummy
High Ind/Mkt Return Dummy
Full Period (any ind/mkt return)
Full Period (any ind/mkt return)
*
-0.93
-1.03
(-3.05)
3.21
1.48
2.71
(4.87)
0.44
(1.45)
(3.84)
0.33
(1.88)
(6.01)
1474
4712
-1.46
*
*
(-2.91)
*
Subsample
(-0.95)
0.10
(0.36)
1.79
(-1.64)
High Ind/Mkt Return Dummy
-0.57
(-1.74)
0.19
(0.86)
(3.58)
0.28
(1.30)
3238
N (Industry-Month)
PANEL B: Governance Quartiles (G<7 and G>11)
-0.47
Low Ind/Mkt Return Dummy
Mid Ind/Mkt Return Dummy
N
(-3.39)
0.15
(0.37)
(-1.66)
0.03
(0.12)
Subsample
Dualmies
2002-2008
1990-2001
PANEL A: Democracies vs. Dictatorships (G<6 and G>13)
-1.93
-0.65 *
Low Ind/Mkt Return Dummy
*
*
(6)
GICS-8
(7)
Industry Includes
Industries
Non-IRRC Firms
-0.67
*
-0.85
**
(-2.38)
0.15
(0.66)
(-1.94)
-0.15
-0.42
(-0.44)
(-2.02)
0.99
1.48
(1.29)
(3.50)
*
1.85
*
*
(4.14)
(-1.69)
(-0.07)
(4.80)
0.33
(1.88)
2412
2300
5753
4712
1.02
*
-0.39
-0.02
-0.47
-0.95
(-0.07)
(-0.95)
(-3.28)
*
*
-0.01
-1.08
*
-0.49
0.00
-0.09
-0.07
-0.26
0.25
-0.01
0.04
(-0.42)
(-0.44)
(-1.24)
(1.13)
0.99
(1.47)
(-0.04)
(0.26)
1.40
**
8439
N (Industry-Month)
Standard errors are robust (clustered at month level).
T-statistics in parentheses. (*.10, **.05, ***.01)
2.46
*
1.05
**
2.02
*
(5.42)
0.22
(1.07)
(2.57)
0.22
(1.54)
(4.43)
0.60 *
(2.59)
4637
13076
6616
1.48
*
1.31
(-0.96)
(4.57)
0.10
(0.79)
(3.32)
0.22
(1.54)
6460
13713
13076
-0.16
**
(-2.08)
(-4.79)
(-0.00)
(2.54)
0.23
(1.15)
**
*
Table 1.4: Stock Returns of High vs. Low Governance Firms - Robustness Tests II
This table presents further robustness tests of the results in Table 1.2. The approach parallels that in Table 1.2, with the following differences. (1) and
(2) exclude "high" and "low" governancefirms altogetherfrom industry return calculations; (2) uses value weights for these industry return calculations
and within the high and low governance portfolios. (3) uses Carhart's PR1YR factor in place of the Fama-French UMD factor; (4) excludes high
tech, telecom, financial and related industries. (5)/(6) is a subsample of industries with above/below median cash flows relative to other industries.
(7) requires a minimum of 10 firms per industry-month observation (for industry return dummy assignment as well as inclusion in regression). All
specifications use adjusted returns (adjusted for estimated firm betas on market, SMB, HML, and momentum factors, estimated in a pre and post
period) to calculate monthly high minus low governance returns. Coefficients reported in percentage points (i.e., 0.01 is 1 basis point return).
Monthly Adjusted Return Difference (high governance - low governance, by industry, in percent per month)
(4)
(3)
(2)
(1)
Industry Return
(1) and
FF+
Excl. Tech and
Excl. H/L Gov Value Weights
PR1YR
Financial
PANEL A: Democracies vs. Dictatorships (G<6 and G>13)
Low Industry Return Dummy
-0.52
-0.36
-0.81 **
-1.52 *
Mid Industry Return Dummy
High Industry Return Dummy
Full Period (any industry return)
(-1.48)
0.16
(0.73)
(-0.99)
0.49
(2.00)
1.50 *
(4.11)
0.33
(1.88)
*
N (Industry-Month)
4712
PANEL B: Governance Quartiles (G<7 and G>11)
Low Industry Return Dummy
-0.50 **
Mid Industry Return Dummy
(-2.03)
0.28
(1.92)
High Industry Return Dummy
0.91
(2.49)
Full Period (any industry return)
0.22
(1.54)
*
**
N (Industry-Month)
12508
Standard errors are robust (clustered at month level).
T-statistics in parentheses. (*.10, **.05, ***.01)
1.09
(3.54)
0.43
(2.56)
4712
**
***
**
(-2.33)
0.04
(0.19)
2.29
(6.05)
0.39
(2.18)
(-3.80)
-0.20
2.51
**
(4.70)
0.12
(0.52)
4712
*
2924
0.07
-0.78
-1.45
(0.29)
0.21
(1.15)
(-2.91)
0.06
(0.40)
(-4.97)
0.38
(1.52)
-0.59
-0.71
(-1.23)
(-0.52)
(-1.92)
0.19
(0.76)
2.46 *
(4.69)
2.28 *
(4.72)
0.24
(0.84)
0.39
(1.70)
0.49
(2.38)
2006
2706
**
(-2.57)
0.10
(0.29)
(-0.69)
*
-1.46
*
(7)
At Least 10
Firms/Industry
(6)
Low CF
Industries
(5)
High CF
Industries
2.26
(3.31)
-0.67
-0.15
*
*
-0.97
*
*
**
3522
*
-0.33
(-2.84)
-0.09
-0.04
(-0.99)
(-1.92)
0.06
(0.27)
(-0.48)
(-0.21)
1.99 *
(5.12)
2.12 *
(4.45)
1.73 *
(4.34)
1.87 *
(3.05)
1.46 *
(4.31)
0.19
(1.42)
0.33
(2.16)
0.08
(0.46)
0.29
(1.76)
0.17
(0.80)
0.26
(1.82)
12508
13076
10034
5914
7162
6880
-0.17
**
*
(-1.35)
*
Table 1.5: Operating Performance of High vs. Low Governance Firms, 1990-2008
This table reports the coefficients from a regression of the within-industry differential operating performance between governance groups on dummies
for the type of operatingperformance experienced by the industry in the same quarter. The left-hand-side variable is the specified quarterly accounting
measure differential, by 3-digit SIC industry, of "high" versus "low" governance firms. High and low governance throughout this table mean G Index
levels of less than 6 or more than 13 respectively. Performance of firms in the same industry and governance group are equally-weighted. The righthand-side variables are dummies correspondingto low, mid, and high levels of the specified (by panel) quarterly accounting measure in the industry,
classified based on the quartiles of quarterly measures specific to each individual industry from 1990 to 2008. See Section 1.1.2 for more detail on the
construction of these industry performance quartiles.
Quarterly Performance Difference (high governance - low governance, by industry)
Governance Variable (Democracies/Dictatorships)
Type of Quarter for the Industry
Low
High
(based on panel accounting measure)
G <6
G > 13
N
Low
Mid
High
Operating Performance Variable
(Industry-Qtr) (1st quartile) (2nd and 3rd) (4th quartile)
PANEL A: Return on Assets Industry Dummies
Return on Assets (operating income to assets)
1,525
-0.0004
0.0031
0.0108
(-0.28)
(2.98) ***
(7.37) ***
(-0.21)
(1.94) *
(4.84) ***
Revenue (revenue to assets)
1.553
0.0001
0.0094
0.0384
(0.01)
(1.37)
(3.06) ***
Operating Profit Margin (op. income to revenue)
PANEL B: Return on Equity Industry Dummies
Return on Equity (income to book equity)
1,535
1,506
Revenue (revenue to assets)
1,553
Net Profit Margin (net income to revenue)
1,563
T-Statistics for Differences
Low - Mid
High - Mid
Diff in Spreads Diff in Spreads
-0.0035
(-1.99)
**
(-1.76) *
-0.0093
(-0.71)
0.0077
(4.06) ***
(3.52) ***
0.0290
(1.88) *
(0.01)
(0.64)
(1.46)
(-0.67)
(1.52)
-0.0043
(-0.25)
-0.0010
(-0.10)
0.0200
(1.28)
-0.0033
(-0.17)
0.0211
(1.13)
(-0.25)
(-0.07)
(1.04)
(-0.23)
(0.84)
-0.0054
-0.0045
(-1.61)
(-0.78)
0.0081
(0.86)
(-1.18)
0.0034
(0.47)
0.0106
(2.44) **
(1.96) **
0.0437
(3.80) ***
-0.0008
(-1.09)
0.0151
(2.89) ***
(2.60) ***
0.0404
(2.82) ***
(1.77)
(-0.15)
(-0.11)
0.0047
(0.38)
(0.55)
(0.23)
(0.46)
(2.15)
0.0039
(0.24)
-0.0124
(-1.25)
0.0398
(3.15) ***
0.0163
(0.91)
0.0522
(3.45)
(0.27)
(-0.81)
(1.22)
(1.12)
(1.27)
*
Standard errors are robust (clustered at quarter level) and either include an AR(1) correction for autocorrelation or are also clustered by industry.
AR(1)-corrected T-statistics in parentheses (above), two-way clustered T-statistics italicized in parentheses (below). (*.10. **.05, ***.01)
**
Table 1.6: Operating Performance of High vs. Low Governance Firms - Robustness Tests
This table presents robustness tests of the results in Table 1.5. The approachparallels that in Table 1.5, with the following differences. (1) includes
observations only from 1990 to 2001; (2) includes observations only from 2002 to 2008. In both cases, industry quartiles are also set within these
subperiods. (3) uses 8-digit GICS industries instead of 3-digit SIC industries. (4) includes all firms available in the CRSP-Compustat merged database
for industry calculations rather than excluding those without G Index data. (5) and (6) exclude "high" and "low" governance firms altogether from
industry ROA calculations; (6) uses value-weights for these industry ROA calculations and within the governance portfolios. (7) excludes high tech,
telecom, financial and related industries. (8) requires a minimum of 10 firms per industry-quarterobservation (for industry ROA dummy assignment
as well as inclusion in regression).
Quarterly ROA Difference (high governance - low governance, by industry)
(1)
(2)
(3)
GICS
1990-2001
2002-2008
Industries
PANEL A: Democracies vs. Dictatorships (G<6 and G>13)
Low Industry ROA Dummy
C.,-
Mid Industry ROA Dummy
High Industry ROA Dummy
Full Period (any ind. ROA)
-0.0030
(-1.54)
0.0022
(1.33)
0.0098 *
(3.41)
0.0025 *
0.0003
(0.08)
0.0084 *
(2.70)
0.0118 *
(2.96)
0.0069 **
(1.74)
(2.39)
N (Industry-Quarter)
1047
478
PANEL B: Governance Quartiles (G<7 and G>11)
Low Industry ROA Dummy
Mid Industry ROA Dummy
High Industry ROA Dummy
Full Period (any ind. ROA)
-0.0021
(-a98)
0.0024
(0.96)
0.0055
(1.78)
0.0020
(0.94)
*
-0.0058
(-2.25)
0.0022
(1.26)
0.0053
(2.00)
0.0011
(0.63)
-0.0011
(-0.37)
0.0000
(-0.02)
0.0062
(1.85)
0.0012
*
(0.59)
1956
**
**
-0.0039 **
(-2.18)
-0.0003
(-0.19)
0.0073 *
(3.55)
0.0006
(0.47)
N (Industry-Quarter)
2809
1524
4529
Standard errors are robust (clustered at quarter and industry level).
T-statistics in parentheses. (*.10, **.05, ***.01)
(4)
(5)
(6)
(7)
(8)
Industry Incl. Industry ROA
(5) +
Excl. Tech and At Least 10
Non-IRRC
Excl. H/L Gov Value Weights
Financial
Firms/Industry
0.0018
(0.78)
0.0041 *
(2.50)
0.0055 **
(2.41)
0.0039 *
0.0022
(1.15)
0.0038 **
(2.26)
0.0060 *
(3.13)
0.0039 *
0.0027
(1.11)
0.0035
(2.02)
0.0046
(2.02)
0.0036
(2.81)
1525
(2.81)
1525
(2.42)
1525
(2.05)
951
(1.75)
1130
-0.0010
(-0.53)
0.0013
(0.81)
0.0051
(2.13)
0.0017
(1.05)
0.0008
(0.49)
0.0007
(0.41)
0.0023
(1.24)
0.0011
(0.81)
0.0006
(0.42)
0.0014
(0.96)
0.0007
(0.33)
0.0010
(0.76)
-0.0035
(-1.64)
0.0015
(0.67)
0.0082
(2.40)
0.0019
(0.95)
-0.0040
(-2.13)
-0.0012
(-0.84)
0.0046
(2.12)
-0.0005
(-0.43)
4127
4126
3333
4333
**
**
**
**
-0.0014
(-0.66)
0.0025
(1.15)
0.0133 *
(4.19)
0.0037 **
**
-0.0004
(-0.15)
0.0021
(1.33)
0.0069 *
(2.90)
0.0026 *
2235
**
**
Table 1.7: Analyst Forecast Patterns, 1990-2008
This table reports the coefficients from a regression of the within-industry differential analyst earnings forecasts or surprises relative to such forecasts between governance groups on dummies for the type of analyst forecasts or surprises corresponding to the industry in the same quarter. The
left-hand-side variable is the specified quarterly measure differential, by 3-digit SIC industry, of "high" versus "low" governance firms. High and
low governance throughout this table mean G Index levels of less than 6 or more than 13 respectively. Forecasts are all mean forecasts across analysts. Performance/forecastsof firms in the same industry and governance group are equally-weighted. The right-hand-side variables are dummies
corresponding to low, mid, and high levels of the specified (by panel) quarterly measure in the industry, classified based on the quartiles of quarterly
measures specific to each individual industry from 1990 to 2008 or subperiods as indicated. See Section 1.1.2 for more detail on the construction of
these industry performance quartiles. Also reported are p-values for the Wald test that all coefficients of the pre-2002 regression are the same as those
of the post-2001, based on the two-way clustered standard errors.
Quarterly Difference (high governance - low governance, by industry)
Governance Variable (Democracies/Dictatorships)
Low
High
N
G<6
G > 13
Operating Performance / Forecast Variable
(Industry-Quarter)
PANEL A: Forecast Income / Book Equity Industry Dummies
Full Period: Forecast Income / Book Equity
1,099
Pre-2002: Forecast Income / Book Equity
722
Post-2001: Forecast Income / Book Equity
377
PANEL B: Analyst Surprise Industry Dummies
Full Period: Actual - Forecast Income / Book Equity
1,099
Type of Quarter for the Industry
(based on panel-specific measure)
High
Mid
Low
(1st quartile)
(2nd and 3rd) (4th quartile)
-0.0057
(-1.71)
(-1.34)
-0.0070
(-1.49)
(-1.59)
-0.0058
(-1.29)
(-0.81)
*
0.0003
(0.13)
(0.09)
0.0027
(1.00)
(0.80)
0.0019
(0.74)
(0.40)
-0.0247
0.0002
(-3.02) ***
(0.17)
(0.17)
(-2.41) **
Pre-2002: Actual - Forecast Income / Book Equity
722
-0.0329
-0.0007
(-3.16) **
(-0.38)
(-2.75) ***
(-0.36)
Post-2001: Actual - Forecast Income / Book Equity
377
-0.0047
0.0016
(-0.39)
(0.77)
(-0.27)
(0.79)
Standard errors are robust (clustered at quarter level) and either include an AR(1) correction for autocorrelation or
AR(1)-corrected T-statistics in parentheses (above), two-way clustered T-statistics italicized in parentheses (below).
0.0165
(2.14) **
(1.54)
0.0112
(1.36)
(1.15)
0.0145
(0.92)
(0.59)
P-value for Wald Test
Pre vs. Post Sample
0.98
0.0054
(2.65) *
(2.59) ***
0.0035
(1.57)
(1.94) *
0.0082
(2.33) **
(1.71) *
0.33
are also clustered by industry.
(*.10, **.05, ***.01)
Table 1.8: Valuation and Investment Patterns, 1990-2008
This table reports the coefficients from a regression of the within-industry differential Tobin's Q or investment between governance groups on dummies
for the level of Tobin's Q corresponding to the industry in the same quarter. The left-hand-side variable is the specified quarterly accounting measure
differential, by 3-digit SIC industry, of "high" versus "low" governance firms. Performance of firms in the same industry and governance group are
equally-weighted. The right-hand-side variables are dummies corresponding to low, mid, and high levels of the Tobin's Q in the industry, classified
based on the quartiles of quarterly Q specific to each individual industry from 1990 to 2008. See Section 1.1.2 for more detail on the construction
of these industry quartiles. Q is calculated as the market value of assets - estimated as the total book value of assets plus the market value of equity
minus book value of equity incl. deferred taxes - divided by the total book value of assets.
Quarterly Difference (high governance - low governance, by industry)
Type of Quarter for the Industry
(based on Q-Ratio)
N
Low
Mid
High
T-Statistics for Differences
Low - Mid
High - Mid
Investment/Performance Variable
(Industry-Quarter) (1st quartile) (2nd and 3rd) (4th quartile)
Diff in Spreads Diff in Spreads
PANEL A: Democracies vs. Dictatorships (G<6 and G>13)
Tobin's Q
1,556
0.1456
0.3108
0.4352
-0.1651
0.1244
(3.71) ***
(6.66) ***
(5.22) ***
(-2.59) ***
(1.27)
(2.27) **
(3.63) *
(3.31) *
(-2.11) **
(1.16)
CAPEX to Assets
1,273
0.0011
0.0038
0.0035
-0.0027
-0.0003
(0.87)
(4.64) ***
(3.04) *
(-1.85) *
(-0.21)
(0.71)
(2.88) ***
(2.33) **
(-1.72) *
(-0.19)
Percent Change in Net PPE
1,518
0.6188
0.3878
2.0510
0.2310
1.6632
(1.17)
(0.89)
(3.46) ***
(0.34)
(2.31) **
(1.04)
(0.86)
(3.16) ***
(0.33)
(2.21) **
PANEL B: Governance Quartiles (G<7 and G>11)
Tobin's Q
4,356
0.0807
0.1530
0.5584
-0.0723
0.4054
(5.14) *
(-1.63)
(8.64) ***
(3.99) ***
(3.36) ***
(1.92) *
(2.15) **
(3.52) ***
(-1.24)
(3.22)
CAPEX to Assets
3,890
0.0022
0.0017
0.0026
0.0005
0.0009
(3.92) ***
(4.70) ***
(4.79) ***
(0.77)
(1.25)
(1.93) *
(1.83) *
(2.33) **
(0.65)
(1.13)
Percent Change in Net PPE
4,342
0.1325
0.7782
0.9978
-0.6457
0.2196
(0.42)
(3.40) ***
(2.42) **
(-1.85) *
(0.46)
(0.48)
(-1.59)
(2.32) **
(3.21) ***
(0.35)
Standard errors are robust (clustered at quarter level) and either include an AR(1) correction for autocorrelation or are also clustered by industry.
AR(1)-corrected T-statistics in parentheses (above), two-way clustered T-statistics italicized in parentheses (below) (*.10, **.05, ***.01)
Table 1.9: Lagged Investment vs. Profitable Periods, 1990-2008
This table reports the coefficients from a regression of the within-industry differential investment between governance groups on dummies for the type
of operating performance experienced by the industry in a following quarter. The left-hand-side variable is the specified lagged differential in capital
expenditures to assets, by 3-digit SIC industry, of "high" versus "low" governance firms. Investment of firms in the same industry and governance
group are equally-weighted. The right-hand-side variables are dummies corresponding to low, mid, and high levels of ROA in the industry, classified
based on the quartiles of quarterly ROA specific to each individual industry from 1990 to 2008. See Section 1.1.2 for more detail on the construction
of these industry performance quartiles.
Difference in Investment (high governance - low governance, by industry)
o
Type of Quarter for the Industry
(based on ROA)
N
Low
Mid
High
Lagged Investment Variable
(Industry-Period) (1st quartile) (2nd and 3rd) (4th quartile)
PANEL A: Democracies vs. Dictatorshiips (G<6 and G>13)
CAPEX to Assets : Quarters -1 to -4
807
0.0102
0.0117
0.0199
(1.22)
(2.40) **
(3.60) ***
(1.04)
(2.17) **
(3.26) ***
CAPEX to Assets: Quarters -5 to -12
391
0.0182
0.0237
0.0400
(1.21)
(1.81) *
(2.95) ***
(0.80)
PANEL B: Governance Quartiles (G<7 and G>11)
CAPEX to Assets: Quarters -1 to -4
2,864
0.0124
(3.84)
(2.33)
CAPEX to Assets: Quarters -5 to -12
1,816
(1.49)
***
**
0.0103
(5.61)
(2.41)
(2.08)
***
**
0.0088
(3.60)
(1.66)
**
***
*
T-Statistics for Differences
Low - Mid
High - Mid
Diff in Spreads Diff in Spreads
-0.0015
(-0.16)
(-0.19)
-0.0056
(-0.29)
0.0082
(1.16)
(1.21)
0.0163
(0.88)
(-0.39)
(0.68)
0.0020
(0.53)
-0.0016
(-0.50)
(0.63)
(-0.55)
0.0263
0.0320
0.0276
-0.0057
-0.0044
(4.58) ***
(7.10) ***
(4.12)
(-0.79)
(-0.51)
(2.08) **
(2.96) ***
(1.88) *
(-1.11)
(-0.49)
Standard errors are robust (clustered at quarter level) and either include an AR(1) correction for autocorrelation or are also clustered by industry.
AR(1)-corrected T-statistics in parentheses (above), two-way clustered T-statistics italicized in parentheses (below) (*.10, **.05, ***.01).
Table 1.10: Characteristic-Corrected Return and Operating Income Patterns, 1990-2008
This table reports the coefficients from a regression of the within-industry differential performance between governance groups on dummies for the type
of performance experienced by the industry in the same period. The left-hand-side variable is the specified performance differential, by 3-digit SIC
industry, of "high" versus "low" governance firms. High and low governance throughout this table mean G Index levels of less than 6 or more than 13
respectively. Characteristic-corrected
performance is the residual from a regression of all firms' given performance statistic minus their industry-period
mean on several industry-period-demeanedcharacteristics, each interacted with dummies corresponding to low, mid, and high levels of the industry
performance statistic. The characteristics included in the first stage correction are log market capitalization, market to book ratio, 5-year revenue
growth, and 3-year capital expenditures to assets. The right-hand-side variables in the specifications below are dummies corresponding to low, mid,
and high levels of the specified (by panel) performance measure in the industry, classified based on the quartiles of periodic measures specific to each
individual industry from 1990 to 2008. See Section 1.1.2 for more detail on the construction of these industry performance quartiles. Periods are
months in Panel A and quarters in Panel B. Performance of firms in the same industry and governance group are equally-weighted. Returns are
expressed in percentage points.
Performance Difference (high governance - low governance, by industry)
Governance Variable (Democracies/Dictatorships)
Low
High
G > 13
G <6
N
Performance Variable
(Industry-Period)
Type of Period for the Industry
(based on panel measure)
Low
Mid
High
(1st quartile) (2nd and 3rd) (4th quartile)
Full Period
(Any Industry
Performance)
PANEL A: Monthly Industry Return Dummies
Monthly Adjusted Returns
Characteristic-Corrected Monthly Adj. Returns
4,712
2,438
-1.01
-0.02
2.38
0.33
(-3.11)
(-0.08)
(5.98)
(1.88)
-0.91
(-1.78)
PANEL B:
Quarterly
Quarterly ROA
*
*
-0.25
2.20
(-0.79)
(3.01)
(0.61)
0.0108
(4.84) ***
0.0085
(2.48) **
0.0039
(2.81)
0.0059
(2.91) ***
*
0.17
Industry ROA Dummies
1,525
-0.0004
0.0031
(-0.21)
(1.94) *
Characteristic-Corrected Quarterly ROA
803
0.0037
0.0060
(2.78) *
(1.53)
Standard errors are robust (clustered at period level); for Panel B, they are also clustered at the industry level.
T-statistics in parentheses (*.10., **.05., ***.01).
Table 1.11: Event Study - Business Combination Laws
The numbers reported in the table are average portfolio cumulative abnormal returns (CARs), for 19 state-of-incorporationportfolios, upon first
newspaper report of the business combination law in that state (rangingfrom 1985 to 1991). The event horizon is trading days -1 to 0 relative to such
report. CARs and the accompanying standard errors are calculated using market betas and forecast errors determined for each state-quartile portfolio
(or state-size-quartileportfolio) from trading days -241 to -41. The sample includes a total of 3,921 firms (3,787 firms when excluding utilities) across
the 19 states, which are Arizona, Connecticut, Delaware, Georgia, Illinois, Kentucky, Maryland, Massachusetts, Minnesota, New Jersey, New York,
Ohio, Oklahoma, Pennsylvania, Tennessee, South Carolina, Virginia, Washington, and Wisconsin. Tobin's Q quartile and median size cutoffs are
specific to each individual state. Tobin's Q is measured 5 days before the event, with the required accounting variables interpolatedfrom the prior and
next quarter end. Size is measured as market capitalization 5 days before the event. CARs are reported in percentage points (i.e., 0.01 is 1 basis point
return).
2-Day CAR [-1, 0], reported in percentage points (0.01 = 1 basis point return)
Full sample
Above median size
Below median size
Excluding utilities
Above median size
Below median size
Z-statistics in parentheses (*.10, **05, ***.01)
All Firmk
-0.234
(-2.19)
-0.286
(-1.92)
-0.188
(-1.17)
-0.251
(-2.27)
-0.287
(-1.94)
-0.227
(-1.25)
**
*
**
*
Lowest
Quartile
-0.084
(-0.13)
-0.252
(-1.03)
-0.012
(-0.09)
-0.168
(-0.32)
-0.177
(-1.01)
-0.061
(-0.25)
Tobin's Q Quartiles, As of Day -5
Third
Highest
Second
Quartile
Quartile
Quartile
-0.149
-0.088
-0.6067
(-0.6J)
(-0.63)
(-2.34)
0.078
-0.888
*
-0.0757
(-0.14)
(-1.81)
(-1.22)
0.023
0.265
-0.9920
(-0.24)
(-0.31)
(-1.97)
-0.107
-0.098
-0.6057
(-0.47)
(-0.75)
(-2.29)
-0.112
-0.772
-0.0866
(-0.67)
(-1.52)
(-1.29)
-0.033
0.447
-1.3387
(-0.31)
(0.01)
(-1.93)
**
**
**
*
Chapter 2
The Impact of Shareholder
Disagreement: Evidence from
Spin-Offs and Mergers
In a spin-off transaction, a corporate subsidiary is established as a separate company
whose equity is distributed proportionately to the shareholders of the parent company. Such transactions, which are generally tax-free and involve no cash payments,
have been associated with average wealth gains of 3 to 6 percent of the value of the
joint enterprise.1 A stock merger is essentially the reverse situation, whereby a target
company is acquired by issuing stock of the acquiring company to the target's shareholders in consideration for their target stock. Again, no money changes hands and
there are generally no tax liabilities generated.
Such stock acquisitions have been
associated with negative returns to acquirers on the order of -2 percent. 2
In this
paper, I find that a portion of the gains and losses derived from separating or joining
securities through spin-offs and stock mergers can be attributed to shareholder disagreement about the relative prospects of the two entities, rather than the business
impacts of these transactions.
'See, e.g., Hite and Owers (1983), Schipper and Smith (1983), Miles and Rosenfeld (1983), Vijh
(1994), Daley, Mehrotra and Sivakumar (1997), Burch and Nanda (2003), Gertner, Powers and
Scharfstein (2002)).
2
See, e.g., Andrade, Mitchell and Stafford (2001).
51
Miller (1977) laid out the basic theoretical case for a stock price impact of disagreement, arguing that constraints on short sales prevent some investors' views on a
stock from being incorporated in its price, and thus that a stock price is determined
by relative optimists or by the clientele that prefers a particular security. Evidence
of such effects on stock prices and corporate policies have been demonstrated in the
case of dividend clienteles. 3 However, more general disagreements about a company's
prospects have been harder to observe and analyse. One approach has been to proxy
for investor differences of opinion with variables such as the dispersion among analyst earnings forecasts, idiosyncratic volatility, or trading volume, but these measures
could also be interpreted as proxies for other important factors such as risk and uncertainty.4 Chen, Hong and Stein (2002) alternatively proxy for disagreement with
the narrowness of the investor base, measured by the fraction of all mutual funds
holding a stock, but this measure may also capture factors such as the monitoring
effect of investor concentration.
I present a more direct approach to measuring such general disagreement in the
context of corporate restructuring transactions. Specifically, I focus on spin-offs and
stock mergers, and create a measure of shareholder disagreement based on preferences
that institutions reveal through their holdings before and after such transactions. For
example, after a spin-off, if some of the original shareholders of the joint company
choose to hold only the parent stock, while others choose to hold only the spun
entity, it is likely that these two groups disagree about the prospects of the two
enterprises. In this way, a greater separation of the shareholder base upon division of
the firm indicates a higher level of disagreement among shareholders. Thus, I measure
disagreement based on the lack of overlap of these shareholder bases, and then relate
this measure to the price impact of separating (or joining) two businesses.
The theoretical motivation for this relationship is provided through a formalization
of Miller's (1977) theory about the impact of disagreement, which demonstrates that
3
See, e.g., Perez-Gonzalez (2003), Graham and Kumar (2006) and Hotchkiss and Lawrence (2007).
4 See, e.g., Boehme, Danielsen and Sorescu (2006) and Diether, Malloy and Scherbina (2002) for
examples of such approaches, and Johnson (2004) and Jones, Kaul and Lipson (1994) for related
criticisms.
52
disagreement often leads to a positive price impact when securities are separated as
in a spin-off (or a negative impact in a merger) when some investors face binding
short-sales constraints. Importantly, not all investors and not all securities need to
have short-selling restrictions for these results to hold. Also, only disagreement that
leads investors to choose not to hold at least one of the securities is related to the price
effect; investors who simply change the proportion of their holdings do not impact
prices.
Consider, for example, the notorious case of the spin-off of Palm from 3Com. In
March 2000, 3Com sold 5% of its Palm subsidiary in an IPO, and announced its
intention to distribute the remaining 95% of Palm to its shareholders in a spin-off
later that year. After the IPO, Palm shares were difficult and costly to borrow for the
purpose of short-sales, and the market price of 3Com (which still held 95% of Palm)
was significantly less than the implied value of its Palm stake. Importantly, only 13%
of the original institutional shareholders of 3Com ended up holding any Palm stock
once it was fully spun off. This high separation of the shareholder bases, together with
the effective constraints on short-selling, may help to explain the extreme divergence
between the initial independent pricing of Palm and the pricing of 3Com. 5
The 3Com-Palm case also seemed to involve some additional frictions or perhaps
irrationality on the part of Palm investors (since their implied valuation of the rest of
3Com was actually negative), but investor disagreement need not imply irrationality.
Differences of opinion may also arise from asymmetric information or differing investor
priors. There may also be structural reasons for investor clienteles, as when segmented
markets or regulatory requirements restrict some investors from holding particular
stocks. Such restrictions would directly lead to demand functions of the sort that
would be derived from disagreement, so this more exogenous source of clienteles will
lead to the same theoretical and empirical results that I attribute to disagreement.6
5
6
See Lamont and Thaler (2003) for more detail on the 3Com-Palm transactions.
However, other researchers have had limited success in attributing the price changes I explore here
to such structural clienteles. See, e.g., Abarbanell, Bushee, and Raedy (2003), who use factor and
cluster analysis on past investment behavior to classify institutions into large-value, large-growth,
small-value, and small-growth styles. They find that these classifications are predictive of trading
decisions upon receiving a spin distribution, but that the trading that results does not seem to drive
price movements.
53
Thus, I will continue to frame my conclusions in terms of investor disagreement, with
the understanding that structural clienteles would theoretically have similar impacts.
While understanding the underlying cause of disagreement (or shareholder clienteles) would undoubtedly be interesting, the main goal of this paper is to explore the
impact of such disagreement on stock prices. Empirically, it is important to isolate
such effects from the many business impacts of the transactions that I consider. For
example, spin-offs may undo the effects of inefficient internal capital markets, may
reduce information asymmetry by increasing subsidiary-level reporting, may allow for
better incentivization of subsidiary managers, may increase the effectiveness of parent company managers through an increase in operational focus, may transfer wealth
from bondholders to shareholders, and may remove conflicts of interest that prevent
or complicate relationships with particular counterparties. 7 If the overlap of shareholder bases is related to any of these other effects, relating my disagreement measure
to the full value created in a spin-off might capture some of these business impacts
together with the direct price effects of disagreement.
These potential confounding factors lead me to focus primarily on the ex date
returns of spin-off transactions, which are the situations in which the price impact
of disagreement is most cleanly identified. The ex date is the first date on which a
parent company and the newly spun company are traded as separate securities. As
investors shuffle their holdings to rebalance into their preferred securities, the first
direct evidence of actual disagreement may be observed and, to the extent that the
actual level of disagreement has not been fully anticipated, may impact prices. At
the same time, the timing of the ex date allows me to isolate a price impact that
is unrelated to the various possible business effects of a spin-off. SEC rules 8 require
at least 20 days to pass between the mailing and distribution of the information
statement provided to shareholders - which includes a discussion of the management's
rationale for the transaction, details of the structure of the spin-off, and pro forma
7
See, among others, Aron (1991), Krishnaswami and Subramaniam (1999), Gertner, Powers and
Scharfstein (2002), Maxwell and Rao (2003), and Fulghieri and Sevilir (2011) for examinations of
some of these possibilities.
8
See SEC Rule 14c-2.
54
financial information for the company to be spun off - and the completion of the
transaction. Also, as in the case of a cash dividend, a spin-off ex date is pre-announced
on a declaration date, so there is no remaining uncertainty of transaction completion
on this date. Thus, there is no new information revealed about the business aspects
of the transaction on the ex date, and investors would have already incorporated into
prices any of the anticipated business impacts mentioned above.
My shareholder disagreement variable, equal to the ratio of continuing investors
who choose to hold only one component after the spin-off, is a significant predictor of
the ex date excess return. A one standard deviation increase in this ratio is related to
65 to 125 basis points of additional return. This effect does not reverse in the 10 days
following the ex date, and when the disagreement measure is broken down into the
component that is predicted by differences in size, industry, and Tobin's
Q
and the
residual disagreement, it is the unpredicted component that drives the ex date effect.
While the ex date provides the best-identified effect, it is possible that the anticipated
part of disagreement is incorporated into stock prices before the ex date. However,
I find no relationship between my revealed disagreement measure and returns on the
announcement date or between the announcement and ex date. Thus, the price effect
of disagreement appears to be concentrated on the ex date, as shareholders reveal
their opinions in their trading patterns.
I next consider stock mergers, a natural extension from spin-offs in that they
represent the combination rather than the separation of two stocks.'
Since spin-
offs are not randomly assigned, and in fact may be most likely to happen when
disagreements about the two businesses are particularly high, mergers also provide
an alternative situation in which to explore disagreement at levels which may be
less extreme. However, mergers do not provide me with as clean of an identification
strategy because the two securities are already tradable in any combination at the
announcement date. Still, if some investors wait to reshuffle their holdings until the
ex date, perhaps because target shareholders do not pay attention until they actually
9
1n fact, Allen, Lummer, McConnell and Reed (1995) consider spin-offs that follow an earlier acquisition of the business that is spun and find that losses in the original acquisitions are related to gains
in the eventual spin-offs.
55
receive acquirer shares, I might still find an impact of disagreement on ex date returns.
In fact, I do find that my measure is a significant predictor of ex date returns, such
that a one standard deviation increase in the ratio of continuing investors who held
only one component before the merger is related to a 20 to 40 basis point lower return,
though there is evidence of reversal thereafter in the case of very small acquisitions.
In contrast to my results for spin-offs, however, there is a negative relationship
between my measure and merger returns before the ex date as well, for a total (including the effect of partial reversal for small deals) of 10 to 400 basis points of lower
return for a one standard deviation higher level of my disagreement measure. This is
consistent with the fact that both stocks are separately tradable at any time until the
ex date, so shareholders who disagree about the prospects of the two firms can trade
in reaction to news of the merger at any time after the announcement. Of course, on
these other dates, my measure may also be proxying for business-related information.
Still, while I cannot confidently attribute the full effect to disagreement, the results
for the longer period provide a high water mark for the total impact of disagreement.
This chapter is organized as follows. The next section presents the theoretical
motivation for my approach. Then, Section 2.2 provides details on the data used and
the samples analyzed. Sections 2.3 and 2.4 describe my results for corporate spin-offs
and stock mergers respectively. Concluding remarks are offered in Section 2.5.
2.1
Theoretical Motivation
Miller (1977) theorizes that in the presence of short-sales constraints, equity issues
tend to be held by those who are more optimistic about them, leading to higher
prices, lower returns, and a potential to increase stock market valuations by catering
to particular clienteles. Jarrow (1980) examines this proposition more formally and
finds that disagreement about expected payoffs of assets together with short-sales
constraints would result in higher asset prices when asset payoffs are uncorrelated
or when investors agree upon the variance-covariance matrix of the these payoffs.1 0
10
To the extent that disagreement is rational, and related either to differing priors or asymmetric access
to information, Williams (1977) argues that disagreement in means is more likely to persist than
56
Building on Jarrow's results, I find that unbundling assets when there is disagreement
about asset payoffs (but agreement about the variance-covariance matrix) and when
investors face short-sales constraints often results in higher asset prices.
2.1.1
Model Set-Up
Following Jarrow (1980), I begin with a single period mean variance model in the
style of Lintner (1969) and extend it to incorporate short sales restrictions and the
bundling of assets. Prices are determined and all trading occurs at time zero, such that
investors maximize their expected utility over terminal wealth at time one. Further,
Al. There are no transactions costs or taxes, assets are infinitely divisible, and all
investors act as price takers.
A2. Asset payoffs (and thus asset returns) follow a multivariate normal distribution
as seen by each investor.
A3. The risk-free rate is exogenously determined, and borrowing and lending at this
rate is unlimited.
A4. Investors are risk averse and exhibit non-satiation.
A5. Short sales restrictions (or minimum holding constraints, which can be positive
or negative) may apply to some or all assets for some or all investors. 1
A6. Investors may have heterogeneous beliefs regarding the expected payoff of any
risky asset and/or the variance-covariance matrix of these payoffs. 1 2 The variancecovariance matrix, as seen by each investor, is of full rank.
disagreement in variances and covariances. That is, given the ability to learn from observed returns,
and assuming continuous trading and information processing, he demonstrates that variances and
covariances can be estimated to any desired degree of accuracy while means cannot be estimated
without error from observed returns.
"While the minimum required holding can be positive or negative, the sum across investors of the
minimum units required to be held of a given risky asset must be less than or equal to the supply
of that asset.
12
For most of the following analysis, A6 will be tightened to assume agreement on the variancecovariance matrix of payoffs.
57
A7. Investors exhibit constant absolute risk aversion.
A8. For each investor, the original units endowed of (risky) assets 1 and 2 is equal.
A9. In the bundle equilibrium, a unit of asset 1 may be traded only as a non-separable
bundle with a unit of asset 2.
Assumptions A1-A7 are consistent with Jarrow (1980), though A5 has been generalized. Assumption A8 is necessary in order to compare equilibria with and without
requiring these two assets to be traded only as a bundle, as per A9.
The market has K investors, indexed by k = 1, ..., K, and N risky assets, indexed
by i or
j
= 1, ..., N. The Pratt-Arrow coefficient of absolute risk aversion for investor
k is ak. The number of units of asset i endowed to investor k is denoted as z, with
zk'
=
[z,
... ,
Zk]
representing the vector of risky assets endowments. Total population
endowments are assumed to be -' = Ezk' = el' = [1, ... , 1], a scaling assumption that
k
is made without loss of generality. After trading has concluded at time 0, investor k
holds xk units of the risk-free asset, with the vector xk giving their holdings of the
risky assets. The minimum permitted holding by investor k of asset i is c
ci=
; 0 (e.g.,
0 in case of no permitted short sales by this investor in this asset). As in the
case of assumption A8 for endowments, the short sales constraint on risky asset 1
and risky asset 2 is held the same, i.e., ck
=
ck for any given investor, so that the
bundled and unbundled markets are comparable. The price of a unit of asset i at
time 0 is denoted pi, where po, the price of the risk-free asset, is assumed to be 1,
another scaling made without loss of generality, and the vector of risky asset prices
is P' = [P1, ... , PN].
The payoff per unit of asset i at time 1 is multivariate normally distributed and
denoted as
fi.
Investor k's expectation of the payoff for asset i is Pj = Ek[fi], with the
vector of expected payoffs of the risky assets denoted as
=
... ,
Pk ].
Investor
k believes the variance-covariance matrix of these payoffs to be Qk with elements rj .
The payoff per unit of the risk-free asset, yo = fo, is agreed upon by all investors.
58
2.1.2
Equilibrium Prices without Bundling
In the unbundled equilibrium, investor k solves:
k
max
kl
_akk
xOkPO + E
f Pi -
E(2.1)
subject to
S
Z k+ =zk +
z pk
i
(2.2)
i
and
C,i> i =
x
(2.3)
1, ..., N
The objective function follows from constant absolute risk aversion and the multivariate normal distribution of asset payoffs. The budget constraint in (2.2) is stated
with equality given non-satiation. The short-sales constraints in (2.2) may vary by
investor, with ci =
in case of no limitations on short sales for this investor in
-o
this asset. Denoting the non-negative Lagrangean multipliers as
9 k,
the shadow cost
of the budget constraint, and A , the shadow cost of each short-sales constraint, the
first order conditions for the optimization problem are:
J=
k-
a kE
+ A = 0,i = 1,..., N
-kp,
_
(2.4)
JL
6L
Jgk
k
-Z0+
k k
JZ
(2.5)
= 0
= p'to -
=
k
~
X0
0
(2.6)
_
i
i
and the Kuhn-Tucker conditions
Ai
- i = 0, Ai
,
i "0
Taking into account (2.5), (2.4) can be rewritten in matrix notation as
59
(2.7)
ak7kxk = A k
pop + Ak
-
(2.8)
Note that (2.6), the budget constraint, will be satisfied through the choice of
xo, since there are no restrictions on borrowing and lending. Thus, we can solve for
equilibrium by setting the sum across individuals of the demand for risky assets equal
to the aggregate supply of risky assets. The aggregate demand for the risky assets
(based on the optimal individual quantities derived from (2.8)) is 13
5xk*
k
{1
=
Qk]1
(Ilk
(2.9)
kp±+A k)}
k
Since the aggregate supply of each risky asset was normalized to 1, setting the
above equal to a vector of l's and solving for prices gives us
P* =O
[Qk}]
{1
-1[]
k
+ Ak)
- e]
(2.10)
For the special case of agreement on the payoff variance-covariance matrix, or
Qk
= Q for all k, (2.10) simplifies to
[P
p*
1
[
1 (k+
(2.11)
Ak)}Qe1]
or for an individual risky asset
-
-
1-1
PP =[1
([Z
+ A) -
ai
(2.12)
Note that in the absence of short sales restrictions or other minimum holding
constraints, the expression for the equilibrium price of asset
j
would be the same as in
(2.12) except that the A term would not appear. Thus, these results are consistent
with the finding by Jarrow (1980) that, with disagreement about risky asset payoffs
but agreement on the variance-covariance matrix, the equilibrium price of an asset in
13
The expression in (2.9) is not an explicit demand function because each Ak is a function of the price
vector, but it does usefully characterize demand for the exposition that follows.
60
the presence of short sales constraints is greater than or equal to the equilibrium price
of that asset in the absence of such constraints, and is strictly greater when short sales
are restricted as long as at least one investor faces a binding short sale constraint (that
is, A is positive for at least one investor). This conclusion does not follow in the case
of generalized disagreement about the variance-covariance matrix because, in (2.10),
the impact of the shadow costs in the expression for the price is ambiguous once they
are multiplied by coefficients from the inverse variance-covariance matrices.14
2.1.3
Equilibrium Prices with Bundling and Comparisons
The equilibrium from Section 2.1.2 can now be compared to the equilibrium in a
market where risky assets 1 and 2 are joined in an inseparable bundle. As discussed
above, the endowments and short sales constraints of these two assets were always
held in proportion, zk
=
zk and ck = ck, in order to ensure that this market is
otherwise comparable to that in the unbundled case.
The subscript b is used to
denote variables in the bundled economy and the subscript u to denote variables in
the unbundled economy (where any common parameters are not given a subscript).
The subscript b is also used for the asset bundle comprised of one unit of asset 1 and
one unit of asset 2 (so, e.g., x
X b = X).
First consider the case where investors agree on the variance-covariance matrix
and there are no short-selling constraints, that is, Qk = Q and ck = -00 for all k and
all i. In this case, the unbundled market equilibrium prices are (as per (2.12) above,
but without short-selling constraints):
-
[]-1
POu
k
, j = 1, .. ,N
0~i
_ki
(2.13)
.
while the prices in the bundled equilibrium can similarly be shown to be:
14 Jarrow (1980) shows that the conclusion is, however, robust to a special case in which there is
disagreement about variances but the assets payoffs are uncorrelated with each other. This is not
the case for our conclusions about the price effects of bundling and unbundling assets.
61
[z
PP+
L..kI
Pbb-
Lk J
ck
(2.14)
(Oi + Oi2)
_=
k
ci l
and
kk
,..,N(.5
,1
-
1-
= F
Comparing (2.13) and (2.14) we see that, in this case,
Pbb =
1U
+ P2u
so in the absence of short-sales constraints and when there is agreement on the
variance-covariance matrix, there is no difference between the price of the bundle in
the bundled equilibrium and the sum of the prices of the individual bundle components in the unbundled equilibrium. There are also no changes to the prices of any
other assets.
Now we can introduce short sales constraints. Consider the case where Qk
-
Q
and ci = 0 for all k and i=1,...,N. 1 5 Then our bundled and unbundled prices are
derived from (2.12) above to be
+ A k)
(k
P
={
k
[E
-1b
PP
-
jI
1( + 1k2 +A bb} -Z(oii
k
1
2
1, ..., N
(2.16)
+ Oi2)
(2.17)
1
b
k
(k+Ab,;--=,...,
k
N
(2.18)
This time, from (2.16) and (2.17) we have
bb
Pbb - (P1U+P2)=
15
(u+2u
(2.19)
The assumption that all investors face short sales restrictions on all risky assets (c = 0 for all k and
i=1,...,N) can be relaxed as long as at least one of the investors has a short sale constraint (which
may be a limit on the amount of short-selling rather than a restriction from short-selling) that binds
on one of the two unbundled assets but not the other such asset.
62
Since yo and all a k are positive, the direction of the change in price depends on
the weighted average of the A k - (A k+
A k) terms. When short sales constraints bind
on only one of the two unbundled assets for some individuals, this term is often less
than zero, meaning that the price impact of bundling is negative. It is possible for
bundling to have a positive price impact through the second-order effects of changes
in prices on assets outside of the bundle (since, as a result of the change, holdings of
these assets may also be rebalanced and are also assumed to be subject to short sales
constraints). Additional details on some conditions that would guarantee a negative
price effect of bundling and an example of the type of situation which would give rise
to a positive price effect are provided in the Appendix to this chapter.
In addition to determining the overall price effect from bundling in the presence
of (binding) short-sales constraints, we can also identify the individuals, by their observed holdings, that will contribute to this difference one way or the other. Investors
who do not face short sales constraints do not contribute to the price change. The
possible groups of investors who face short sales constraints are as follows:
1. Hold bundle in bundled equilibrium, and hold both component assets in the unbundled equilibrium - For these individuals, A k=
Ak = A
= 0, as their short
sales constraints in these assets are never binding, so they do not contribute to
the price differential at all.
2. Hold bundle in bundled equilibrium, but only one component asset in the unbundled equilibrium - For these individuals, A k = 0 but A k + A k
0 , so they
generally contribute negatively to the price differential from bundling (as long
as their short sales constraint in their undesired component is binding).
3. Hold bundle in bundled equilibrium, but hold neither component asset in the
unbundled equilibrium - For these individuals, the new unbundled prices are too
rich to attract their investment anymore. For them, Ak = 0 but Ak
+ Ak
>
0
and they generally contribute negatively to the price differential from bundling
(again, as long as one of the constraints is binding).
63
4. Do not hold bundle in bundled equilibrium, and hold neither component asset in
the unbundled equilibrium - For these individuals, A' > 0 and A k + Aik
0.
In this case, the contribution is a second order effect and its sign depends on
how the portfolio rebalancing of other individuals impacts prices (of the bundle
assets as well as other assets in the economy that are correlated with them). For
example, if unbundling results in a higher total price for the two bundle assets,
because of the contributions of the previous investor groups, these individuals
may have a higher total shadow cost of not being able to sell the (now more
expensive) assets.
5. Do not hold bundle in bundled equilibrium, but hold one component asset in the
unbundled equilibrium - For these individuals, the second asset in the bundle
is too undesirable to attract investment in the bundle even though they like
one component. For these individuals, Ak > 0 and A k + Ak,)
0, and they
contribute negatively to the price differential from bundling as long as the increased desire to sell the undesired asset once it is separated from their favored
asset dominates any second order effects through market changes in other asset
prices that are correlated with them.
2.1.4
Key Implications of Theory for Empirics
Holding all else constant, the theory implies that returns to a spin-off (merger) transaction are expected to increase (decrease) with disagreement about the two components. This conclusion requires short sales constraints, but not on all investors or on
all assets; the price impact would result as long as short-sales constraints bind for at
least one investor on at least one of the two assets in the bundle. The empirics in this
paper look across a wide range of asset pairs with likely different distributions of beliefs, so it is hard to generalize in terms of the exact shape of the relationship between
disagreement and restructuring returns that we should expect in such a cross-section.
Still, the model provides some useful intuition for the basic relationship explored here.
Notice that only disagreement that leads investors to choose not to hold at least
64
one of the securities is related to the price effect, while investors who simply change
the proportion of their holdings do not impact prices. For this reason, when I measure
the overlap in shareholder bases, I count as overlapping any shareholders who hold
at least some quantity of both securities, however disproportionate, rather than only
crediting the quantities which are held in the original proportions.
Among the investor groups described at the end of Section 2.1.3, the first two
groups are the investors that I will focus on in my empirical analyses. If most of
the investors fall in group 1, and hold both components of the company both before
and after the spin-off (or merger), the implication is that there is little disagreement
among investors and that there should be little price effect (since, as shown above,
group 1 investors do not contribute to the price differential from unbundling). On the
other hand, if most of the investors fall in group 2, the implication is that investors
disagree strongly about the prospects of the two businesses and, as shown above, that
there should be a large price impact (positive in the case of a spin-off, and negative in
the case of a merger) if many of these investors face short sales constraints. Thus, I
will use the fraction of the investors in these two groups who fall in the second group
as my primary measure of disagreement.
The third group and fifth groups (who do not participate in the assets in one
equilibrium but "drop in" or "drop out" when the bundle is separated) are considered
empirically as an additional disagreement measure, but it could be argued that these
groups may have other reasons (outside of this theoretical model) for their empirical
change in participation. Also, as mentioned in Section 2.1.3 and further explored in
the Appendix, the direction of the contribution of group five to the price impact is
indeterminate. The fourth group, which also has an indeterminate impact, has only
second order effects and is a difficult group to identify empirically.
By basing my primary disagreement measure on the first two groups of investors,
I am therefore focusing on the first order effects of disagreement, am quantifying
those groups that can be identified empirically, and am not subject to the uncertain
directional predictions related to the second order effects of the portfolio rebalancing
of investors.
65
2.2
2.2.1
Data and Empirical Methodology
Data and Sample Characteristics
Transaction details are sourced from SDC and confirmed against CRSP data for fields
available in CRSP. Transaction ex dates and returns over the relevant periods are
determined from CRSP. I restrict my analysis to successfully completed 100% spinoffs or mergers of public companies that are not accompanied by other significant
transactions.
1
Stock mergers in the sample are required to be stock-for-stock deals
with no other forms of consideration. (Similarly, the cash acquisitions analyzed herein
must involve no form of consideration other than cash.) For spin-offs I also require
that there was no "when-issued" trading prior to the ex date and that both entities
continue trading for at least 90 days after the ex-date, and I exclude cases of multiple
units being spun off at the same time and other special situations. The spin-offs and
mergers that remain in my sample should generally not trigger any tax liabilities to
the initial shareholders unless they respond by selling their holdings.
The shareholder disagreement measure is based on institutional holdings data in
13F filings from Thomson Financial."
Some noise is introduced by using data only
on institutional holdings in order to estimate disagreement, but this data limitation
is expected to dampen my results rather than introducing any bias. For spin-offs,
my measure of disagreement is the ratio, weighted by their holdings, of continuing
investors, i.e., initial investors who continue to hold at least one of the the securities
after the transaction, who hold only one of the securities after the transaction. For
mergers, the corresponding measure is the ratio of continuing investors, weighted by
"Cases are excluded from the sample if other significant transactions (such as one of the companies
acquiring or being acquired by another party) close less than 150 days before the spin-off or merger
in question is announced or are announced less than 150 days after the spin-off or merger in question
is closed. These windows are chosen to limit the interference of other events with investor holdings,
which are given 30 days to respond to an event and are collected over a 120 day window. If an
announcement date is not available for a potentially conflicting M&A transaction, it is assumed to
occur at most 240 days before the closing date. Among other situations, these restrictions allow me
to avoid so called "Morris-Trust" transactions, in which a spin-off is used to facilitate a merger.
17
Insititutions who have investment discretion over $100 million or more in 13F securities (including
all equities traded on US exchanges as well as certain other securities) are required to file form 13F
reporting their holdings of such securities every calendar quarter, within 45 days of quarter-end.
66
their holdings, who, before the transaction, held only one of the two securities. For the
reasons discussed in Section 2.1.4, these measures of non-overlap consider investors
to be overlapping as long as they hold at least some amount of each security, even if
they are held out of proportion.
Initial investors are those who report holding the joint firm (in the case of spinoffs) or one of either the target or acquiring firms (in the case of mergers) in a
120 day window before the announcement date of the transaction. Considering preannouncement holders allows me to focus on long-term shareholders, rather than short
term speculators who buy and sell the securities after the announcement. Continuing
investments are checked in the 120 day period starting 30 days after the ex date, to
allow some time for investors to reshuffle their holdings.
I also calculate and control for investor "drop-in" and "drop-out" variables - that
is, holders of the joint firm who do not (or did not) hold either of the individual
components. As discussed in Section 2.1.4, though these variables could also measure disagreement, they are open to alternative interpretations (e.g., the dropping
out of institutions could reflect overall dissatisfaction with the transaction or could
reduce active monitoring), so I consider them to be control variables rather than main
variables of interest.
The resulting sample of spin-offs consists of 172 full spin-offs of wholly-owned
subsidiaries of publicly-traded US firms closed between 1988 and 2012. Summary
statistics are presented in Table 2.1. Consistent with the literature, I find a 3.28%
mean excess return (over the value-weighted market index) to the joint firm on the
announcement date and a 2.38% mean excess return on the ex date. The excess
volume of trade, calculated relative to the daily trading volume from a 60 day reference
period ending on the 31st day before the announcement date or beginning on the 31st
day after the ex date, is between 1-2% on both the announcement date (for the joint
company) and the ex date (for the spinner).
The mean level of my disagreement variable, the ratio of continuing investors who
hold only one of the two securities after the transaction, is 24%. Of these investors,
who held the joint company before the transaction but hold only the parent or only the
67
newly spun company afterwards, the mean fraction who hold the parent is 80% (and,
on average, the remaining 20% hold only the spun company). This is not surprising
because, on average, the ratio of the larger to smaller component of the joint company
(generally, the ratio of the parent to spun company) is 14 times. The small relative
size of the spun-off companies makes the event returns even more impressive, as a 5%
return to the joint company would equal about 75% of the value of the subsidiary at
the average size ratio.
Some of the spin-offs are very small; the maximum parent-to-spin-off ratio is over
400. Given that spinning off a relatively very small subsidiary can be expected to
have only limited impact on the joint value of both components, I will consider two
subsamples of relatively more significant transactions: (i) a subsample, which is about
15% smaller than the full sample, where the relative size ratio is no more than 25
(i.e., the spun entity is at least 4% of the parent) and (ii) a subsample, which is about
25% smaller than the full sample, where the relative size ratio is no more than 10.
The sample of stock mergers consists of 1,126 successfully completed stock-forstock mergers of publicly-traded US firms between 1980 and 2012. Summary statistics are presented in Table 2.2. The mean level of my disagreement variable, the ratio
of continuing investors who had held only one of the two securities before the transaction, is 70%.ls Of these investors, who held only one security before the merger but
hold the joint company afterwards, the mean fraction who originally held only the
larger component is 87% (and, on average, the remaining 13% held only the smaller
company). On average, the acquirer is 22 times the size of the target, with a maximium such ratio of well over 1,000. As in the case of spin-offs, I will therefore consider
subsamples of less extreme size deviations: (i) a subsample where the ratio of acquirer
to target size is no more than 25, resulting in a sample that is about 15% smaller
than the overall sample and (ii) a subsample where this ratio is above the median
such ratio of around 5, cutting the sample in about half.
18
Jt is possible that the high level of this non-overlap ratio, relative to the low level in the case of spinoffs, may reflect some inertia. That is, the 70% in the case of mergers may include some investors
who do not like and will thus sell the joint firm some additional months after the ex date, while the
22% in the case of spin-offs might not include some investors who do not like and thus will sell one
of either the parent or the spun-off company some additional months after the ex date.
68
2.2.2
Empirical Methodology
The main regression specification is
r,= a + /Disagreementj + 1yXj + Ej
where ri is the event return, Disagreementi is the measure of non-overlap of shareholder bases discussed in 2.1, and Xi is the vector of control variables, including the
investor "drop-in" and "drop-out" variables discussed in 2.1.
Figure 2-1 provides an illustrative timeline of the significant event dates in a spinoff transaction. The event return that I focus on is the ex-date return. As discussed
in the introduction, the ex date is pre-announced and occurs after information has
been disseminated and any uncertainty about the transaction has been resolved, so
the ex-date return should not reflect business information. On the other hand, there
is empirically a significant return on the ex date of both types of transactions (see
Vijh (1994) and Mitchell, Pulvino and Stafford (2004)), indicating that these dates
are important. In the case of a spin-off, since the ex date is the first day that the
securities trade separately,1 9 it is also the first date at which investors can trade in and
out of their preferred securities. To the extent that the exact amount of reshuffling
and the valuations of the reshuffling parties are not fully predicted, the ex-date return
should reflect the unpredicted part of the value impact I am trying to identify (the
impact of allowing a separation of clienteles).
Mergers do not allow as clean an identification strategy because, while some investors may wait to reshuffle their holdings until the ex-date, the two securities are
already tradable in any combination at the announcement date.
However, to the
extent that some investors wait until the ex date to make these trades, perhaps in
the case of target investors who do not pay attention until they actually receive the
acquirer stock, I might still be able to identify an impact.
Another difference in
the case of mergers is that there is an imposed exchange ratio, which could create a
value transfer from acquirer to target shareholders (or vice versa) and impact investor
19
1n some transactions, spin-offs commence when-issued trading before the ex date, but these situations
are excluded from the sample analysed in this study.
69
decisions.
To the extent that some disagreement may be anticipated (and, in the case of
mergers, may induce trading) before the ex date, estimates on the ex date will only
provide a portion of the full impact of disagreement. Thus, in order to provide a
high water mark for the impact of disagreement, I will also consider returns at announcement and over the period from announcement until the ex date. However,
these other returns will also reflect information about the transaction and any accompanying business impact. I cannot be confident that any incremental impact of
disagreement estimated in these earlier periods is not instead related to the business
impact of these transactions.
2.3
Spin-Off Results
2.3.1
Spin-Off Ex Date and Post Ex Returns
As shown in Table 2.3, the main shareholder disagreement variable is a significant
predictor of the ex-date excess return, whereby the joint firms earn about 65 to 70
basis points of additional return for a one-standard deviation increase in the ratio
of continuing investors who choose to hold only one component after the spin. As
expected, the relationship is stronger in the subsamples of less disparate relative sizes
and is monotonically increasing with the relative size of the spun company (or the
parent if it is the smaller company). When the larger-to-smaller size ratio is limited
to no more than 25, there are 80 basis points of additional return for a one-standard
deviation increase in the disagreement variable; when this ratio is limited to no more
than 10, this effect grows further to 125 basis points.
In contrast to my results, Abarbanell, Bushee, and Raedy (2003) were not able to
associate spin-off ex date returns with the trading of style (i.e., small or large, value
or growth) investors. They find that style classifications are predictive of trading
decisions upon receiving a spin distribution, but that the trading that results does
not seem to drive price movements. Thus, the disagreement that I am measuring
70
is likely a more general form of disagreement about the future prospects of the two
entities.
The institutional holders drop-out ratio, which may also measure disagreement,
also has a significant positive relationship with ex date returns when unweighted,
but not when weighted by ex ante shareholdings. As discussed in Section 2.1.4, this
variable is open to alternative interpretations.
Other control variables are limited
because there are few other reasons to expect an abnormal return on the ex-date.
The index sellers dummy indicates cases in which trackers of the S&P 500 would be
expected to trade to rebalance their portfolios. Of course, this is also a clientele effect,
but of a very specific variety. The excess volume of trade of the spinner is intended
to control for short-term speculative trading, as most long-term reshufflers hold the
spinner and trade the spun company. Thus, this variable should not be driven by the
long-term rebalancing volume.
It could be useful to explore the results for the subsample of tracking stock spinoffs. Since there is no business separation of the entities in these cases, some of the
other channels of value impacts (e.g., internal capital markets) in spin-offs do not
apply in the same way. However, tracking stocks are rare, and "clean" cases of the
sort I focus on in this study are even rarer. My sample includes 8 tracking stock
spin-offs, which limits the possible empirical analyses I can run on this subsample.
Still, it is interesting to note that the (unreported) coefficient of my primary variable
in the ex date regression is in the same direction when interacted with a tracking
stock and non-tracking stock dummy.
One concern with the ex date returns is there may be a short-term price pressure
effect that could be reversed in the days that follow.
To address this, I examine
returns over a 10 trading day period after the ex date. The results, presented in
Table 2.4, demonstrate no such reversal.
71
2.3.2
Spin-Off Announcement and Announcement until Ex
Date Returns
At announcement, and from announcement until the ex date, information about the
business impact and likelihood of the transaction actually closing may be revealed
and incorporated into prices. Thus, these returns may include the possible business
effects discussed in the introduction - for example, the impact of deconstructing
the internal capital market, a reduction in asymmetric information, creation of a new
currency with which to incentivize spin-off management, or shareholder expropriation
of bondholder wealth.
Of course, at the same time, expectations about changes in the shareholder base
could impact prices as well. For example, in Table 2.11, it is shown that some of
the variation in shareholder overlap can be explained by the relative sizes of the
two components, so this part of the reshuffling of shareholders could be anticipated.
However, as demonstrated in Tables 2.5 and 2.6, there is no impact of the nonoverlap variable prior to the ex date. Also, as shown in Table 2.12, it is the portion
of disagreement that cannot be attributed to differences in size, industry, or growth
prospects, and thus is unlikely to be predicted, that drives the ex date results.
2.4
2.4.1
Merger Results
Merger Ex Date and Post Ex Returns
As demonstrated in Table 2.7, the shareholder disagreement variable is a significant
predictor of the ex-date joint return for stock mergers, with about 20 to 25 basis
points of lower return for a one standard deviation increase in the ratio of continuing
investors who held only one component before the merger. As in the case of spinoffs, the impact is stronger in the subsamples of less disparate relative sizes, growing
to a 30 to 40 basis point lower return for a one standard deviation increase in the
disagreement measure. The control variables are similar to those used in the spin-off
analysis. The index buyers dummies indicate cases in which trackers of the S&P 500
72
would be expected to buy to rebalance their portfolios. As mentioned above, this is
also a clientele effect, but of a very specific variety. While I have included a volume
of trade control, note that in this case I cannot isolate short-term speculative trade
volume from long-term rebalancing volume, so this variable may absorb some of the
effect of disagreement-related trading and the associated price impact.
As shown in Table 2.8, there a significant positive coefficient on the non-overlap
variable for the post ex date period full period, which provides evidence of some
reversal of the ex date effect. This reversal is consistent with the finding in Table
2.12 that much of this ex date effect in the full sample can be attributed to predictable
trading based on the relative size of the components. That is, there may be short
term selling pressure due to target investors predictably "dumping" acquirer stock,
resulting in a temporary price effect that then reverses. However, there is no evidence
of reversal in the larger relative size samples. Since the subsample of deals with a
relative size ratio of no more than 25 represents about 85% of the full sample, this
means that it is only the 15% of the sample with the most extreme size disparities in
which the result seems to be driven by short term price pressure.
In the case of mergers, there is a practical control sample to consider - that of
cash acquisitions. Since the acquiring company will still hold the target going forward,
shareholders' interest in holding the target and the acquirer together may still result
in a reshuffling of ownership and be relevant for price effects. However, there is no
particular reason for any such reshuffling of ownership to happen on the ex date,
since there is no share consideration to deliver on that date. As conjectured, the
non-overlap variable has no explanatory power for ex date or post ex date returns in
the case of cash deals.
2.4.2
Merger Announcement and Announcement until Ex
Date Returns
Results for the announcement date and announcement to ex date are presented in
Tables 2.9 and 2.10.
For these periods, disagreement may generally be predicted
73
to have similar effects in the case of cash acquisitions as in stock mergers, since
disagreement about the two components in either type of transaction could give rise to
trading and an associated price impact at or after the announcement. However, there
are a few complications to keep in mind. Cash acquisitions represent the exchange of
cash for a target security, so the expected price reaction of shareholders may depend on
their assumptions regarding likely use of that cash in the absence of this transaction.
Also, cash consideration is also immediately taxable to target shareholders upon
receipt. The results for cash transactions may thus also reflect tax effects if nonoverlap of the shareholder bases is related to the embedded tax liability of target
shareholders, which might be the case if longer term target shareholders (with larger
embedded gains) are less likely to be overlapping investors.
In contrast with the results for spin-offs, which were concentrated on the ex date,
for both stock and cash acquisitions there is an additional large negative relationship
of returns with non-overlap before the ex date, but not on the announcement date
itself. The lack of a relationship between my non-overlap variable and announcement
date returns is consistent with Harford, Li and Jenter (2007), who consider the impact
of acquirer-target crossholdings on bidder announcement returns.
While my non-
overlap variable is constructed differently from the crossholdings measure considered
by those authors, and I consider joint returns rather than bidder-only returns, the
analyses are similar in nature.
The relationship of disagreement with returns between the announcement and
ex date is not statistically significant for the full sample of stock mergers, but it
is significant for the subsamples of more significantly sized stock transactions and
for both the full sample and subsamples of cash acquisitions. This relationship is
consistent with the fact that in both stock and cash deals, both stocks are separately
tradable at any time until the ex date, so shareholders can trade in reaction to the
news of the merger at any time after the announcement. As demonstrated in Table
2.12, these results are driven by the part of disagreement that cannot be attributed to
differences in size, industry, or growth prospects of the two components. Of course,
these results may also be capturing business-related information as noted above.
74
2.5
Concluding Remarks
This paper documents a price impact of disagreement among investors in the context
of corporate spin-offs and mergers. By using revealed preferences in institutional
holdings data, I am able to measure a general form of disagreement that has otherwise
proven difficult to observe, as compared to more specific disagreements (e.g., about
dividend policy). Also, when I analyze returns on spin-off ex dates, I am able to
differentiate the impact of disagreement from any business impact that might be
related to characteristics that are proxied for by my disagreement measure. Since no
new business information is revealed on these dates, and yet disagreement may be
demonstrated as investors are first allowed to trade the two securities separately, I
am able to cleanly identify a price impact of disagreement.
These results provide some new insight into the price impacts of spin-offs and
mergers as well as the rationales behind such transactions. That is, spin-offs may be
undertaken in order to cater to two divergent investor bases. Mergers. on the other
hand, may have a rationale that overcomes any downside of forcing investors to hold
a bundle of two entities that they might not agree about.
This new evidence of investor disagreement and its effects also demonstrates that
investors may have substantial differences in opinion about more general prospects of a
firm, beyond specifics such as dividend policy. Such disagreement may have important
business impacts, as investments, hedging, and corporate restructuring may all be
designed to cater to particular shareholder clienteles. Details of the channels through
which investor disagreement effects corporate decision-making, and the extent of such
effects outside of specific transactions like the ones analyzed here, should be further
explored.
75
2.6
Figures and Tables
Figure 2-1: Spin-Off Illustrative Timeline
This is an example tirelinefor a corporate spin-off transaction. Some transactions require additional
steps, such as a proxy distribution and shareholder vote. In some deals, the payment date is after the
transactionex date. in which case the spin-off trades as a when-issued security from the ex date until
the payment date. In addition. some spin-offs commence when-issued trading before the ex date, but
these situations are excluded from, the analyses in this study.
r-,200 Days
>20 Days
JOINT
STOCK
Announcement
(
/
I
I
Final
SPUN
STOCK
Record Ex
Date
Date
Information
Statement
Mailed
PARENT
STOCK
P an
vV't
Dten+
Declaration
Date:
Announce Record, Payment Dates
Exchange sets Ex Date
76
Table 2.1: Spin-Off Summary Statistics
The sample consists of 172 spin-offs of 100% of the wholly-owned subsidiaries of publicly-traded
US firms, closed between 1988 and 2012. See Section 2.2.1 for details on these variables and their
construction.
N
Mean Median St. Dev.
Min
Max
PANEL A: Explained and Explanatory Variables
Event Period Joint Returns - Excess over VW Index
Announcement (2 days)
172
3.28%
2.86%
6.54% -28.40% 21.05%
Announcement to Day Before Ex Date
172
4.14%
3.48% 25.06% -86.05% 103.97%
Ex Date
172
2.38%
1.90%
4.33% -10.52% 19.79%
Post Ex Date (10 days)
172
-2.71% -1.48%
9.24% -38.47% 25.98%
Continuing Investors Holding One Side Only'
172
0.24
0.19
0.18
0.00
1.00
Institutional Holders Drop-out Ratiol
172
0.17
0.14
0.11
0.01
0.63
Institutional Holders Drop-out Ratio2
172
0.31
0.31
0.10
0.07
0.61
Excess Volume of Trade - Announcement Date
172
1.74%
0.93%
2.79% -3.26% 16.53%
Excess Volume of Trade - Ex Date, Spinner
172
1.25%
0.32%
2.71% -2.28% 23.21%
S&P500 Index Sellers Dummy
172
0.40
0.00
0.49
0.00
1.00
PANEL B: Background Characteristics
Combined Firm Size (pre-announceient; $M)
172
8,090
2,373
18,201
55 127,838
Larger Size/Smaller Size3
172
14.3
4.4
41.7
1.0
481.3
Num Institutional Holders pre-Announcement
172
248.4
194.0
231.2
7
1393
Num Institutional Holders post-Ex - Spinner
172
229.7
170.0
220.4
4
1280
Num Institutional Holders post-Ex - Spun
172
132.4
94.5
131.1
0
825
Deal Horizon - Ann. to Ex Date (days)4
172
203.9
195.0
127.3
6.0
1014.0
'Holdings-weighted
2Un-weighted (ratio of the number of institutions)
3
In 26 (15%) cases, spun is larger than spinner.
4
The four cases of deal horizon of <20 days have been hand-checked and have erroneous
announcement dates reported by SDC (all actual horizons are at least 20 days).
77
Table 2.2: Merger Summary Statistics
The sample consists of 1,126 stock deals and 828 cash deals between publicly-traded US firms, closed
between 1980 and 2012. See Section 2.2.1 for details on these variables and their construction.
Max
Mill
N
Mean Median St. Dev.
PANEL A: Stock Deals - Explained and Explanatory Variables
Event Period Joint Returns - Excess over VW Index
6.86% -50.95% 52.69%
-0.60% -0.35%
1117
Announcement (2 days)
-147.40% 141.33%
26.06%
-1.18%
-3.13%
1117
Date
Ex
Day
Before
Announcement to
4.50% -26.31% 84.27%
0.39%
0.76%
1126
Ex Date
-1.67% -0.97% 10.87% -75.31% 77.48%
1128
Post Ex Date (10 days)
1.00
0.00
0.24
0.74
0.70
1126
Continuing Investors Holding One Side Only1
0.98
0.00
0.16
0.16
0.20
1126
Institutional Holders Drop-out Ratio'
0.87
0.00
0.14
0.33
0.34
1126
Institutional Holders Drop-out Ratio2
5.33% -11.82% 48.51%
1.40%
3.26%
1117
Excess Volume of Trade - Ann. Date, Joint
18.99%
-6.94%
1.52%
0.03%
0.34%
1126
Excess Volume of Trade - Ex Date, Joint
1.00
0.00
0.40
0.00
0.20
1126
Ex-Date S&P500 Index Buyers Dummy
Characteristics
Background
PANEL B: Stock Deals
13 576,541
1,145 28,076
7,223
1126
Combined Firm Size (eve of ex date; $M)
1286.6
1.0
78.4
4.7
21.9
1126
Larger Size/Smaller Size (eve of ex date)
1473.0
0.0
181.8
91.5
150.5
1126
Num Institutional Holders pre-Ann. - Acquirer
860.0
0.0
87.0
21.0
51.2
1126
Num Institutional Holders pre-Ann. - Target
1591.0
1.0
196.0
119.0
175.8
1126
Num Institutional Holders post-Ex - Joint
1155.0
42.0
86.4
132.0
150.0
1126
Deal Horizon - Ann. to Ex Date (days)
PANEL C: Cash Deals - Explained and Explanatory Variables
Event Period Joint Returns - Excess over VW Index
5.28% -25.54% 29.58%
1.67%
2.62%
822
Announcement (2 days)
1.80% -2.13% 16.74% -116.07% 61.29%
822
Announcement to Day Before Ex Date
0.08% 2.11% -13.22% 16.47%
0.11%
828
Ex Date
6.95% -46.48% 27.94%
-0.41% -0.46%
828
Post Ex Date (10 days)
1.00
0.00
0.23
0.78
0.73
828
Continuing Investors Holding One Side Only'
0.74
0.00
0.13
0.11
0.15
828
Institutional Holders Drop-out Ratio'
0.83
0.00
0.13
0.25
0.28
828
Institutional Holders Drop-out Ratio2
40.42%
-2.51%
4.80%
1.13%
2.81%
822
Excess Volume of Trade - Ann. Date, Joint
-2.68% 14.00%
0.87%
0.04% -0.04%
828
Excess Volume of Trade - Ex Date, Joint
Characteristics
Background
Deals
D:
Cash
PANEL
19 291,496
29,863
1,560
10,584
828
Combined Firm Size (eve of ex date; $M)
1.0 20711.6
752.2
8.8
76.0
828
Larger Size/Smaller Size (eve of ex date)
1655.0
1.0
269.4
132.0
225.7
828
Num Institutional Holders pre-Ann. - Acquirer
345.0
0.0
57.9
21.0
46.5
828
Num Institutional Holders pre-Ann. - Target
1685.0
2.0
279.4
140.0
235.0
828
Num Institutional Holders post-Ex - Joint
644.0
28.0
87.9
83.0
112.2
828
Deal Horizon - Ann. to Ex Date (days)
'Holdings-weighted
2Un-weighted (ratio of the number of institutions)
78
Table 2.3: Spin-Off Ex Date Returns
The ex date excess return is the excess of return, in percentage points, on the originalparent stock
on the ex date minus the value-weighted market index. "Cont. investors hold one side only" is,
among institutional holders that held the joint firm before the announcement and continue to hold
at least one piece afterwards, the ratio (weighted by ex-ante shares) of those who hold only one
piece. "Institutionalholders drop-out ratio" is the ratio of originalinstitutional holders of the joint
firm who do not hold either piece after the spin-off, either weighted by ex-ante shares or unweighted
(simply the number of institutions that drop out relative to the number of original institutions) as
indicated. The excess volume of trade on the ex date is calculated relative to the reference period
from +31 to +90 days after the ex date. The sample in (4) is restricted to transactions where the
ratio of the parent to the spun-off company size (or spun-off company to parent size, if the spun
company is larger than the parent), measured on the ex date, is no more than 25; the sample in (5)
is restricted to transactions where this relative size ratio is no more than 10.
Ex Date Excess Return
(over value-weighted market index, in percentage points)
No Relative Size Restrictions
(2)
(3)
3.652 **
3.649 **
3.904 **
(2.22)
(2.09)
(2.09)
-0.239
(-0.07)
9.887 *
(2.73)
-25.195 **
-25.108 **
-33.008 *
(-2.30)
(-2.26)
(-2.97)
-0.829
-0.839
-0.502
(-1.23)
(-1.25)
(-0.78)
2.140 *
2.184 *
-1.065
(3.50)
(2.83)
(-0.93)
(1)
Cont. Investors Hold One Side Only
Institutional Holders Drop-out Ratio
(holdings-weighted)
Institutional Holders Drop-out Ratio
(unweighted)
Excess Volume of Trade - Spinner
S&P500 Index Sellers Dummy
Constant
N
172
172
172
Adjusted R2
0.03
0.02
0.08
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
79
RelSizes25
(4)
4.522 **
(2.37)
RelSizes10
(5)
7.006 ***
(2.90)
9.806 **
(2.54)
-35.066 ***
(-3.16)
-0.384
(-0.55)
-1.088
(-0.90)
11.539 **
(2.54)
-43.881 *
(-3.59)
-0.524
(-0.62)
-1.872
(-1.23)
156
0.08
126
0.11
Table 2.4: Spin-Off Post Ex Date Returns
The post ex date excess return is the excess of return, in percentage points, in the 10 trading days
after the ex date on the value-weighted combination of the parent and spun-off stocks minus the
value-weighted market index. "Cont. investors hold one side only" is, among institutional holders
that held the joint firm before the announcement and continue to hold at least one piece afterwards,
the ratio (weighted by ex-ante shares) of those who hold only one piece. "Institutionalholders dropout ratio" is the ratio of original institutionalholders of the joint firm who do not hold either piece
after the spin-off, either weighted by ex-ante shares or unweighted (simply the number of institutions
that drop out relative to the number of originalinstitutions) as indicated. The excess volume of trade
for the 10 days post ex is calculated relative to the reference period from +31 to +90 days after the
ex date. The sample in (4) is restricted to transactions where the ratio of the parent to the spun-off
company size (or spun-off company to parent size, if the spun company is larger than the parent),
measured on the ex date, is no more than 25; the sample in (5) is restricted to transactions where
this relative size ratio is no more than 10.
Cont. Investors Hold One Side Only
10 Days Post Ex Date Joint Excess Return
(over value-weighted market index, in percentage points)
RelSizes10
RelSizeS25
No Relative Size Restrictions
(5)
(4)
(3)
(2)
(1)
2.635
1.516
0.794
0.849
0.991
(0.27)
Institutional Holders Drop-out Ratio
(holdings-weighted)
Institutional Holders Drop-out Ratio
(unweighted)
Excess Volume of Trade - Joint
S&P500 Index Sellers Dummy
Constant
(0.24)
-13.412
(0.22)
(0.39)
(0.51)
*
(-1.80)
-12.554 *
-10.539
-13.958 *
(-1.80)
(-1.49)
(-1.68)
6.719
9.164
9.877
14.427
12.169
(0.34)
(0.46)
(0.49)
(0.67)
(0.52)
2.463 *
1.861
1.987
2.406 *
2.779
(1.89)
(1.36)
(1.50)
(1.79)
(1.76)
-4.147 ***
-1.684
-0.065
-1.043
-0.463
(-2.93)
(-0.87)
(-0.03)
(-0.44)
(-0.16)
156
0.01
126
0.02
172
172
172
N
0.02
0.02
0.00
Adjusted R2
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
80
*
Table 2.5: Spin-Off Announcement Date Returns
The two-day announcement date excess return is the excess of return, in percentage points, for the
two-day announcement period (day 0, +1) on the original parent stock minus the value-weighted
market index. "Cont. investors hold one side only" is, among institutional holders that held the
joint firm before the announcement and continue to hold at least one piece afterwards, the ratio
(weighted by ex-ante shares) of those who hold only one piece. "Institutionalholders drop-out ratio"
is the ratio of original institutional holders of the joint firm who do not hold either piece after the
spin-off, either weighted by ex-ante shares or unweighted (simply the number of institutions that
drop out relative to the number of original institutions) as indicated. The excess volume of trade on
the announcement date is calculated relative to the reference period from -90 to -31 days before the
announcement. The sample in (4) is restricted to transactions where the ratio of the parent to the
spun-off company size (or spun-off company to parent size, if the spun company is larger than the
parent), measured on the ex date, is no more than 25; the sample in (5) is restricted to transactions
where this relative size ratio is no more than 10.
Cont. Investors Hold One Side Only
2-Day Announcement Excess Return
(over value-weighted market index. in percentage points)
No Relative Size Restrictions
RelSize 25 RelSize 10
(5)
(2)
(3)
(4)
(1)
-2.963
-2.956
-3.019
-2.145
0.056
(-1.09)
Institutional Holders Drop-out Ratio
(holdings-weighted)
(1.09)
(unweighted)
S&P500 Index Sellers Dummy
Constant
(-0.73
-12.881 **
-15.673 **
-19.271 **
(-2.16)
92.501 *
(4.18)
0.999
(1.23)
6.068 *
(3.14)
(-2.44)
95.270 *
(4.30)
1.337
(1.53)
6.762 **
(3.33)
(-2.56)
91.785 *
(3.91)
1.816 *
(1.84)
7.760
(3.19)
(0.02)
(-0.94)
Institutional Holders Drop-out Ratio
Excess Volune of Trade - Joint
(-1.13)
-6.686
79.181 *
(3.11)
1.377 *
(1.66).
2.086 **
(2.32)
85.832 *
(-05)
1.126
(1.35)
3.198 **
(2.55)
N
172
172
172
2
Adjusted R
0.11
0.12
0.14
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%: ** significant at 5%: *** significant at 1% (all two-tailed)
81
156
0.16
126
0.19
Table 2.6: Spin-Off Announcement until Ex Date Returns
The announcement to (pre)ex date excess return is the excess of return, in percentage points, from
(and including) the announcement date until (and excluding) the ex date on the originalparent stock
minus the value-weighted market index. "Cont. investors hold one side only" is, among institutional
holders that held the joint firm before the announcement and continue to hold at least one piece
afterwards, the ratio (weighted by ex-ante shares) of those who hold only one piece. "Institutional
holders drop-out ratio" is the ratio of original institutional holders of the joint firm who do not hold
either piece after the spin-off, either weighted by ex-ante shares or unweighted (simply the number
of institutions that drop out relative to the number of original institutions) as indicated. The excess
volume of trade for the event period is calculated relative to the reference period from -90 to -31
days before the announcement. The sample in (4) is restricted to transactions where the ratio of
the parent to the spun-off company size (or spun-off company to parent size, if the spun company is
larger than the parent), measured on the ex date, is no more than 25; the sample in (5) is restricted
to transactionswhere this relative size ratio is no more than 10.
Announcement to (Pre) Ex Date Excess Return
(over value-weighted market index. in percentage points)
No Relative Size Restrictions
(3)
(2)
-5.177
-5.836
-5.071
(-0.63)
(-0.65)
(-0.71)
(1)
Cont. Investors Hold One Side Only
Institutional Holders Drop-out Ratio
-7.498
(holdings-weighted)
Institutional Holders Drop-out Ratio
(unweighted)
Excess Volume of Trade - Joint
(-0.36)
S&P500 Index Sellers Dununy
Constant
N
Adjusted R 2
4.926
(1.37)
-3.074
(-0.83)
(.83)
4.789
(1.34)
-3.385
(-0.87)
7.633
(1.55)
172
0.00
172
0.00
6.208
*
-37.251 *
(-1.87)
3.996
(1.09)
-4.316
(-1.15)
18.659 *
(2.77)
172
0.01
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
82
RelSize:25
(4)
-5.17476
(-0.60)
RelSize 10
(5)
-15.746
(-1.47)
-40.599 *
(-1.91)
3.978
(1.07)
-4.708
(-1.15)
19.803 *
(2.81)
-55.625
(-2.25)
4.258
(1.02)
-2.648
(-0.54)
156
0.02
**
26.165 *
(3.14)
126
0.03
Table 2.7: Stock Merger Ex Date Returns
The ex date excess return is the excess combined return (weighted by size) of the merging companies
on the ex date over the value-weighted market index. "Cont. investors held one side only" is,
among institutional holders that held at least one piece before the announcement and continue to
hold the joint firm afterwards, the ratio (weighted by ex-post shares) of those who held only one
piece. "Institutionalholders drop-in ratio" is the ratio of institutional holders of the joint firm who
did not hold either piece before the merger, either weighted by ex-post shares or unweighted (simply
the number of institutions that drop in relative to the total number of institutions that hold the joint
firm) as indicated. The excess volume of trade on the ex date is calculated relative to the reference
period from +31 to +90 days after the ex date. The sample in (4) is restricted to transactions
where the ratio of the acquirer to target company size (or the reverse, if the target is larger than the
acquirer), measured on the ex date, is no more than 25; the sample in (5) is restrictedto transactions
where this relative size ratio is no more than 5 in the case of stock deals and 10 in the case of cash
deals.
Ex Date Excess Return
(over value-weighted market index. in percentage points)
No Relative Size Restrictions
RelSize 25 RelSize 5/10
(1)
(2)
(3)
(4)
(5)
PANEL A: Stock-for-Stock Mergers
Cont. Investors Held One Side Only
-0.857 *
(-1.84)
Institutional Holders Drop-in Ratio
-1.061 **
(-2.27)
1.770 **
(2.05)
(holdings-weighted)
Institutional Holders Drop-in Ratio
S&P500 Index Buyers Dunmy
Constant
N
Adjusted R 2
PANEL B: Cash Acquisitions
Cont. Investors Held One Side Only
47.589 *
46.003 *
(3.94)
(3.80)
-0.591 *
(-2.81)
1.318 *
(3.62)
-0.426 *
(-1.80)
1.086 *
(2.71)
N
Adjusted R 2
47.232 *
(3.93)
-0.554 **
(-2.17)
1.208 **
(2.27)
42.029 *
(3.41)
-0.627 **
(-2.30)
1.266 *
(2.94)
46.256 *
(3.36)
-1.611 *
(-3.99)
1.807 *
(3.48)
1126
0.03
1126
0.03
1126
0.03
968
0.03
578
0.02
0.089
(().2'9)
0.116
(0.38)
-0.250
(-0.41)
0.094
(03)
0.232
(0.(q)
0.124
(0.2.9)
**
**
Institutional Holders Drop-in Ratio
Constant
-1.680 **
(-1.98)
1.545
(1.26)
0.359
Institutional Holders Drop-in Ratio
(holdings-weighted)
(unweighted)
Excess Volume of Trade - Joint
-1.230 *
(-2.29)
1.757 *
(1.90,)
(0.36)
(unweighted)
Excess Volume of Trade - Joint
-0.885 *
(-1.92)
-0.061
**
**
**
32.829
(1.93)
0.031 *
32.986
(1.95)
0.048 *
(-0.10)
32.864
(1.94)
0.044 *
(0.15)
(0.22)
(0.17)
(-0.10)
(0.24)
828
0.02
828
0.02
828
0.01
616
0.02
450
0.00
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at. 1% (all two-tailed)
83
34.761
(1.78)
-0.024 *
19.566
(1.00)
0.068 *
Table 2.8: Merger Post Ex Date Returns
The 10-day post ex date excess return is the excess combined return (weighted by size) of the merging
companies for the 10 days after the ex date over the value-weighted market index. "Cont. investors
held one side only" is, among institutional holders that held at least one piece before the announcement and continue to hold the joint firm afterwards, the ratio (weighted by ex-post shares) of those
who held only one piece. "Institutionalholders drop-in ratio" is the ratio of institutional holders of
the joint firm who did not hold either piece before the merger, either weighted by ex-post shares or
unweighted (simply the number of institutions that drop in relative to the total number of institutions that hold the joint firm) as indicated. The excess volume of trade for the 10 days post ex is
calculated relative to the reference period from +31 to +90 days after the ex date. The sample in (4)
is restricted to transactionswhere the ratio of the acquirer to target company size (or the reverse, if
the target is larger than the acquirer), measured on the ex date, is no more than 25; the sample in
(5) is restricted to transactions where this relative size ratio is no more than 5 in the case of stock
deals and 10 in the case of cash deals.
10 Days Post Ex Date Excess Return
(over value-weighted market index, in percentage points)
No Relative Size Restrictions
RelSize:25 RelSize:5/10
(1)
(2)
(3)
(4)
(5)
PANEL A: Stock-for-Stock
Mergers
Cont. Investors Held One Side Only
2.860
*
(1.99)
Institutional Holders Drop-in Ratio
(holdings-weighted)
Institutional Holders Drop-in Ratio
2.825
(1. 79)
0.309
(0.09)
*
S&P500 Index Buyers Dummy
Constant
N
Adjusted R 2
PANEL B: Cash Acquisitions
Cont. Investors Held One Side Only
-8.203
(-1.1)
0.348
(0.50)
-3.608 *
(-2.28)
N
Adjusted
R
-8.239
(-1. m)
0.377
(0.52)
-3.650 *
(-3.48)
-8.341
(-.1.1,9)
0.495
(0.67)
-4.053 *
(-2.03,)
-9.650
(-L.43I)
-0.045
(-0.05)
-3.044
(-2.87)
-12.024
(-.)
1.446
-2.323
(-1.98)
1128
0.01
970
0.01
579
0.01
0.850
(0.90)
0.677
(0.68)
1.562
(0.77)
0.348
(0.36)
0.042
(0.04)
0.501
(0.40)
**
-2.583
(-0.23)
**
-2.502
(-0.23)
**
5.408
**
(2.83)
-5.411
(-0.44)
4.597
-2.168 *
-1.964
(-1.58)
(-2.72)
(-209)
(-1.90)
828
0.00
828
0.00
828
0.01
616
0.01
450
0.00
*
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%: ** significant at 5%; *** significant at 1% (all two-tailed)
84
**
**
(2.15)
-6.979
(-0.46)
('-1.41)
*
-1.130
5.429
(3.18)
-1.693
(-0.16)
*
(1.19)
*
1128
0.01
-1.025
2
-0.870
(-0.48)
3.833
(1.16)
1128
0.01
Institutional Holders Drop-in Ratio
(unweighted)
Constant
1.309
(0.96)
2.498
(0.96)
(0.49)
Institutional Holders Drop-in Ratio
(holdings-weighted)
Excess Volume of Trade - Joint
*
1.427
(unweighted )
Excess Volume of Trade - Joint
2.753
(1.86)
*
-2.048
*
Table 2.9: Merger Announcement Date Returns
The announcement excess return is the excess combined return (weighted by size) of the merging
companies for the two-day announcement period over the value-weighted market index. "Cont. investors held one side only" is, among institutional holders that held at least one piece before the
announcement and continue to hold the joint firm afterwards, the ratio (weighted by ex-post shares)
of those who held only one piece. "Institutionalholders drop-in ratio" is the ratio of institutional
holders of the joint firm who did not hold either piece before the merger, either weighted by ex-post
shares or unweighted (simply the number of institutions that drop in relative to the total number of
institutions that hold the joint firm) as indicated. The excess volume of trade on the announcement
date is calculated relative to the reference period from -90 to -31 days before the announcement. The
sample in (4) is restricted to transactions where the ratio of the acquirer to target company size (or
the reverse, if the target is larger than the acquirer), measured before the announcement date, is no
more than 25; the sample in (5) is restricted to transactionswhere this relative size ratio is no more
than 5 in the case of stock deals and 10 in the case of cash deals.
2-Day Announcement Excess Return
(over value-weighted market index. in percentage points)
No Relative Size Restrictions
RelSizes25 RelSizes5/10
(5)
(4)
(3)
(2)
(1)
PANEL A: Stock-for-Stock Mergers
Cout. Investors Held One Side Only
Institutional Holders Drop-in Ratio
(holdings-weighted)
Institutional Holders Drop-in Ratio
(unweighted)
Excess Volume of Trade - Joint
S&P500 Index Buyers Dummy
Constant
N
Adjusted R 2
PANEL B: Cash Acquisitions
Cont. Investors Held One Side Only
-0.056
-0.221
-0.124
-0.344
-1.659
(-0.05)
(-0.18)
(-0.11)
(-0.25)
(-0.SY)
1.472
(0.62)
1.314
(0.38)
1.300
(0.61)
0.779
(0.43)
-8.061
(-0.81)
-0.197
(-0.54)
-0.258
(-0.26)
-8.464
(-0.85)
-0.081
(-0.21)
-0.408
(-0.44)
-8.269
(-0.83)
-0.121
(-0.31)
-0.488
(-0.50)
-7.983
(-0.7,)
-0.422
(-0.77)
-0.382
(-0.38)
-1.472
(-0.12)
-0.503
(-0.52)
0.180
(0.15)
1117
0.00
1117
0.00
1117
0.00
922
0.00
524
0.00
-0.075
(-0.07)
-0.391
(-0.36)
2.438
-0.320
(-0.30)
0.080
(0.06)
-0.610
(-0.37)
Institutional Holders Drop-in Ratio
(holdings-weighted)
(1.40)
Institutional Holders Drop-in Ratio
**
**
(unweighted)
Excess Volume of Trade - Joint
Constant
25.660
(4.03)
1.951
('2.33)
*
25.011
(3.88)
1.840
(2.22)
*
-0.237 **
0.051
(1.36)
(-0.12)
(0.02)
25.216
(3.94)
1.494
(1.86)
20.741
(3.08)
2.640
(:2.46)
16.403
(2.24)
3.944
(2.85)
2.335
**
*
822
822
822
N
0.05
0.05
Adjusted R.2
0.05
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%: ** significant at 5%; *** significant at 1% (all two-tailed)
85
577
0.03
*
391
0.02
**
*
Table 2.10: Merger Announcement until Ex Date Returns
The announcement to ex date excess return is the excess combined return (weighted by size) of the
merging companiesfrom and including the announcement date to and including the ex-date over the
value-weighted market index. "Cont. investors held one side only" is, among institutional holders
that held at least one piece before the announcement and continue to hold the joint firm afterwards,
the ratio (weighted by ex-post shares) of those who held only one piece. "Institutionalholders dropin ratio" is the ratio of institutional holders of the joint firm who did not hold either piece before
the merger, either weighted by ex-post shares or unweighted (simply the number of institutions that
drop in relative to the total number of institutions that hold the joint firm) as indicated. The excess
volume of trade for the event period is calculated relative to the reference period from -90 to -31
days before the announcement. The sample in (4) is restrictedto transactionswhere the ratio of the
acquirer to target company size (or the reverse, if the target is larger than the acquirer), measured
before the announcement, is no more than 25; the sample in (5) is restricted to transactions where
this relative size ratio is no more than 5 in the case of stock deals and 10 in the case of cash deals.
Ann. to (Pre) Ex Date Excess Return
(over value-weighted market index. in percentage points)
(1)
PANEL A: Stock-for-Stock
No Relative Size Restrictions
(2)
(3)
Mergers
Cont. Investors Held One Side Only
-1.054
-2.128
-3.295
-5.781
(-0.35)
(-0.69)
(-1.10)
(-1.73)
(-3.15)
30.726 *
(4.40)
11.949 *
31.701 *
(4.17)
12.547 *
45.720 *
(5.03)
12.778 **
(4.97)
(4.93)
(3.52)
12.694 ***
12.370 *
(5.13)
4.521 *
(3.15)
-5.947 **
6.733 *
(4.67)
-14.234 *
5.541 *
(3.22)
-13.131 ***
4.843 *
(1.91)
-14.349 *
(-2.48)
(-4.72)
(-4.08)
(-3.56)
1117
1117
1117
922
524
0.08
0.08
0.10
0.12
0.17
-4.938
(-1.93)
-5.033
(-1.97)
0.769
(0.14)
-6.110
(-2.38,)
-6.929
(-2.32)
-8.947
(-2.11)
(5.28)
S&P500 Index Buyers Dummy
3.630 *
(2.59)
Constant
-4.693 **
(-1.99)
N
2
Adjusted R
PANEL B: Cash Acquisitions
Cont. Investors Held One Side Only
Institutional Holders Drop-in Ratio
(holdings-weighted)
Institutional Holders Drop-in Ratio
(unweighted)
Excess Volume of Trade - Joint
**
**
-1.857
-1.902
(-0.62)
(-0.63)
5.490
(2.93)
N
AdjustedR 2
-13.951 *
(1.46)
(unweighted)
Excess Volume of Trade - Joint
*
9.535
Institutional Holders Drop-in Ratio
(holdings-weighted)
Institutional Holders Drop-in Ratio
Constant.
RelSize 25 RelSize 5/10
(4)
(5)
*
5.448
(2.84)
*
11.653 **
3.836
(2.37)
-2.335
(0.71)
-1.104
(0.73)
-0.785
(-0.77)
(-0.35)
(-0.22)
**
4.769 **
3.135 *
6.843 *
8.434 *
(1.46)
(2.68)
(2.56)
822
822
822
577
391
0.02
0.01
0.01
0.01
0.01
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
86
Table 2.11: Attribution of Disagreement
The disagreement measures are the left-hand size variable. Explanatory variables are measures of the differences in size, industry, and growth prospects
between the two components of the transaction. "Cont. investors hold one side only" is. among institutional holders that held the joint firm before
the announcement of the spin-off and continue to hold at least one piece afterwards, the ratio (weighted by ex-ante shares) of those who hold only one
piece. "Cont. investors held one side only" is, among institutional holders that held at least one piece before the announcement of the merger and
continue to hold the joint firm afterwards, the ratio (weighted by ex-post shares) of those who held only one piece. Relative size is measured on the ex
date in the case of spin-offs and on the eve of announcement in the case of stock mergers.
-1
Relative Size (Smaller/Larger)
Primary 3-digit SIC Code is Same
Absolute Difference in Q-Ratios
Constant
N
Spin-Offs
Cont. Investors Hold One Side Only
(2)
(1)
-0.233 *
-0.253 *
(-5.13)
(-5.07)
-0.011
(-0.39)
0.002
0.313 *
(16.17)
172
(0.3_0(1.1
0.324 *
(13.12)
152
Adjusted R2
0.12
0.15
Heteroskedasticityv-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
Stock Mergers
Cont. Investors Held One Side Only
(4)
(3)
-0.361 *
-0.354 *
(-13.36)
(-12.63)
-0.024 *
(-1.67)
-0.002
0.792
(90.00)
*
0.787 *
(66.53)
1129
957
0.14
0.14
Table 2.12: Predicted vs. Unpredicted Disagreement and Returns
All event returns used are the excess joint return over the value-weighted market index. "Cont. investors hold one side only" is, among institutional
holders that held the joint firm before the announcement of the spin-off and continue to hold at least one piece afterwards, the ratio (weighted by
ex-ante shares) of those who hold only one piece. "Cont. investors held one side only" is, among institutional holders that held at least one piece
before the announcement of the merger and continue to hold the joint firm afterwards, the ratio (weighted by ex-post shares) of those who held only
one piece. The sample in (5) and (6) is restricted to transactions where the ratio of the acquirer to target company size (or the reverse, if the target
is larger than the acquirer), measured before the announcement, is no more than 5.
Spin-Offs
Stock Mergers
Ex-Date - Full Sample
Prediction Variables by Colunmn:
(1) Size
(2) Size,
Q, SIC3
Ex-Date - Full Sample
(3) Size
(4) Size, Q, SIC3
Ann to Ex Date - RelSize 5
(5) Size
(6) Size,
Q. SIC3
Cont. Investors Hold/Held One Side Only:
00
-9.729
Unpredicted Component
3.784
-0.462
-0.455
-8.159
Predicted Component
(2.07)
2.623
(2.58)
4.936
(-0.77)
-3.129
(-0.69)
-3.020
(-1.82)
-4.423
(-1.97)
-7.147
(0.45)
(0.88)
(-1.79)
00
Excess Volume of Trade
S&P500 Index Buyers/Sellers Dummy
Constant
N
Adjusted R 2
**
4.891
**
*
*
(-.58)
(-0.31)
(-0. 46)
-21.677
(-1.69)
*
46.057
(3.70)
**
47.030 *
(3.50)
15.695 *
(4.05)
15.752
(3.43)
-0.797
-1.247
*
-0.399
*
-0.420
(-1.17)
(-1.74)
-25.621
(-2.27)
**
(-1.96)
*
-1.94)
**
1.839
(0.72)
-3.212
-1.930
(-0.35)
(-0.20)
2.380
1.855
2.865
(1.54)
(1.22)
(2.21)
172
152
1126
957
574
487
0.02
0.04
0.03
0.03
0.13
0.12
Heteroskedasticity-robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1% (all two-tailed)
**
2.773
1.485
(0.62)
(1.98)
**
*
2.7
Appendix
In this appendix, I further examine the equilibrium prices in the bundled and unbundled economies in Sections 1.1-1.3. providing sets of sufficient (but not necessary)
conditions for the price of the bundle in the bundled econom to be less than or equal
to the sum of the prices of the two assets that are separately tradeable in the corresponding unbundled economy, and strictly less than the sum of these prices when
short sales constraints are binding for at least one investor on only one of the two
unbundled assets. In the absence of these conditions, there are situations that would
give rise to the bundle price exceeding the price of the two standalone assets, and I
will provide a, numerical example to illustrate this possibility.
Throughout the appendix, assumptions A1-A9 from Section 2.1.1 and the notation
of that section are maintained, and investors are assumed to agree on the variancecovariance matrix of asset payoffs (that is, Qk
2.7.1
Q).
Sufficient Conditions for Non-Negative Price Impact of
Unbundling
Conditions that limit the second order effect of rebalancing portfolios due to bundling
or unbundling (namely, the changes in prices of assets that are outside of the bundle,
due to rebalancing related to changing holdings of the bundle assets but in the face
of short sales constraints on these non-bundle assets, which cause secondary impacts
on the prices of the bundle assets) can guarantee a non-negative price impact of
unbundling. I will provide two sets of such sufficient conditions. While they are
somewhat restrictive, it is important to note that these are sufficient but not necessary
conditions, and they are intended to illustrate the channel that must be limited in
order to result in a non-negative price impact of unbundling.
The following notation will identify the investor groups presented in Section 2.1.3:
(i)
0
b
encapsulates groups 1, 2, and 3 as defined in Section 2.1.3, and is the set of
investors who hold the bundle in the bundled economy (and who may hold some
or none of assets 1 and 2 in the unbundled economy); (ii) 0o0o represents group 4
89
as defined previously, and is the set of investors who do not hold the bundle or its
component assets; and (iii) 00,1 and 00,2 are two subgroups of group 5 as defined
previously, specifically the sets of investors who do not hold the bundle but hold
either asset 1 or asset 2 (respectively) in the unbundled economy. The group 0o is
the union of the groups in (ii) and (iii).
Also, define the incremental hedge portfolio, consisting of assets in set <D where
assets outside of this set are assumed to be held constant (hence it is an "incremen-
tal" hedge), for assets i = 1. 2 as h?.
For <D consisting of assets 3 to N (assets
outside of the bundle), these incremental hedge portfolios are thus denoted h
.
and h3-.Given the optimality condition from equation 2.8, the elements of these
hedge portfolios must satisfy
ZumAxrj
0.
ijn>
Solving the N-2 equations in (2.20) for Axj,
j
(2.20)
2
> 2, (that is, the elements of the
hedge portfolio) gives
AX[3-N]
where
0-[i,3-N]
Q[3-N]
=
h-NAxi
=
-Q-1N
i,3-N] AXi
=
1 2
(2.21)
is the submatrix of Q excluding the first two rows and columns, and
is the subvector of covariances of asset i (with assets 3 through N). Note
that a hedge portfolio is the same for all investors since there is agreement on the
variance-covariance matrix. The element of the portfolio corresponding to asset
be denoted as h[ (j), and an asset
j
j will
will be said to be part of a hedge portfolio if
h' (j) # 0.
Assume that an investor starts with an optimal portfolio (satisfying the first order
conditions from equation (2.8)) and then changes his holdings of assets 1 and 2 by Ax 1
and Ax 2 (e.g., in response to a change in constraints). Importantly, the incremental
hedge portfolios h 13 N,]r areigdefined such that changihihodnsf
his holdings of assets 3 to N as
suggested by the two incremental hedge portfolios will then result in a new portfolio
that again satisfies the first order conditions from (2.8) as long as the prices and
90
shadow costs associated with assets 3 to N do not change. I will apply this property
when considering the propositions that follow.
Proposition 1. If (i) there are no investors in sets 00.1 and O02 and (ii) the short-sale
constraint is never (in the bundled or unbundled equilibrium) binding for
k E Obwith respect to assets that are part of either or both of h.i-
and
'[3 N]
A-],
then Pbb < (p* + pa).
Further, if short sales constraints bind
on one of the assets 1 or 2 in the unbundled economy for at least one
individual in
Ob,
then pga < (p* + p*,).
Proof: Let each investor hold their optimal quantity of the bundle in the bundled
equilibrium,
(2.22)
x k = x *.,k = ... K
It can be shown that the prices and shadow costs of assets 3 to N are the same in
the bundled and unbundled economies given assumptions (i) and (ii) of the proposition. By definition of the incremental hedge portfolios, the optimal quantities of each
other asset i held by each investor in the bundled economy are then
xk*
-
_k*
+ h-(i)
[Xk*
_
4*]
+ h -N](i) [x * - x *] , i > 2, k = 1, ...K
(2.23)
Given these optimal quantities, and assumptions (i) and (ii), it can be shown that
the equilibrium prices in the bundled economy (relative to the prices in the unbundled
economy) are then
Pib =
K) i
(2.24)
>2
and
1
Pb*b = [p
-
+ p 1] +
-
bb
-kc0l
keOb
91
a
(2.25)
where the shadow costs are given by
Ak,= Ak, i > 2, k = 1,...K
Abb
-
u++
b=
- 0.,k
E
(2.26)
(2.27)
()b
P[pb - (piu +
p*u)] k E 0Oo
(2.28)
Note that (2.26) and (2.27) are used to evaluate the expression in (2.25), which is
then used to evaluate (2.28).20
Finally, given (2.27) and the non-negativity of shadow costs, (2.25) implies that
Pib
(p*u + p*u). Further, if short sales constraints bind on one of the assets 1 or 2
in the unbundled economy for at least one individual in
positive A k and/or A'
for at least one individual in
6
Ob,
,
then there would be some
and therefore (2.25) would
imply P*b < (p* + p2*
Proposition 2. If (i) asset 2 is not part of the hedge portfolio for asset 1 and vice
versa, that is hN](2) = 0 and hi3N](1) = 0 and (ii) the short-sale
constraint is never (in the bundled or unbundled equilibrium) binding for
k E
[,
00,100,2]
with respect to assets that are part of either or both of
hjN] andh 3-N], then P2b
or
00,2
(P*
)+pi
Further,
1).
if (i) at least one of 0o.
is non-empty or if short sales constraints bind on one of the assets
1 or 2 in the unbundled economy for at least one individual in
0
b,
then
Pbb < (p1 + P2u).
Proof: First consider an additional, modified unbundled economy, identified by a
subscript m. In this economy, additional constraints restricting holdings of assets 1
and 2 to zero are imposed on individuals who do not hold the bundle in the bundled
economy. Thus, the additional constraints are:
20
Also note that combining (2.25) and (2.28) gives us equation (2.19) from earlier in the text.
92
k
Applying Proposition 1,2
< 0. i E (1. 2). k E
o
(
(2.29)
w have
Pbb
(2.30)
< (P*r + P*n)
It remains to compare the prices in the unbundled economy to those in the modified
unbundled economy. Let each investor hold their optimal quantity of assets 1 and 2
in the unbundled equilibrium:
=
ki*i
E (1. 2), k = 1. ...K
(2.31)
It can be shown that the prices and shadow costs of assets 3 to N are the same
in the unbundled and modified unbundled economies given assumption (ii) of the
proposition. Thus, by definition of the incremental hedge portfolios, the optimal
quantities of each other asset i held by each investor in the unbundled economy,
relative to their optimal holdings in the modified unbundled economy, are then
k*
i* =i*,
k*
1k
+ h
2
*
-N](')
[X]
+u
1
[k
~-rn]
[-
(-2
k*..K
,
i > 2-,k = 1,
where the hedge portfolios applying to assets 3 to N can be used because assumption
(i) of the proposition precludes assets 1 or 2 from appearing in the hedge portfolios
of each other.
Given these optimal quantities, and assumptions (i) and (ii), it can be shown that
the equilibrium prices in the bundled economy (relative to the prices in the unbundled
economy) are then
p* = p
i> 2
(2.33)
"The proof of Proposition 1 can be adapted to this situation by reflecting the shadow costs of the new
constraints. That is, for those individuals k E 00 for whom one of the new constraints from (2.29)
is binding, the corresponding A , in (2.28) is replaced by -6k. The conclusions are unchanged.
93
and
Piu
=
pi,4 +
a
1
[
-1
{.
A
-* --
-1
rn
kE b
os
wk(theh
(2.34)
i E (1, 2)
ak.
k
where the shadow costs are given by
Ai
= An,
Ai >Ai
A
A
= A
Tui
=
> 2, k = 1, ... K
(2.35)
E (1. 2), k E Ob
(2.36)
0, iE(1.2),kE0o
+ yo [P irr - Pn],
1, k e
(2.37)
[0oo, 00,2]
(2.38)
=
2, k E [Oop Oo,1]
Given (2.36) and the non-negativity of shadow costs, (2.34) implies that p* > pm
for each of i E (1, 2) which combined with (2.30) means that pb < (p* +pI). Further,
if (i) at least one of 00.1 or 00,2 is non--empty or if short sales constraints bind on one
of the assets 1 or 2 in the unbundled economy for at least one individual in
there would be some positive Ak or A'
6'
for some individual in
0b
Ob,
then
or some positive
for some individual in 00,1 or 00,2, and therefore (2.25) and (2.34) would imply
Ptb < (*,
2.7.2
p*r).
Numerical Example of Negative Price Impact of Unbundling
I provide a numerical example to demonstrate that, in the absence of the conditions
set forth in Proposition 1 or Proposition 2 (or other sets of sufficient conditions),
94
there exist situations that would give rise to the bundle price exceeding the price of
the two standalone assets because of the second order price effects discussed above.
The parameters in the unbundled economy are as follows.
/10 =1
ak
p"i
P=
P
= 1,k =1,...3
0 20]
0
=1
20
19.5
0
20
20
=3 19.5
2 1
A
1
1
2 0
1
0 2
1
1
1
I
Also, all three investors are restricted from short selling any of the risky assets,
that is:
Ck'
For the bundled economy, the bundle parameters are therefore:
pb = 0
2=
39.5
3
39.5
Obb
6
(-b3
1
95
-
b
= 0 k =1,...3
k
Given these parameters, the equilibrium prices and quantities can be calculated
numerically. The unbundled equilibrium is given by:
18.011363 18.25 18.954546
P* =
[0
*
0.909091 0.170455 0
x*
[ 0.090909
x
0 0.522727
0.829545 0.477272
[18.534090 18.249999 0
=,
=2
0 0 19.863637
A=
0 0 0
The bundled equilibrium is given by:
Pbb
=
36 308
.
d11,P
XI* = 0, x=
=
18.765958
0.617021
xbb* = 0.531915, x
= 0
=* 0.468085, x 3 = 0.382979
A'b = 36.925532, A'b = 0
= 0, A = 19.297873
Ab
0,A 3 b = 0
96
The optiiality of the solutions can be confirmed by applying the equilibrium price
and quantity equations for the bundled and unbundled economies from Section 2.1.
Notice that the short sale constraint on asset 3, which is part of the hedge portfolio
for assets 1 and 2, is always binding for investor 2., who holds the bundle in the
bundle equilibrium (and is thus in Gb). This causes condition (ii) of Proposition 1 to
be violated, so that proposition does not guarantee that the bundle price will be no
larger than the sum of the prices of assets 1 and 2 above. In fact,
36.308511 = Pb >
p*
+P*, = 18.011363 + 18.25 = 36.261363
so the given parameters lead to a negative price impact of unbundling. Notice that
investors 2 and 3, who hold the bundle in the bundle equilibrium, each continue to hold
positive quantities of both assets 1 and 2 in the unbundled equilibrium. The fact that
the short sales constraints on these individual assets are not binding for those investors
who are constrained by the requirement to hold the bundle assets in proportion in
the bundle equilibrium means that the primary price impact of disagreement and the
related reshuffling of holdings is zero. This allows the secondary price impact, which
results from the movement of the price of asset 3 and happens to be negative in this
case, to dominate in this situation.
97
98
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