Inefficient Markets, Efficient Investment?∗
Justin Birru†
The Ohio State University
March 2014
Abstract
I examine the effect of stock market misvaluation on corporate investment using a novel measure
of relative market mispricing, dual-listed share deviations from parity. In aggregate, firms in
countries with relatively overpriced equity invest more and raise more external finance. This
pattern appears to be driven by the most financially constrained firms; unconstrained firms
do not respond to relative market mispricing, indicating the results cannot be explained by
differences in rational discount rates across countries. Specifically, the effect is confined to those
constrained firms belonging to industries that are dependent on external capital to finance
growth. The results are consistent with stock market mispricing relaxing financial constraints
and facilitating closer to first-best investment levels.
∗
I thank my committee, Jeffrey Wurgler, Stephen Figlewski, and Andrea Frazzini for valuable discussions. I
would also like to acknowledge helpful comments from Yakov Amihud, André de Souza, William Greene, Marcin
Kacperczyk, Anthony Lynch, Holger Mueller, and seminar participants at Harvard Business School, NYU Stern,
Ohio State University, University of California Irvine, UCLA, University of Miami, and Yale University.
†
Fisher College of Business, The Ohio State University. Email: birru.2@fisher.osu.edu
1. Introduction
Whether stock market inefficiencies spill over to the real economy by affecting corporate
investment and financing decisions is an important and long-studied open question with farreaching implications. This paper examines the extent to which stock market mispricing affects
the behavior of the firm. Specifically, I focus on the effect of aggregate stock market misvaluation on corporate investment and financing decisions. I then ask the next logical question: Can
mispricing facilitate efficient investment?
A link may exist between mispricing and investment for a number of reasons. Irrational variations in stock prices may hinder the efficient allocation of capital, resulting in distortionary
investment. On the other hand, mispricing has the potential to relax financing constraints
via its effect on the cost of capital, and to therefore facilitate closer to first-best investment
levels. Over the years, a number of empirical papers have set out to examine whether a relationship exists between stock market mispricing and corporate investment.1 Despite the wealth
of research, empirical evidence that market-wide mispricing encourages efficient investment is
lacking.2 Furthermore, no consensus has emerged regarding an answer to the more fundamental
question of whether market mispricing affects investment in the first place. The lack of consensus reflects the daunting challenge for any empirical test of market inefficiency, identifying
a viable measure of mispricing, a variable which is innately difficult to pin down. To overcome
this challenge, I utilize a novel measure of relative aggregate market mispricing, Siamese twin
stock deviations from parity. Armed with this measure of market mispricing, and a panel of
international data, I provide evidence consistent with market-wide mispricing facilitating efficient investment through its effect on the cost of capital.
1
A non-comprehensive list includes Fischer and Merton (1984), Barro (1990), Morck, Shleifer, and Vishny
(1990), Blanchard, Rhee, and Summers (1993), Galeotti and Schiantarelli (1994), Chirinko and Schaller (2001),
Baker, Stein, and Wurgler (2003), Goyal and Yamada (2004), Farhi and Panageas (2005), Gilchrist, Himmelberg,
and Huberman (2005), Massa, Peyer, and Tong (2005), Campello and Graham (2007), Polk and Sapienza (2009),
and Bakke and Whited (2010).
2
Using firm-specific measures of mispricing, Baker, Stein, and Wurgler (2003), and Campello and Graham
(2007) find some evidence that firm-specific mispricing can encourage efficient investment.
1
Specifically, I find that firms in relatively overvalued countries invest more and raise more
external equity finance than firms in countries with relatively undervalued equity. Consistent
with overpricing relaxing financing constraints, I find that this effect is confined to only those
firms that are financially constrained, and specifically only constrained firms belonging to industries that are dependent on external capital to finance growth. As explained below, the results
are consistent with the behavior of a long-run value maximizing manager, and inconsistent with
alternative value-destroying theories. I next turn to introducing the novel measure of mispricing
used before discussing the various theories linking mispricing to investment.
I utilize dual-listed company share deviations from parity as a measure of relative market
sentiment. A dual-listed company structure (DLC) involves two companies operating in different countries agreeing to function as a single entity, but maintaining separate stock listings. A
dual-listed company (often referred to as a ‘Siamese twin’) has shares traded in two different
markets, however the shares are claims to the same underlying cash flows, and as a result, the
stock prices of the two shares should always trade in a fixed ratio. Furthermore, because the
shares are claims on the same underlying cash flows, price deviations from this fixed ratio can
not be interpreted as compensation for risk, in contrast to many previously identified proxies for
mispricing. This measure of mispricing has the advantage that it does not hinge on a model of
market equilibrium. Rather, twin deviations from parity literally represent a textbook violation
of the law of one price (Brealey, Myers, and Allen (2008), Bodie, Kane, and Marcus (2008)).
The widely accepted explanation for the deviation of twin prices from parity is that the
relative prices reflect noise trader behavior in segmented markets, and therefore reflect market
sentiment. Consistent with this explanation, the relative changes in twin prices are strongly
related to the relative changes in their local market indices at daily, weekly, monthly, and
longer horizons (Froot and Dabora (1999) and De Jong, Rosenthal, and van Dijk (2009)). Froot
and Dabora (1999) undertake a comprehensive study of potential rational explanations for
Siamese twin deviations from parity and conclude that “market-wide noise shocks from irra2
tional traders, which infect locally traded stocks more than foreign traded stocks, can explain
the comovements.”
Shleifer (2000) shares this view, arguing that systematic noise trader risk explains the existence and persistence of the twin phenomenon. He further points out that structural explanations, such as differences in tax rates, can not explain the fact that sometimes one twin trades
at a discount and at other times the other trades at a discount. Providing further support for
the use of twin deviations as a measure of mispricing, Baker, Wurgler, and Yuan (2012) construct country-level sentiment indexes for six different markets and employ twin deviations as a
validation test of their constructed country-level sentiment index measures. While the overlap
in country coverage only encompasses two countries (the United States and United Kingdom),
they nevertheless find that the relative twin prices are strongly positively correlated with the
local sentiment indices of the markets in which they trade. In brief, while twin deviations from
parity have long been thought to reflect market-specific mispricing, this is the first paper to
employ it as such.
If relative twin share prices reflect relative market mispricing, then twins sharing the same
pair countries should see their relative price ratios comove. Figure 1 plots the deviations from
parity for Royal Dutch/Shell and Unilever NV/Unilever PLC over the time period 1980 to
2002. These twins happen to trade in the same pair markets. Shell and Unilever PLC were
both traded in the UK, while Royal Dutch and Unilever NV both traded in the Netherlands,
and were also components of the S&P 500. To the extent that deviations from parity reflect
movements in market sentiment, we would expect the deviations from parity for these twins to
move together. Figure 1 shows that this is the case, even though these twins are in very different
industries (Royal Dutch/Shell is in the oil and gas industry, while Unilever NV/Unilever PLC’s
business spans a number of industries, most notably food and beverage). Indeed, the annual
(daily) correlation of relative twin share price deviations from parity for the two DLCs over this
time period is 0.88 (0.87). Section 2 provides a more thorough discussion of dual-listed shares.
3
How might mispricing facilitate efficient investment? Faced with the reality of overpriced
equity, a long-run value-maximizing manager will issue equity and invest the proceeds in cash,
effectively transferring value from new shareholders to existing shareholders. To the extent that
market inefficiency leads to a redistribution of wealth from one shareholder to another, market
misvaluations will have no real economic impact. However, in the presence of financing constraints preventing a firm from funding its marginal investment, a long-run value-maximizing
manager will issue equity to finance investment in times of overpricing, but will be less likely to
do so in the presence of underpricing. Unconstrained firms, however, will have investment that
is unaffected by the availability of irrationally cheap funds. This is because overpricing allows
managers to gain access to funds that are irrationally cheap, however it does not change the
rational cost of capital managers use to evaluate projects. Constrained firms, that by definition
are unable to access funds to finance all of their positive-NPV projects, will use the access to
cheaper financing to finance new investment, while those firms not facing financing constraints
will always invest at the first-best level and have investment that is unaffected by mispricing.
To the extent that stock market overpricing can reduce the effects of financing frictions
within a country, by decreasing the cost of capital, there should be a positive link between stock
market mispricing and aggregate investment. Of course, this hypothesis relies on the existence
of financing constraints that have distortionary effects on investment. Indeed, overwhelming
evidence suggests that despite the increasing integration of capital markets, country-specific
financing frictions do exist, and lead to inefficient levels of aggregate investment in even the
most developed countries. Campello, Graham, and Harvey (2010) provide evidence in direct
support of the presence of pervasive financing frictions throughout the world, and the adverse
effect that these frictions have on corporate investment. Through a global survey of managers in
the midst of the recent financial crisis, they find that 86% (44%) of constrained (unconstrained)
firms restricted investment in attractive projects. Additionally, more than half of respondents
were forced to cancel or postpone planned investment as a result of the credit crisis. Given
4
the ability of financing constraints to strongly affect investment, the potential exists for stock
market overpricing to play a welfare-enhancing role by counteracting imperfections in financial
markets.
While the idea that mispricing can induce investment is not new, models by Stein (1996),
Farhi and Panageas (2004), Gilchrest, Himmelberg, and Huberman(2005), and Jermann and
Quadrini (2006) have only relatively recently formalized the link between bubbles and investment. Each shares the common implication that by reducing the cost of capital, stock market
bubbles can encourage investment. The important takeaway from these models is the potential
for overpricing to benefit firms that face frictions in obtaining financing, facilitating investment
for firms that otherwise would have to pass up positive-NPV investment projects, thereby helping to overcome underinvestment problems.
On the other hand, there are a number of theories predicting that mispricing will have a
distortionary effect on investment and the allocation of capital. For example, if managerial and
investor sentiment is correlated, managers will invest precisely when investors are overoptimistic.
Similarly, an empire-building manager may use overpriced equity as a cover to undertake selfaggrandizing investment. A third possibility, tested by Polk and Sapienza (2009), is that myopic
managers cater to investor sentiment by investing when investors are overly optimistic, in order to maximize the firm’s short-term share price. Ultimately, whether mispricing-motivated
investment is efficient or inefficient is an empirical question.
To be clear, mispricing is relative to a perfectly-integrated-global-market benchmark. One
could also tell a story in which mispricing does not exist, and the measure of market mispricing, Siamese twin deviations from parity, instead reflects frictions preventing cross-border
risk sharing. In this case, twin price premiums reflect differences in the market-wide price of
risk that will affect the rational cost of capital, and therefore investment, for all firms in the
country. Furthermore, if movements in twin prices are in fact capturing differences in discount
raters, they will likely be particularly representative of discount rate changes of firms similar
5
to the twin firms. The twin firms are among the largest firms in the world. The discount rate
channel therefore predicts that the investment of large, unconstrained firms will be most correlated with the price movements of twin firms. Conversely, if differences in twin prices do reflect
relative market mispricing, then the relaxation of financing constraints channel predicts that it
will only be the investment of constrained firms that is sensitive to twin price premiums. The
opposing theories provide clear cross-sectional hypotheses. The inefficient mispricing-motivated
stories predict that investment of both constrained and unconstrained firms will respond to
twin price premiums. The rational risk sharing story also predicts that both constrained and
unconstrained firms will respond to twin price premiums, with a potentially pronounced effect
among unconstrained firms. The relaxation of financing constraints channel, on the other hand,
predicts that only the investment of constrained firms will respond to twin price premiums.
Exploiting cross-sectional heterogeneity in firm-level financing constraints, I find that consistent with the relaxation of financing constraint channel, and inconsistent with the inefficient
mispricing-motivated channels and rational risk sharing story, investment is sensitive to twin
premiums for constrained firms, but insensitive to twin premiums for unconstrained firms.
The results can be summarized as follows. Using a novel measure of market-specific mispricing that exploits time-series variation in relative cross-market mispricing, I find that firms
in relatively overvalued countries invest more than firms located in relatively undervalued countries, and consistent with overpricing relaxing financing constraints, this link arises through
equity issuance. However, this does not rule out alternative explanations predicting inefficient
investment, such as the possibility that managerial and investor sentiment is correlated, and
that managers therefore invest and issue equity precisely when investors are overly optimistic, or
that empire-building managers use overpriced equity as a cover to undertake self-aggrandizing
investment. To distinguish between efficient and inefficient theories, I exploit a cross-sectional
prediction unique to the relaxation of financing constraint story, namely, that constrained firms
will have investment that is sensitive to mispricing, while non-constrained firms will not. To
6
undertake this test I employ a relatively new methodology, generalized propensity score matching (Hirano and Imbens (2004)). Consistent with this theory, I find that only constrained firms
have investment and financing that is sensitive to mispricing, specifically only those constrained
firms in industries dependent on external capital to finance growth. Finally, the analysis provides an estimate of the economic magnitude of the effect of mispricing.
As a whole, the results are not consistent with inefficient mispricing-motivated investment
theories, or a rational risk sharing explanation. Instead, the evidence provides support for the
hypothesis that mispricing-motivated investment exists and arises due to the ability of mispricing to relax financing constraints. The findings are consistent with the behavior of a long-run
value-maximizing manager. Overall, the evidence supports the view that market sentiment has
real economic implications.
The analysis proceeds in several steps. In the next section, I describe the measure of mispricing used in the study. Section 3 discusses the data, research methodology, and econometric
issues. Section 4 provides aggregate evidence on the effect of mispricing, which I further supplement with firm-level evidence. Section 5 examines whether the relationship between mispricing
and investment reflects efficient or inefficient firm behavior. Section 6 examines the robustness of the aggregate results to a mispricing residual measure that is purged of any potential
contaminants that do not reflect market-wide sentiment. Section 7 concludes.
2. Measure of Mispricing
DLCs are extremely rare, with only a handful in existence today. A DLC is created when
two companies operating in different countries choose to merge and become one entity. However, in contrast to a typical merger, the new single entity continues to maintain separate stock
listings in both of the countries where the two companies formerly traded. There are two different structures that a DLC can take. In the first, the business operations of the two companies
merge under one or more intermediate holding companies. Though the assets of the companies
7
are combined at the holding company level, the companies continue to be listed separately and
hold shares in the intermediate holding company. The two listed companies function only to
receive dividends from the holding company, and to distribute these dividends to their own
shareholders. In the second type of structure, there is no transfer of assets, and the business
operations of the companies are not jointly owned. However, a unified management is assured
by the presence of an identical board for each company. The companies agree to equalize their
dividends through an equalization agreement, and cross-guarantee the dividends of the other
by agreeing to make up any shortfall in dividend payments by the other company in the event
of inadequate funds.
There are a number of reasons why companies may choose to enter into a dual-listed company structure. Tax benefits arise because the DLC structure avoids capital gains taxes that
would result from a conventional merger, and also minimizes cross-border dividend payments.
Increased capital market access is an often-cited reason, resulting from increased liquidity and
visibility provided by multiple listings that facilitate the ability to attract domestic investors in
two different markets. A further impetus to DLC creation is the fear of flowback. In a traditional
stock acquisition between two companies domiciled in different markets, target shareholders receive stock of a foreign-listed company, flowback then arises due to selling pressure from index
funds and investors in the home country of the target stock. A DLC structure also appeals to
companies with a strong sense of nationalism that seek to avoid the appearance of being taken
over by a foreign competitor. Finally, many merger difficulties are avoided, such as shareholder
approval, regulatory consent, and the avoidance of certain rights that may be triggered in the
event of a takeover.
Rio Tinto is a typical example of a dual-listed company. The company came into existence
in 1995 when Australian mining company CRA merged with UK-listed RTZ. Rio Tinto Limited
shares are traded on the Australian Stock Exchange, and Rio Tinto PLC shares are traded on
the London Stock Exchange. The sharing agreement states that the dividend and capital rights
8
are on a 1:1 basis, implying that the shares should trade in a fixed ratio of 1:1. In reality,
the relative prices of the shares deviate substantially from this theoretical parity ratio, and the
deviations persist over extremely long periods of time.
Froot and Dabora (1999) and De Jong, Rosenthal, and van Dijk (2009) find that the relative
changes in twin prices are strongly related to the relative changes in their local market indices
at daily, weekly, monthly, and longer horizons. For example, when the Australian market appreciates in value relative to the UK market, Rio Tinto Limited increases in value relative to
Rio Tinto PLC. Additionally, Bedi, Richards, and Tennant (2003) find that after unification of
a DLC, prices comove even more strongly with the market index of the new primary market,
and there is no longer comovement with the market index from which the DLC is delisted.
Froot and Dabora (1999) find that the magnitude of the deviation, as well as the substantial
time-series variation can not be explained by rational justifications such as dividends, parent
expenditures, currency fluctuations, voting rights issues, or tax-based explanations. While a
number of various rational explanations have sought to explain the deviation of the ratio of
stock prices from theoretical parity, no explanation relying on fundamentals has come close to
being able to explain the observed variation. Rather, twin deviations from parity seem to reflect
country-level investor demand that affects local market valuations.
Finally, Baker, Wurgler, and Yuan (2012) provide additional support for the view that DLC
price deviations reflect market-level mispricing. They find that deviations from parity are correlated with their constructed sentiment measures. They argue that because DLC price deviations
reflect investor sentiment, the correlation of their measures with deviations from parity can be
taken as compelling evidence that their measures capture sentiment.
Why aren’t the deviations in twin prices simply arbitraged away? The existence, as well as
the observed magnitude and persistence of the mispricing, reflect limits to cross-country arbitrage, and the lack of fungibility of twin shares, hampering arbitrageurs in restoring prices to
parity. Because the shares are not fungible, there is no identifiable date at which prices will
9
converge, requiring any potential arbitrageur to have a long horizon and the ability to withstand
short-term losses in the case of a widening in the mispricing. A clear example of the noise trader
risk involved in attempting to arbitrage the mispricing is conveyed by the case of Long Term
Capital Management. LTCM took a $2.3 billion bet on the Royal Dutch/Shell twin to profit
from the overpricing of Royal Dutch relative to Shell, going long Shell, and short Royal Dutch.
Lowenstein (2000) claims that the mispricing of Royal Dutch relative to Shell increased from
8% to 22% after LTCM entered into its position, and that LTCM ultimately lost $286 million
on equity pairs trading between the beginning of 1998 and its eventual bailout, with over half
of this loss accounted for by the by the bet on the Royal Dutch/Shell twin. Bedi, Richards,
and Tennant (2003) provide additional anecdotal evidence motivating the use of twin deviations
from parity as a measure of relative overvaluation. They find that unification of twin shares
generally results in the company placing the new primary listing on the market with the higher
valuation of the twin companies, in effect arbitraging mispricing for their own benefit.3
Two other assets, ADRs and single country closed-end funds, also trade at prices that at
times deviate from the prices of their foreign traded underlying securities. These discounts seem
like natural choices as additional measures of cross-country market sentiment; however, it turns
out that neither is a valid measure of cross-country market sentiment.4 Due to the fungibility
of ADRs, their prices are very closely tied to the price of the underlying shares. For example,
the ADR on Shell has an average absolute monthly deviation from the price of the UK traded
underlying security of less than 1% over the 1989-2004 period. While the lack of fungibility of
dual-listed shares prevents arbitrageurs from aligning prices, the ability to exchange ADRs for
the underlying stock causes price differentials to be easily arbitraged away. As a result, ADR
discounts reflect little more than the transaction costs of undertaking such arbitrage.
3
Perhaps the most obvious way for twin firms to arbitrage the mispricing of their shares is to issue equity in
only the more highly valued market. However, in practice the firms generally issue equity in both countries in a
ratio that is in accordance with the predetermined equalization ratio.
4
In order to simplify terminology I refer to deviations from underlying asset prices in terms of a discount. A
premium is simply a negative discount.
10
On the surface, single country closed-end funds seem to have promise as another potential
measure of cross-country sentiment. These funds trade in the US, while holding portfolios of
securities traded in a single non-US market. The market value of these funds is determined in
the US, while the market value of the underlying securities is determined in the home country of
the assets. The fund discount is therefore a representation of US sentiment toward the foreign
market on which the underlying assets are listed, rather than a measure of the foreign country’s
domestic investor sentiment. Indeed, Hwang (2011) finds that the single country closed-end
fund discount is associated with the country’s popularity among Americans. Since it is domestic market sentiment that affects the prices of assets domestically, and therefore affects domestic
firm behavior, a measure of US sentiment toward a foreign market will not be informative about
the actions of domestic firms in these foreign markets.
To formalize the intuition behind using twin deviations from parity, I next provide a simple
model similar to Scruggs (2007). The return on stock i can be written as
ri,t = βi ft + ei,t + βis SA,t + ηi,t
(1)
where i indexes firm, t denotes time, and A indexes country. ft is a vector of systematic risk
factors, and βi represents factor loadings for stock i. ei,t represents the firm-specific fundamental shock, reflecting firm-specific news that is diversifiable across stocks. SA,t is aggregate
market sentiment in country A, and βis represents stock i’s loading on the market sentiment
factor, which is assumed to be orthogonal to the fundamental news and not diversifiable across
firms. ηi,t is the firm-specific sentiment shock, reflecting firm-specific changes in noise trader
sentiment regarding stock i.
The fundamental components, βi ft and ei,t , will be the same for twin stocks:
11
βi ft = βj ft
ei,t = ej,t .
The difference in returns between twin shares is equal to
s (S
ri,t − rj,t = βi,j
A,t − SB,t ) + (ηi,t − ηj,t ).
(2)
I assume constant β s across twin stocks.5 The difference in returns captures the difference in
systematic noise shocks in country A and B. To the extent that firm-specific noise shocks differ
across twin countries (ηi,t 6= ηj,t ), this will be a source of measurement error in the measure of
relative market sentiment, leading to overly conservative estimates of the effect of mispricing
as the coefficient on aggregate mispricing will be biased toward 0 due to attenuation bias. I
specifically address this issue in Section 6 by creating a residual measure that is purged of any
potential firm-specific sentiment differences across the pair countries.
3. Data and Empirical Methodology
3.1 Data
The data used in the empirical analysis comes from a number of sources, including the World
Bank (World Development Indicators), Worldscope, Datastream, and SDC Global New Issues
Database. Appendix A gives a detailed description of the variables used in the study, and their
sources. Siamese twin deviations from parity are available through 2002 from Mathijs van Dijk’s
website. I use data from Datastream to extend the time series and sample of twins through
the end of 2008, following the method outlined in Rosenthal and Young (1990) to calculate the
5
Baker and Wurgler (2006) suggest that β s is larger for stocks whose valuations are highly subjective, such
as small, young, high growth, unprofitable, and highly volatile stocks. All of the twin stocks are large, wellestablished companies that are likely to have very similar loadings on the aggregate sentiment factor.
12
theoretical price ratio. The measure of mispricing that I employ is the deviation of dual-listed
shares from parity, calculated as
P remiumA,B,t = log(
PA,t
) − log(Theoretical Parity Ratio),
PB,t
where P is the price of the twin share denoted in a common currency, A and B represent the
pair countries, and t denotes time.
The study utilizes 10 different DLCs, covering 9 different pair countries. Table 1 provides
details of the DLCs and the time period spanned by each pair.6 Twin premiums for each pair
are plotted in Figure 2. As can be seen from Figure 2, these twin premiums are often quite
large and persistent. Table 2 contains summary statistics for the main variables of interest for
the pairs.
3.2 Empirical Methodology
The empirical tests are structured to exploit the relative nature of the mispricing variable. I
first look at the response of relative investment to relative market mispricing. In other words, is
investment in country A high relative to investment in country B when country A is overpriced
relative to country B?
In the presence of a proxy for absolute market-level mispricing, I am interested in examining
InvestmentA,t = β1 M ispricingA,t−1 + β2 XA,t−1 + αA + αt
6
(3)
Because Royal Dutch and Unilever NV were members of the AEX index, and until 2002 also members of
the S&P 500 index, the prices of these shares reflected both Dutch and American noise trader risk, preventing
Royal Dutch/Shell and Unilever NV/Unilever PLC from being clean measures of relative mispricing betweeen
the Netherlands and the United Kingdom. Fortunately, the shares of Elsevier/Reed International also reflect
mispricing between the Netherlands and the United Kingdom. Prior to 2002, I use Elsevier/Reed International
as the measure of relative mispricing between the Netherlands and the United Kingdom. Post 2002, I splice the
Unilever series to the existing Elsevier/Reed International series. I do not use the post-2002 Royal Dutch/Shell
sample, as it announced the unification of its share structure on 10/28/2004.
13
where A indexes country, αA represents country fixed effects, αt represents time fixed effects,
M ispricingA is an absolute measure of market-specific mispricing in country A, and X is a
vector of control variables. Because my measure of market-specific mispricing, P remiumA,B , is
a relative measure of mispricing, rather than an absolute measure of mispricing, I can not identify whether increases to P remiumA,B are attributable to an increase in overpricing in country
A, or an increase in underpricing in country B.7 The latter of which should have no effect on
investment in country A, but should affect investment in country B. Given this relative measure of overvaluation, a proper specification would be to examine relative investment between
countries A and B as a function of the relative overvaluation. Differencing investment using
equation (3) yields
InvestmentA,t −InvestmentB,t = β1 P remiumA,B,t−1 + β2 (XA,t−1 −XB,t−1 ) + αA - αB . (4)
This is the baseline structure of the regression specifications used throughout the paper.
3.3 Econometric Methodology
An econometric issue arises when examining predictive regressions in small samples. When
examining time-series predictive regressions with a persistent predictor variable and a small
sample, coefficient estimates will be biased if innovations in the predictive variable are contemporaneously correlated with the dependent variable (Stambaugh (1986), Mankiw and Shapiro
(1986)). To generate coefficient estimates and p-values that correct for this spurious bias, I
implement a small-sample bias correction similar to that used by Baker and Stein (2004). The
procedure is a bootstrap estimation technique that is similar in spirit to the techniques first
employed by Nelson and Kim (1993) and Kothari and Shanken (1997). The specific details of
the procedure are outlined in Appendix B1.
7
or some combination of both
14
A second econometric issue, known as dynamic endogeneity, can arise if the explanatory
variable is not independent of past realizations of the dependent variable. While generally overlooked in the corporate finance literature, dynamic endogeneity has the potential to lead to
biased coefficient estimates in regressions with persistent dependent and explanatory variables.
I include fixed effects to control for time-invariant unobservable heterogeneity between countries;
however, fixed-effects estimates are only consistent under the assumption that the explanatory
variables are strictly exogenous. For bias to arise from dynamic endogeneity it should be the
case that an explanatory variable is strongly related to past values of the dependent variable,
and while this seems unlikely in the setting here, I nevertheless do not neglect the issue.
The methodology adopted in the literature to address dynamic endogeneity is system GMM,
which is not always appropriate for small samples. System GMM, such as that of Blundell
and Bond (1998), in which estimation is done simultaneously in differences and levels, with
lagged levels instrumenting differences, and lagged differences instrumenting levels has become
increasingly popular, especially in the growth literature. This methodology includes separate
instruments for each time period, with the number of instruments growing quadratically with T .
The downfall of this procedure is that in small samples the number of instruments quickly approaches the number of observations, and leads to overfitting instrumented variables, therefore
failing to expunge the endogeneity from these variables and biasing the coefficient estimates.
Indeed, for my sample, system GMM is not appropriate. The Hansen J test statistic often
rejects the null of joint validity of all instruments.8 Fortunately, dynamic endogeneity does not
turn out to be a serious concern. In Appendix B2, I show that any bias that may arise from
dynamic endogeneity either goes in the wrong direction, or is too small to influence the results.
8
See Roodman (2007) for a great discussion of the dangers of utilizing system GMM without first assessing
the validity of the procedure for the data being used. It also documents the potential for instrument profileration
to vitiate tests of instrument validity, leading to amplification of the potential for type I error.
15
4. Results
4.1 Investment
Table 3 analyzes the relationship between mispricing and aggregate investment. Aggregate investment is defined at the country level as gross fixed capital formation as a percent
of GDP. This variable is obtained from the World Bank (World Development Indicators). All
independent variables in the aggregate regressions are standardized to have zero mean and unit
variance. In column 1, I regress the difference in investment between country A and country
B on beginning of period relative overvaluation. The univariate results indicate that times of
relative overpricing are followed by times of relatively high investment. In terms of economic
significance, a one-standard-deviation increase in P remium predicts an increase in the difference in investment between country A and country B of almost 0.6% of GDP. Given that the
sample average annual gross fixed capital formation as a percentage of GDP is about 20%, this
represents an increase in the difference in investment between countries that is equal to about
3% of average annual country-level investment. This number likely represents a lower bound on
the effect of mispricing on investment since gross fixed capital formation includes data for all
firms in a country, including those that are too small to access public equity or debt markets,
and therefore less likely to reap the benefits from market-level overpricing. Consistent with this,
firm-level results examining the investment behavior of publicly traded firms later in the paper
find a larger effect.
I next follow the standard methodology of including profitability as a measure of fundamentals.9 The results in column 2 show that P remium is still a significant predictor of investment
after controlling for fundamentals. In columns 3 and 4 I replace profitability with cash flow and
Tobin’s Q respectively, with no change in outcome.10 In column 6 I include cash flow and Q
9
Aggregate profitability is defined as aggregate EBITDA scaled by the aggregate book value of total assets.
Aggregate Tobin’s Q is defined as the aggregate market value of common equity plus the aggregate value of
book assets less the book value of common equity all divided by the book value of total assets.
10
16
together as controls, and consistent with the investment literature cash flow is a stronger predictor of investment than Q. P remium continues to be economically and statistically significant
in all specifications, consistent with the existence of a mispricing channel in investment.11
12
Because all of the independent variables in the analysis are standardized to have zero mean
and unit variance, another way to characterize economic significance is to compare the regression
coefficients. The results in column 3 show that a one-standard-deviation increase in mispricing
has about half the effect on investment as a one-standard-deviation increase in cash flow.13
It could be the case that investment reflects growth opportunities arising over the course of
the year that are not captured by the beginning of period measures of growth opportunities.
To control for this possibility, contemporaneous measures of Q, cash flow, and profitability are
included in column 9. Another concern is that the measures of fundamentals are somehow
not fully capturing what they are intended to. To account for this, future Q, cash flow, and
profitability are included as additional proxies for the marginal product of capital. Column 10
includes time t + 1 Q, cash flow, and profitability, while column 11 also includes the t + 2 values
of these variables. The inclusion of these additional measures of fundamentals does not vitiate
the effect of mispricing.
The main concern in any test of mispricing is that the proxy for mispricing also reflects
fundamentals. The two most well-established proxies for the marginal product of capital, Tobin’s Q and profitability, have been used to mitigate this concern. I have also included GDP
growth to further capture country-level growth opportunities, as well as future realized values of
profitability, Q, and cash flow to control for investment opportunities not fully captured by the
use of lagged and contemporaneous proxies for investment opportunities. Abel and Blanchard
11
The results here, and throughout the paper are similar if I include the country A and B controls separately,
rather than as differences. In the interest of parsimony, I estimate all specifications with the controls as differences.
12
I have also verified that specifying the controls or dependent variable as log differences, rather than differences,
does not affect the results.
13
I have also tried using household consumption expenditure and gross domestic income as controls, as both
have been used in the past literature as measures of aggregate fundamentals. These variables have virtually no
predictive power after controlling for Q and cash flow. In alternate specifications I have also included variables
to capture market-wide interest rates, index levels, and inflation. The results are unaffected by the inclusion of
any of these variables.
17
(1986) suggest that the information in Q regarding investment opportunities may be smeared
by mispricing. If this is the case, then the effect of the market level mispricing measure on
investment will be understated, as Q will pick up some of the effect of mispricing.
Another concern is P remium is also capturing aggregate Q in addition to mispricing, that
is, P remium can be rewritten as
P remium = (Q + M ispricing),
(5)
where M ispricing is the true level of mispricing. In this case, the regression of interest is
Y = β1 M ispricing + β2 Q,
(6)
however, the model I am estimating is
Y = β1 P remium + β2 Q.
(7)
This can be rewritten as
Y = β1 (M ispricing + Q) + β2 Q.
(8)
By the Frisch-Waugh theorem, β1 will be identical in both estimations. Therefore, using
P remium to proxy for M ispricing will not bias the results.
4.2 Financing
This section explores whether the effect of mispricing on investment is accompanied by an
equity issuance channel. If overpricing relaxes financing constraints, then we should expect to
18
see greater equity issuance in the relatively overpriced pair country. I again begin by examining
the ability of the market component of mispricing to explain aggregate behavior.
Table 4 analyzes the following specification
IssuanceA,t − IssuanceB,t = β1 P remiumA,B,t−1 + β2 (XA,t−1 − XB,t−1 ) + αA − αB
(9)
where aggregate equity issuance is obtained from the SDC Global New Issues Database. Issuance is equal to the aggregate value of all domestic equity issuance as a fraction of GDP
(where GDP is measured in millions). I focus first on the strength of P remium as a financing
predictor in a univariate setting.
The results in column 1 show that P remium is a strong predictor of equity issuance. The
next column includes relative profitability, tangibility, cash, leverage, and assets as additional
predictors of issuance. Lastly, I include lagged GDP growth as an additional measure of macro
fundamentals in column 3. The significance of the premium variable persists in each specification, supporting the view that managers opportunistically time equity issuances, and providing
evidence that mispricing-motivated investment is accompanied by an equity issuance channel.
4.3 Firm-Level Analysis: Propensity Score Matching
The aggregate regression results show that overpricing predicts increased investment and
financing. These results are based on a relatively small sample of country-level observations,
and while I include a number of different controls, the specification employed constrains the
response of the dependent variable to the additional controls to be constant across all countries.
One could make the argument that there is heterogeneity in firm response to determinants of
investment and financing across different countries.14 To the extent that this is a legitimate
concern, I should include additional country-specific interaction terms to account for this. Given
14
For example, McLean, Zhang, and Zhao (2011) find that investment-Q sensitivity is higher for firms in
countries with strong legal protection of investors.
19
the relatively small sample size, this isn’t a feasible solution. For these reasons, and the inherent
noise in aggregate data, I now turn to Worldscope data to test whether the results are robust
to a firm-level analysis.
Beyond addressing the concerns just described, a firm-level analysis also benefits from the
ability to directly control for firm-specific investment opportunities, while focusing on the effect
of the market-level mispricing common to all firms. Because the mispricing variable is still a
relative measure, it is necessary to match firms in the pair countries in order to analyze relative
outcome variables. One matching method to control for differences in firm characteristics is
to identify firms in pair countries that are similar along a number of observable dimensions.
Because matching on a large number of covariates is impractical, I instead employ a generalized
propensity score matching procedure.
Ideally, I want to identify two firms at time t, one in country A and one in country B, that
have the same expected value of the outcome variable in the next period, and to then examine
the realized outcome variable as a function of market mispricing. The generalized propensity
score matching methodology allows me to match firms in pair countries on the predicted level of
investment or equity issuance, based on observables. Generalized propensity score matching is
a relatively new procedure first introduced by Hirano and Imbens (2004), that is seldom used in
the finance literature. It differs from propensity score matching in that it employs a continuous
treatment, rather than a binary treatment, allowing for matching on predicted investment or
equity issuance. The use of a propensity score matching methodology improves the matching
process in two ways. First, it reduces the dimensionality of the matching problem, eliminating
the difficulty of obtaining proper matches in a matched-pair research design when each observation is characterized by many relevant dimensions. Second, if the outcome variable sensitivity to
covariates varies across country, then simply matching on observed values of the covariates will
not be adequate to identify firms with equal expected outcome variables, instead the parameter
estimates will be biased if there is not an identical functional relationship between the control
20
variables and outcome variable across countries. To avoid this potential bias, the appropriate
first-stage regression should be estimated separately for each country. Because propensity score
methods have been used previously in the literature, I refrain from a detailed discussion here.15
The interested reader can see Rosenbaum and Rubin (1983) and Dehejia and Wahba (1999,
2002) for a detailed analysis, and Hirano and Imbens (2004) for a discussion of the generalized
propensity score analysis.
I first examine the robustness of the investment results at the firm level. In the first stage I
predict next period investment from a regression of investment on beginning of period Q, cash
flow, and firm fixed effects. Investment is defined as capital expenditures scaled by beginning
of period assets.
The first stage estimation uses data from all firms with at least $1 million in assets over
the years 1989-2007, although I’m only interested in matching firms for years in which I have
data for the mispricing variable. All variables in the firm-level analysis are winsorized at the
2.5% and 97.5% levels. I then perform a nearest neighbor match based on the predicted level of
investment, finding four matches from the larger country for each firm in the smaller of the pair
countries. Firms are matched with replacement to improve the accuracy of the match; doing
so reduces asymptotic bias (Dehejia and Wahba (2002)). I exclude observations that are not
in the common support, and employ a tolerance level of 0.05 when employing the matching to
avoid bad matches when the closest match is not too close. As an additional robustness check,
I include the first stage covariates in the second stage regression to address any remaining differences in characteristics.
Appendix C presents the first stage results. Each regression includes firm fixed effects, and
standard errors are clustered at the firm and year level. The results confirm that there is crosscountry heterogeneity in investment sensitivity to Q and cash flow. A comparison of pre and
post-match means of the predicted value of the dependent variable is shown in Panel B.
15
See Villalonga (2004) and Drucker and Puri (2005) for early examples of propensity score use in finance, as
well as detailed discussions of the propensity score methodology.
21
Using the sample of firms matched on predicted investment, I examine how realized investment differs as a function of mispricing. In Table 5, the difference in firm-level investment is
regressed on the twin price premium. Standard errors are clustered by two dimensions, firm i
and firm j, in order to correct for correlation induced by the presence of multiple firm observations arising from matching with replacement. As with the aggregate results, the effect of
mispricing on investment is economically and statistically strong. A one-standard-deviation increase in P remium predicts an increase in the difference in investment between firm i and firm
j of about 0.4% of total assets, which corresponds to about 5.5% of the sample average annual
firm-level capital expenditure. In the additional columns, I control for any bias in the matching
that may still be present by including the first stage covariates in the regression (expressed as
firm i minus firm j values). Including firm fixed effects in column 2 doesn’t affect the results,
nor does the inclusion of the additional controls in columns 3 and 4.
I also turn to propensity score matching as a robustness test of the aggregate equity issuance
results. In the first stage firms are matched on their predicted level of equity issuance. Issuance
is defined as stock issuance net of repurchases as a fraction of the firm’s beginning of period
assets. I employ the same control variables as in the aggregate regression, now measured at the
firm level. As in the investment propensity score matching, a separate regression is estimated
for each country, with four matches chosen in the larger country for every firm in the partner
country. The coefficient estimates from the first stage OLS regressions are shown in Table C2.
The results are consistent with the past literature. As expected, issuance is negatively related
to profitability, cash holdings, and size, and positively related to Q and leverage. The positive
coefficient on tangibility in all but one specification is consistent with the pecking order theory
in which low information asymmetry results in less costly equity. Panel B provides a comparison
of pre and post-match means of the predicted value of the dependent variable.
Table 6 uses the propensity score matched sample to examine the effect of market-specific
sentiment on equity issuance. The results of the matched sample resemble the aggregate results.
22
In terms of economic significance, a one-standard-deviation increase in P remium predicts an
increase in the difference in equity issuance between firms of about 1% of total assets, which is
about 44% of average annual firm-level equity issuance in the sample.
5. Is mispricing-driven investment value maximizing?
5.1 Financial Constraint Heterogeneity
I next examine a prediction unique to the relaxation of financing constraint channel, namely,
that constrained firms will have investment that is sensitive to mispricing, while unconstrained
firms will not. This hypothesis does not follow from the other theories. As before, I match firms
based on predicted next period investment and equity issuance as estimated in the first stage
regression. However, I now impose the additional restriction that the firms have the same level
of financial constraint. Within each country, I sort firms into terciles each year based on the
level of financial constraint, and constrain the matched firms to be in the same tercile.
How to measure financial constraint is a matter of much debate in the finance literature
(see Fazzari, Hubbard, and Petersen (2000) and Kaplan and Zingales (1997, 2000)). Kaplan
and Zingales (1997) propose an index to measure the level of financial constraint, in which they
argue that financially constrained firms have low cash flows, low dividends, low cash balances,
high leverage, and high Q. However, Almeida, Campello, and Weisbach (2004), Whited and
Wu (2006), and Hadlock and Pierce (2010) all find that the KZ index is not a valid measure of
financial constraint.
The one common link in the financial constraint literature (including Kaplan and Zingales
(1997), Almeida, Campello, and Weisbach (2004), and Hadlock and Pierce (2010)) is the finding
that smaller firms face more impediments to financing than larger firms. Because of its universal acceptance, I use size as my first proxy for financial constraint. Almeida, Campello, and
Weisbach (2004), identify four measures of financial constraint: size, payout policy, commercial
23
paper rating, and bond rating. As I do not have access to the latter two variables in all countries
of my analysis, I’m unable to use these proxies. I do, however, adopt dividend payout policy as
a second proxy for financial constraint. I also employ two index measures of financial constraint,
the Whited-Wu (2006) index, and the SA index of Hadlock and Pierce (2010). Whited and Wu
(2006) construct a financial constraint index via an investment Euler equation that classifies
small, low cash flow, low dividend, high leverage firms with low sales growth, but belonging
to high sales growth industries as being constrained. The SA index from Hadlock and Pierce
(2010) relies only on firm size and age to measure financial constraint.
Table 8 shows the results from the second stage regressions of investment and equity issuance
on P remium for the different financial constraint terciles. The table reports the coefficient on
P remium in the univariate specification and the full specification containing all of the control
variables. Size is defined as beginning of period assets (measured in US dollars), while payout
policy is measured as the ratio of total dividends to operating income.
Panel A (B) provides results for the investment (equity issuance) regressions. The efficient
mispricing-motivated investment theory predicts that the behavior of firms in the high financial
constraint tercile should be sensitive to mispricing, while that of unconstrained firms will not be.
The ‘Full Sample’ column of results ensure that the general result of investment and equity issuance sensitivity to P remium still holds in these new matched samples. Column 1 contains the
results for the most constrained firms (smallest size, smallest payout policy, largest Whited-Wu
index, and largest SA index values), while column 3 contains the results for the least constrained
firms. The results indicate that the sensitivity of investment and equity issuance to mispricing
is increasing in the level of financial constraint, consistent with the efficiency-enhancing mispricing story. Among the most constrained firms, a one-standard-deviation increase in P remium
leads to an increase in the difference in investment between firm i and firm j of about 13% of
average annual firm-level investment. The increase in equity issuance is even larger, and is able
to fully finance the observed increase in investment. In all specifications, the most constrained
24
group of firms has the largest coefficient on P remium of all terciles, while the coefficient on
P remium is not significant at the 5% level in any of the specifications for the least constrained
group of firms. The coefficients on the P remium variable from the full control specifications
are displayed in Figures 3 and 4, along with the 95% confidence intervals for the estimates.
5.2 External Finance Dependence
Heterogeneity in firm-level response to mispricing across constraint terciles is inconsistent
with theories predicting that investment arising from mispricing is inefficient. The constraint
results are also inconsistent with a story in which twin deviations from parity capture movements
in market-wide rational discount rates, as this would affect all firms, and not solely those that
are constrained. Additionally, because the identification of mispricing comes from stock price
movements of extremely large firms (dual-listed companies), the results are also not consistent
with the interpretation that twin stock movements are capturing movements in discount rates
(or growth opportunities) that are specific to constrained firms, but not unconstrained firms.16
I next exploit a finer prediction of the relaxation of financing constraint story. Specifically,
access to irrationally cheap (or expensive) external capital should disproportionately affect firms
in industries that are dependent on external finance for growth. Table 9 examines the response
of investment and equity issuance to mispricing for constrained firms, conditional on the external
finance dependence of the industries in which they operate. Constrained firms are classified as
belonging to industries with above or below median needs for external financing. Following
Rajan and Zingales (2008), I measure external finance dependence as capital expenditures
minus cash flows divided by capital expenditures, where the industry-level measure of external
finance dependence is the three-digit SIC industry median. In other words, firms that face
frictions to raising financing, that also operate in an industry dependent on external financing
16
Almost all of the twin firms were at one point among the largest 100 firms in the world, measured either
by assets or revenue. Therefore, if price movements of dual-listed firms are capturing discount rate movements
relevant to only a subsample of the universe of firms, then the relevant subsample would be large, unconstrained
firms, possessing qualities quite the opposite of financially constrained firms.
25
to fund investment, such as Pharmaceuticals, should have investment that is more sensitive to
mispricing than firms operating in an industry such as Tobacco, where firms are better able to
finance necessary investment internally. The results in Table 9 confirm that, consistent with
the relaxation of financing constraint story, it is indeed the case that constrained firms typically
dependent on external finance to fund growth exhibit the greatest sensitivity to mispricing.
6. Robustness: Premium Residual
Differences in twin prices might reflect some factors not related to broad-market sentiment,
and while I have included extensive controls throughout the paper, here I go a step further by
purging the dual-listed premium variable of any contaminants that do not reflect broad-market
sentiment. Specifically, I control for differences in liquidity, tax rates, exchange rates, and any
other firm or industry-specific components of mispricing that are relevant to the twin firms, but
not to the broader market. I regress the twin premium measure on these variables, and use the
residual from this regression as a cleaner measure of market sentiment.
To determine whether the twin premiums reflect market-wide noise shocks, Froot and Dabora
(1999) explore a number of potential rational explanations. Discretion in the use of dividend
income is one such potential explanation. Each company maintains a cash reserve to guard
against currency fluctuations, and therefore does not pay out all distributed group earnings as
dividends. Froot and Dabora explore the ratio of cumulative dividends for Royal Dutch/Shell
and find that they never deviate by more than 75 basis points from the 60:40 ratio. This is
far too small to explain the magnitude of premiums observed. I therefore exclude this variable
from my analysis.
Another possible justification for the twin premium is the existence of differences in expenses for the twin companies. However, Froot and Dabora find that in 1993, for example, the
magnitude of the differential in expenses never exceeded 6 basis points, again far too small to
explain the magnitude of premiums observed. I also exclude this variable from my analysis.
26
Differences in voting rights is another potential explanation. For example, Royal Dutch
has a 60% share in voting power, while Shell has only a 40% share in voting rights. However,
anti-takeover provisions make it very difficult to accumulate large blocks of control for the companies under analysis. Furthermore, this story can not explain observed periods in which Shell
is overpriced relative to Royal Dutch, and in order to account for the time variation in the
observed premiums, this story would require substantial time variation in the value of control.
Finally, Froot and Dabora explore whether differences in taxation across countries can explain the observed premiums, and conclude that tax explanations can not explain the mispricing.
For each pair country there is at least one investor group that is tax-indifferent, and the remaining groups of investors have differences in taxes that are far too small to explain the magnitude
of the twin premiums. Additionally, the time variation in twin premiums is far too large to
be explained by occasional changes in taxes. De Jong, van Dijk, and Rosenthal (2009) find
that there is virtually no change in price premiums on ex-dividend days, further suggesting
that differences in dividend taxation are not responsible for the twin premiums. Nevertheless,
I include the relative corporate tax rate as the first variable.
Equation (2) above shows that
s (S − S ) + (η − η ).
ri − rj = βi,j
i
j
A
B
If ηi 6= ηj , then this purported measure of market sentiment will be confounded by a firm or
industry-specific sentiment component. As it is the market sentiment component that is of
interest, it is necessary to eliminate any differences in firm-specific sentiment that may differ
across locales. I now turn to identifying measures that reflect differences in twin firm-specific
sentiment across markets.
To this end, I regress the twin premium on differences in twin share liquidity, and differences
27
in price levels of the relevant industry in respective markets. Twin prices may deviate due to
differences in liquidity of the twin shares, either due to rational risk concerns (Acharya and Pedersen (2005)), or differences in firm-specific sentiment (Baker and Stein (2004)). To the extent
that differences in twin stock liquidity do not reflect differences in market sentiment, I want to
purge the mispricing measure of any component that simply reflects firm-specific sentiment or
a liquidity premium. To measure liquidity I rely on the Amihud illiquidity measure
Illiquidity =
D
|rt |
1 X
(
)
D
V olumet
t=1
where rt is the stock return on day t, V olumet is the dollar volume on day t, and D is the
number of days in the year for which data is available. All variables are from Datastream.
Next, I control for industry-specific sentiment that may differ across markets. I want to
eliminate any industry-specific component of sentiment that is not related to market-wide noise.
I use the FTSE industry identification provided by Datastream to identity each twin’s industry,
and I obtain industry index level data from Datastream. I use the residual from a regression of
the industry index on market index to identify the idiosyncratic industry component.
The final variable that I include is the exchange rate. Although I have already converted
the twin price ratios into a common currency, past work has shown that relative prices do not
respond as fully as they should to exchange rate news. For example, when regressing returns in
local currencies on market returns and changes in exchange rate, past work finds a coefficient
of less than one on the exchange rate change variable, suggesting an underreaction to exchange
rate news. That is, a 1% appreciation in the pound relative to the Australian dollar should
result in a 1% appreciation in the price of the Australian share relative to the London based
share; however, empirically the reaction is less than it should be, meaning that a 1% increase
in the pound relative to the Australian dollar actually results in an increase in the price of the
28
London based share relative to the Australian domiciled share. Twin price premiums therefore
may have a component that simply reflects an underreaction to exchange rate news, and is not
related to relative market sentiment. This is accounted for via the inclusion of the exchange
rate variable.
The results of the premium residual regression are not reported, but the OLS coefficients on
illiquidity and exchange rate are significant and in the expected direction. The coefficient on
illiquidity is positive reflecting an illiquidity premium, while the coefficient on exchange rate is
negative reflecting underreaction to exchange rate news. The coefficients on the idiosyncratic
industry component and tax rate variable are both insignificant, and small in magnitude. I use
the residual from this regression as my cleaner proxy for mispricing and find that all of the
mispricing results above hold. The aggregate results with the residual mispricing variable are
shown in Table 10. The firm-specific results, which are omitted for brevity, are consistent with,
and slightly stronger than, the results with the raw measure.
7. Discussion and Conclusion
Traditional theories of investment assign no role to market sentiment in influencing corporate
investment. I utilize a novel measure of market-specific sentiment, Siamese twin stock deviations
from parity, in conjunction with international data from multiple sources, and document a role
for market sentiment in influencing corporate investment and financing decisions. Evidence
from country-level regressions, as well as a propensity score matched sample of firms, points to
mispricing-motivated investment.
Mispricing-motivated investment could be consistent with overpricing relaxing financing
constraints, resulting in efficient investment. On the other hand, it could also be consistent with
a number of stories pointing to inefficient allocation of capital and investment. To distinguish
between these conflicting motives, I exploit firm-level heterogeneity in financing constraints to
analyze differences in responses of firm-level investment and equity issuance to mispricing. The
29
sensitivity of investment and equity issuance to mispricing is greatest among those firms facing
the greatest financing constraints, while investment and equity issuance of unconstrained firms
exhibits no significant sensitivity to mispricing. Furthermore, I find that the effect is confined
to only those constrained firms belonging to industries that are dependent on external capital to
finance growth. The results provide support for the efficient mispricing-motivated investment
channel.
An alternative interpretation of the reuslts is that constrained firms invest optimally in the
absence of mispricing, and that overvaluation instead leads these firms to inefficiently overinvest.
This runs counter to the prevailing view in the literature (e.g., Campello, Graham, and Harvey
(2010), Franzoni (2009)) that constrained firms invest at below first-best levels. Ultimately,
however, whether investment is efficient or not is untestable.17
Finally, because the measure utilized in the analysis is a relative measure of mispricing, it
is of course possible that we are not encountering overvaluation in the absolute sense in any of
the countries examined over this time period. In this scenario, all markets are undervalued, and
some markets are more or less undervalued than others. Under this interpretation, the efficient
mispricing-motivated investment conclusions remain unchanged. In this context, the results
suggest that underpriced firms maximize value by trading off the extent to which a project
delivers a return over and above the rational cost of capital with the degree to which they
are underpriced; greater underpricing forces constrained firms to underinvest even more, while
investment increases as firms become less underpriced, and more projects deliver a return that
exceeds the degree to which the firm is underpriced. Again, the results suggest that increases in
relative overpricing (decreases in relative underpricing) allow firms to increase investment closer
to a first-best level. The results highlight one potential channel through which overvaluation
17
For example, attempting to test for efficiency via ex-post examination of changes in profitability or stock
returns does not lead to clear predictions. Assuming that firms invest in their highest marginal profitability
projects first, then although the relaxation of constraints allows firms to invest in positive-npv projects they were
previously unable to finance, the average profitability of the firm will decrease. With regards to returns, the
presence of overpricing, coupled with the well-documented inverse relationship between investment and future
returns, potentially arising for purely rational reasons, again leads to no clear predictions for future returns in
the presence of efficient investment.
30
can be welfare-enhancing, while not ruling out the potential for overvaluation to have negative
externalities through other channels.18 The findings suggest that stock market inefficiencies can
spill over to the real economy.
18
See Jensen (2005) for a discussion of some potential agency costs of overvaluation.
31
32
GDP Growth Per
Capita
Tax Rate
CF
ROA
Tangibility
Cash
Assets ($)
Investment
Equity issuance
Variable
Q
Appendix A:
KPMG Corporate and Indirect Tax Rate Survey
Corporate tax rate
Worldscope (firm-level, value-weighted avg)
Worldscope (firm-level, value-weighted avg)
Worldscope (firm-level, value-weighted avg)
Worldscope (firm-level, value-weighted avg)
Worldscope (firm-level, value-weighted avg)
World Bank (World Development Indicators)
SDC
World Bank (World Development Indicators)
Source
Worldscope (firm-level, value-weighted avg)
Description
[Market value of common equity + total assets - book value
of common equity]/total assets
Funds from operations scaled by lagged assets
Net Income scaled by lagged assets
PPE scaled by lagged assets
Cash and short-term investments scaled by lagged assets
Total assets measured in U.S. dollars
Gross fixed capital formation scaled by lagged GDP
Total amount of equity issued by domestic companies in
country as a fraction of GDP (GDP measured in millions)
Annual real percentage growth rate of GDP per capita
Description of Variables
Appendix B: Econometric Appendix
B.1 Stambaugh Bias
Small-sample bias of the type outlined in Stambaugh (1986) and Mankiw and Shapiro (1986)
is of particular concern in regressions forecasting returns when the predictor variable is a scaledprice variable such as the dividend-price ratio, and although my regressions do not take this
specific form, the problem is most easily understandable in this framework. For example, in the
context of returns
Rt = α + βxt−1 + ut ,
xt = θ + ρxt−1 + νt
ut ∼ i.i.d.N (0, σu2 )
νt ∼ i.i.d.N (0, σν2 )
(10)
(11)
an increase in return would coincide with an increase in price, and therefore a decrease in the
contemporaneous dividend-price ratio, leading to a negative correlation between νt and ut .
Stambaugh (1999) shows that the bias in the OLS estimate of β is
E[β̂ − β] =
σu,ν
E[ρ̂ − ρ]
σν2
(12)
where β̂ and ρ̂ are the OLS estimates of β and ρ. The bias is increasing in the absolute value of
the correlation in innovations, and is proportional to the bias in the OLS estimate of ρ in (11).
It is also linked to the persistence of the predictive variable, as Kendall (1954) proves that
E[ρ̂ − ρ] =
−(1 + 3ρ)
+ O(n−2 )
n
(13)
where n is the sample size. As a result, when the persistence of the predictive variable ρ is
33
large, the bias in the OLS estimate of β will be larger. Additionally, the bias is more pronounced in smaller samples. A negative correlation between innovations in return forecasting
regressions with scaled-price variables will result in an upward biased β coefficient. Although
the regressions here do not take this form, and it’s unclear the size or direction of the bias,
the predictive variables are nontheless persistent, although less so than variables in the equity
premium forecasting literature. To generate coefficient estimates and p-values that correct for
this spurious bias, I implement a small-sample bias correction similar to that used by Baker
and Stein (2004).
The procedure is a bootstrap estimation technique that is similar in spirit to the techniques
first employed by Nelson and Kim (1993) and Kothari and Shanken (1997). I estimate the
multivariate analog to (10), and estimate the multivariate analog to (11) as a restricted VAR.
Because the time dimension is small in my sample, I restrict the VAR coefficients to be identical
across pairs, but allow for pair-specific intercepts. To generate a bias-adjusted coefficient estimate, I generate a series of pseudo-independent variables and dependent variables by drawing
with replacement from the empirical distribution of the errors, u and ν. I select a random X0 ,
and substitute the draws of u and ν into (10) and (11). I draw 100 + n pairs and throw out the
first 100 draws. This procedure is repeated for 5,000 iterations, providing a set of coefficients
β ∗ . The bias-adjusted estimate is equal to
β̂ − (β ∗ − β̂).
(14)
To generate p-values under the null of no predictability I undertake a second set of simulations. I run separate simulations for each predictor, similar to the ones above, but now
imposing the null that βi = 0. This results in a second set of coefficients that form an empirical
distribution that is used to calculate p-values.
34
B.2 Dynamic Endogeneity
Applying fixed-effects estimation when sequential exogeneity is satisfied, but strict exogeneity is violated due to the presence of dynamic endogeneity leads to the following bias. As in
Wooldgridge (2002),
T
1X
0
plim(β̂F E ) = β +
E(x̃it x̃it )
T
"
t=1
#−1 "
#
T
1X
0
E(x̃it it )
T
t=1
where x̃it = xit − x̄i .
0
The direction of the bias of β̂F E depends on E(x̃it it ). Under the assumption of sequential
exogeneity,
h
i
0
0
E(x̃it it ) = E (xit − x̄i ) it = −E(x̄i it ).
Therefore,
T
T
1X
1X
0
E(x̃it it ) = −
E(x̄i it ) = −E(x̄i ¯i ).
T
T
t=1
t=1
In other words, if past values of the dependent variable are positively (negatively) related to
future values of the explanatory variable, then the fixed-effects estimate will be negatively (positively) biased.
In unreported analyses, I have examined the extent to which P remium is related to past
values of the dependent variables used in the analysis, in order to guage whether dynamic endogeneity is likely to be a concern. Univariate regressions reveal that investment and bond
issuance are positively related to future values of P remium. Both of these relationships would
lead to a bias that goes in the opposite direction of the observed empirical results.
35
Equity issuance is negatively related to future P remium, leading to fear that dynamic endogeneity could potentially bias the coefficient on P remium in these regressions in the observed
direction. However, the univariate relationship is extremely weak. P remium is insignificantly
related to past values of the dependent variable (t-stat of -0.27), and is also weak in terms of
magnitude. A one-standard-deviation increase in equity issuance is related to a movement in
future premium that is equal to 50 basis points, or about 7% of a one-standard-deviation move
in P remium. The relationship is not nearly of the magnitude necessary to cause concern of
bias arising from dynamic endogeneity.
36
37
Predicted Investment
Firm FE
N
R2
Cash Flow
Q
AU
0.014***
(0.001)
0.015**
(0.008)
Yes
9,769
0.625
Country A
0.084
BG
0.016***
(0.004)
0.144***
(0.036)
Yes
1,323
0.471
T-Stat Diff
7.183
Panel B
Panel A
FR
NL
0.010***
0.011***
(0.001)
(0.002)
0.083***
0.105***
(0.013)
(0.020)
Yes
Yes
8,740
2,545
0.508
0.517
Pre-Match
Country B
Diff
0.077
0.007
FN
0.010***
(0.003)
0.165***
(0.036)
Yes
1,734
0.349
Country A
0.076
SD
0.007***
(0.002)
0.046***
(0.013)
Yes
3,631
0.443
UK
0.012***
(0.001)
0.061***
(0.014)
Yes
21,442
0.540
Post-Match
Country B
Diff
0.075
0.000
SW
0.002
(0.002)
0.132***
(0.041)
Yes
2,012
0.350
T-Stat Diff
0.645
US
0.011***
(0.001)
0.043***
(0.006)
Yes
61,990
0.597
time are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. Panel B displays pre and post-match sample averages for predicted investment.
regression. The dependent variable is capital expenditures scaled by lagged assets. All independent variables are beginning of period values. Standard errors clustered by firm and
This table displays coefficients for firm-level investment regressions run separately for each country for the period 1989-2007. Each column displays coefficients from a separate
Table C.1
Investment 1st Stage
Appendix C:
38
Predicted Issuance
Industry FE
N
R2
log(Assets $)
Leverage
Cash
Tangibility
ROA
Q
FN
0.008**
(0.003)
-0.004
(0.028)
0.008
(0.015)
-0.003
(0.021)
0.055***
(0.019)
-0.008***
(0.003)
Yes
1,422
0.069
Pre-Match
Country B
Diff
0.021
0.020
BG
0.046***
(0.017)
-0.238***
(0.072)
0.052**
(0.023)
-0.004
(0.059)
0.061
(0.045)
-0.004*
(0.002)
Yes
673
0.227
Country A
0.041
AU
0.041***
(0.002)
-0.224***
(0.022)
0.097***
(0.014)
-0.034**
(0.017)
0.002
(0.023)
-0.025**
(0.003)
Yes
8,322
0.343
sample averages for the predicted dependent variable.
T-Stat Diff
14.012
Panel B
Panel A
FR
NL
0.018***
0.023***
(0.006)
(0.005)
-0.099**
-0.058
(0.048)
(0.042)
0.002
-0.006
(0.008)
(0.017)
0.013
-0.068***
(0.011)
(0.020)
0.032***
0.022
(0.011)
(0.023)
-0.003***
-0.006***
(0.001)
(0.002)
Yes
Yes
4,978
2,255
0.090
0.147
Country A
0.019
SD
0.031***
(0.006)
-0.294***
(0.031)
0.030
(0.018)
-0.013
(0.023)
0.027
(0.020)
-0.012***
(0.002)
Yes
2,266
0.307
UK
0.040***
(0.002)
-0.169***
(0.017)
0.021**
(0.010)
-0.026**
(0.010)
0.060***
(0.013)
-0.016***
(0.001)
Yes
20,309
0.234
Post-Match
Country B
Diff
0.019
-0.000
SW
0.013*
(0.006)
-0.086**
(0.041)
0.021**
(0.012)
-0.021
(0.019)
0.014
(0.015)
-0.005**
(0.003)
Yes
1,654
0.083
T-Stat Diff
-0.595
US
0.034***
(0.002)
-0.221***
(0.015)
0.047***
(0.006)
-0.004
(0.006)
0.054***
(0.005)
-0.013***
(0.001)
Yes
54,906
0.328
Standard errors clustered by firm and time are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. Panel B displays pre and post-match
is net ppe scaled by lagged assets. Cash is scaled by lagged assets. Leverage is the ratio of long-term debt to assets. Log(assets $) is the natural log of assets measured in US dollars.
regression. The dependent variable is stock issuance net of repurchases. All independent variables are beginning of period values. ROA is EBITDA scaled by lagged assets. Tangibility
This table displays coefficients for firm-level equity issuance regressions run separately for each country for the period 1989-2007. Each column displays coefficients from a separate
Table C.2
Equity Issuance 1st Stage
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44
Figure 1: Unilever NV/Unilever PLC vs Royal Dutch/Shell
Figure 2: Twin Premium
45
Figure 3: Sensitivity of Investment to Premium
Figure 4: Sensitivity of Equity Issuance to Premium
46
47
Company A
ABB AG
Carnival Corporation
Dexia France
Fortis
Merita
Reed Elsevier NV
Rio Tinto Limited
Smithkline Beecham
Unilever NV
Zurich Allied
Company B
ABB AB
P&O Princess Cruises PLC
Dexia Belgium
Fortis
Nordbanken
Reed Elsevier PLC
Rio Tinto PLC
Smithkline Beecham PLC
Unilever PLC
Allied Zurich
Years
1991-1999
2003-2008
1996-1999
1990-2001
1997-2000
1992-2008
1995-2008
1989-1996
2002-2008
1998-2000
Table 1
Country A
Switzerland
United States
France
Netherlands
Finland
Netherlands
Australia
United States
Netherlands
Switzerland
Country B
Sweden
United Kingdom
Belgium
Belgium
Sweden
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
48
Obs
9
6
4
16
12
4
14
8
3
Obs
9
6
4
16
12
4
14
8
3
Twin
ABB
CAR
DEX
ELS/UNI
FOR
MER
RIO
SMI
ZUR
Twin
ABB
CAR
DEX
ELS/UNI
FOR
MER
RIO
SMI
ZUR
deviation from parity.
Premium
0.102
0.028
0.059
0.082
0.042
0.059
0.069
0.055
0.089
Sample Standard Deviations of Differences
Premium
0.041
-0.031
-0.067
0.030
-0.027
0.002
-0.040
-0.060
0.068
Sample Means of Differences
Investment
1.115
0.739
0.510
1.189
0.523
0.439
1.870
1.772
0.359
Investment
6.511
1.709
-2.000
3.796
1.641
2.121
7.794
0.139
5.148
Equity
Issuance
1.034
0.083
0.714
0.570
0.594
0.207
0.679
0.509
1.007
Equity
Issuance
-0.507
-0.245
-0.189
-0.002
0.714
0.677
0.404
0.137
0.433
capital formation as a percent of GDP. Equity issuance is the total value of all domestic equity issuance as a fraction of GDP (GDP measured in millions). Premium is the twin
This table gives summary statistics for the main variables of interest. All variables are measured as differences (Country A value - Country B value). Investment is gross fixed
Table 2
Summary Statistics
49
Pair FE
N
R2
ROAt+2
Cash Flowt+2
Qt+2
ROAt+1
Cash Flowt+1
Qt+1
ROAt
Cash Flowt
Qt
GDP Growtht−1
ROAt−1
Cash Flowt−1
Qt−1
Premiumt−1
parentheses.
Yes
67
0.883
1
0.590
(<.01)
Yes
67
0.924
0.744
(<.01)
2
0.428
(<.01)
Yes
67
0.919
0.724
(<.01)
3
0.386
(<.01)
Yes
67
0.892
4
0.474
(<.01)
0.520
(0.05)
Yes
67
0.925
0.707
(<.01)
5
0.398
(<.01)
0.174
(0.44)
Yes
67
0.925
6
0.304
(0.04)
0.407
(0.08)
0.691
(<.01)
Yes
67
0.928
7
0.343
(0.02)
0.261
(0.26)
0.349
(0.24)
0.405
(0.17)
Yes
67
0.932
8
0.425
(<.01)
0.249
(0.28)
0.220
(0.45)
0.470
(0.11)
0.248
(0.09)
Yes
67
0.940
9
0.358
(<.01)
-0.035
(0.76)
0.007
(0.95)
0.509
(0.08)
0.243
(0.08)
0.448
(0.14)
0.427
(0.18)
-0.255
(0.23)
Yes
67
0.941
10
0.367
(<.01)
-0.024
(0.87)
0.034
(0.88)
0.555
(0.05)
0.206
(0.13)
0.484
(0.15)
0.392
(0.19)
-0.246
(0.34)
-0.254
(0.17)
-0.038
(0.91)
0.106
(0.80)
11
0.365
(<.01)
-0.110
(0.53)
0.114
(0.77)
0.571
(0.04)
0.259
(0.06)
0.482
(0.13)
0.381
(0.21)
-0.392
(0.22)
-0.502
(0.20)
-0.011
(0.96)
-0.001
(0.99)
0.422
(0.10)
-0.208
(0.58)
0.288
(0.54)
Yes
67
0.954
twin deviation from parity. All independent variables are standardized to have zero mean and unit variance. All coefficients are bias adjusted. Two-tailed bootstrap p-values are in
country B value. All independent variables are measured as differences (Country A value minus Country B value), and are measured as of the beginning of the period. Premium is
This table shows results for country-level investment regressions. The dependent variable is gross fixed capital expenditure as a percent of GDP for country A less the corresponding
Table 3
Aggregate Investment
Table 4
Aggregate Equity Issuance
This table shows results for country-level equity issuance regressions. The dependent variable is equity issuance as a
fraction of GDP (GDP measured in millions) for country A less the corresponding country B value. All independent
variables are measured as differences (Country A value minus Country B value), and are measured as of the beginning
of the period. Premium is twin deviation from parity. ROA is EBITDA scaled by lagged assets. Tangibility is net ppe
scaled by lagged assets. Cash is scaled by lagged assets. Leverage is the ratio of long-term debt to assets. Log(assets $) is
the natural log of assets measured in US dollars. All independent variables are standardized to have zero mean and unit
variance. All coefficients are bias adjusted. Two-tailed bootstrap p-values are in parentheses.
Premiumt−1
1
0.285
(<.01)
2
0.202
(0.04)
0.404
(0.03)
-0.006
(0.96)
-0.076
(0.75)
0.013
(0.95)
0.010
(0.94)
0.068
(0.90)
Yes
67
0.397
Yes
67
0.503
Qt−1
ROAt−1
Tangibilityt−1
Casht−1
Leveraget−1
log(Assets $)t−1
GDP Growtht−1
Pair FE
N
R2
50
3
0.217
(0.05)
0.407
(0.04)
-0.030
(0.79)
-0.072
(0.80)
0.004
(0.99)
0.010
(0.95)
-0.037
(0.93)
0.084
(0.38)
Yes
67
0.509
Table 5
Investment 2nd Stage
This table shows firm-level investment regressions for a propensity score matched sample of firms. The dependent variable
is capital expenditures scaled by beginning of period assets. All variables are measured as differences (Firm i value minus
Firm j value), and are measured as of the beginning of the period. Premium is twin deviation from parity. ROA is
EBITDA scaled by lagged assets. Standard errors clustered by firm i and j are in parentheses. ***, **, and * denote
significance at the 1%, 5%, and 10% level, respectively.
Premiumt−1
1
0.053**
(0.023)
2
0.064***
(0.023)
3
0.063***
(0.022)
0.002
(0.003)
0.027
(0.021)
0.030**
(0.014)
No
Yes
16,516
Yes
No
16,516
Yes
No
16,516
Qt−1
CFt−1
ROAt−1
GDP Growtht−1
Firm FE
Pair FE
N
51
4
0.078***
(0.025)
0.002
(0.003)
0.027
(0.021)
0.029**
(0.014)
0.003**
(0.001)
Yes
No
16,516
Table 6
Equity Issuance 2nd Stage
This table shows firm-level equity issuance regressions for a propensity score matched sample of firms. The dependent
variable is stock issuance net of repurchases for firm i minus the corresponding firm j value. All independent variables are
measured as differences (firm i value minus firm j value), and are measured as of the beginning of the period. Premium
is twin deviation from parity. ROA is EBITDA scaled by lagged assets. Tangibility is net ppe scaled by lagged assets.
Cash is scaled by lagged assets. Leverage is the ratio of long-term debt to assets. Log(assets $) is the natural log of assets
measured in US dollars. Standard errors clustered by twin are in parentheses. ***, **, and * denote significance at the
1%, 5%, and 10% level, respectively.
Premiumt−1
1
0.141***
(0.046)
2
0.138***
(0.046)
3
0.124***
(0.046)
-0.003
(0.006)
-0.044
(0.040)
0.024**
(0.011)
0.047**
(0.019)
0.057***
(0.017)
-0.017***
(0.003)
No
Yes
15,741
Yes
Yes
15,741
Yes
Yes
15,741
Qt−1
ROAt−1
Tangibilityt−1
Casht−1
Leveraget−1
log(Assets $)t−1
GDP Growtht−1
Industry FE
Pair FE
N
52
4
0.127***
(0.048)
-0.003
(0.006)
-0.044
(0.040)
0.025**
(0.011)
0.047**
(0.019)
0.057***
(0.017)
-0.017***
(0.003)
0.001
(0.003)
Yes
Yes
15,741
Table 7
Investment and Equity Issuance 2nd Stage Financial Constraint Terciles
This table shows coefficient estimates for the P remium variable from firm-level investment and equity issuance regressions
for a propensity score matched sample of firms sorted into terciles by level of financial constraint (proxied by firm size,
dividend payout policy, Whited-Wu index, and SA index). 1 is the most financially constrained tercile (smallest size,
lowest dividend payout, largest Whited-Wu index, largest SA index), 3 is the least constrained tercile. In Panel A, the
dependent variable is capital expenditures scaled by beginning of period assets. In Panel B, the dependent variable is
equity issuance scaled by beginning of period assets. All variables are measured as differences (Firm i value minus Firm j
value). ‘All Controls’ includes all control variables specified in column 4 of the appropriate second stage regression (refer
to Table 5 and Table 6). Standard errors clustered by firm i and j are in parentheses. ***, **, and * denote significance
at the 1%, 5%, and 10% level, respectively.
Panel A: Investment
Constraint
Criteria
Size
Model
Univariate
All Controls
N
Univariate
Payout
Policy
All Controls
N
Univariate
Whited-Wu
Index
All Controls
N
Univariate
SA
Index
All Controls
N
Full
Sample
0.079***
(0.022)
0.072***
(0.022)
16,197
1
0.183***
(0.060)
0.184***
(0.059)
5,308
Tercile
2
0.004
(0.035)
0.002
(0.035)
5,417
3
0.020
(0.022)
0.021
(0.021)
5,472
0.064***
(0.022)
0.058***
(0.021)
16,281
0.175***
(0.056)
0.171***
(0.055)
5,306
0.024
(0.040)
0.020
(0.039)
5,556
0.039*
(0.023)
0.035
(0.022)
5,419
0.054**
(0.023)
0.049**
(0.022)
15,996
0.160***
(0.060)
0.158***
(0.057)
5,314
0.012
(0.030)
0.006
(0.031)
5,355
0.018
(0.026)
0.023
(0.026)
5,327
0.070***
(0.026)
0.062**
(0.026)
14,629
0.176***
(0.065)
0.161**
(0.065)
4,803
0.051
(0.053)
0.047
(0.052)
4,948
-0.009
(0.030)
-0.003
(0.028)
4,878
53
Table 7 (Continued)
Panel B: Equity Issuance
Constraint
Criteria
Size
Model
Univariate
All Controls
N
Univariate
Payout
Policy
All Controls
N
Univariate
Whited-Wu
Index
All Controls
N
Univariate
SA
Index
All Controls
N
Full
Sample
0.151***
(0.049)
0.136***
(0.048)
15,369
1
0.389***
(0.140)
0.321**
(0.136)
4,896
Tercile
2
0.043
(0.066)
0.037
(0.067)
5,268
3
0.071*
(0.039)
0.070*
(0.037)
5,205
0.183***
(0.052)
0.155***
(0.050)
15,288
0.416***
(0.156)
0.391***
(0.150)
4,904
0.150***
(0.056)
0.128**
(0.052)
5,230
0.066
(0.053)
0.043
(0.052)
5,154
0.129***
(0.040)
0.109***
(0.040)
15,228
0.271***
(0.097)
0.202**
(0.102)
4,970
0.082
(0.057)
0.073
(0.058)
5,216
0.009
(0.057)
0.009
(0.053)
5,042
0.077
(0.049)
0.053
(0.051)
13,892
0.231*
(0.127)
0.189
(0.126)
4,465
0.026
(0.076)
0.035
(0.076)
4,745
-0.089*
(0.053)
-0.085
(0.053)
4,682
54
Table 8
Investment and Equity Issuance, Conditional on External Finance Dependence
This table shows coefficient estimates for the P remium variable from firm-level investment and equity issuance regressions
for a propensity score matched sample of firms for the most constrained group of firms (proxied by firm size, dividend
payout policy, Whited-Wu index, and SA index). ‘Non-dependent’ are firms in industries with below median external
finance dependence. ‘Dependent’ firms are firms in industries with above median external finance dependence. Standard
errors clustered by firm i and j are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% level,
respectively.
Constraint
Criteria
Size
N
Investment
Non-Dependent Dependent
0.012
0.189**
(0.077)
(0.088)
2,604
2,383
Issuance
Non-Dependent Dependent
0.202
0.608**
(0.139)
(0.264)
2,335
1,973
Payout
Policy
N
-0.017
(0.078)
2,254
0.255***
(0.086)
2,824
0.105
(0.177)
1,759
0.480**
(0.190)
2,648
Whited-Wu
Index
N
-0.025
(0.064)
2,644
0.331**
(0.135)
2,422
0.270**
(0.117)
2,396
0.331*
(0.178)
2,188
Size-Age
Index
N
0.011
(0.086)
2,126
0.297***
(0.099)
2,506
0.109
(0.163)
1,817
0.320
(0.249)
1,962
55
56
Q
0.237
(0.31)
Q
0.410
(0.02)
Premium
0.417
(<.01)
Premium
0.242
(0.01)
Investment
Equity
ROA
-0.112
(0.58)
CF
0.221
(0.45)
Tangibility
0.010
(0.86)
Cash
-0.012
(0.83)
Panel B
Panel A
GDP
ROA
Growth
0.464
0.252
(0.11)
(0.09)
coefficients are bias adjusted. Two-tailed bootstrap p-values are in parentheses.
Leverage
0.046
(0.85)
N
67
Assets
($)
-0.075
(0.78)
R2
0.931
GDP
Growth
0.054
(0.49)
N
67
R2
0.537
illiquidity, tax rate, and an industry index level residual. All variables are as defined before. All independent variables are standardized to have zero mean and unit variance. All
This table shows results for country-level regressions using the premium residual. Premium is the residual from a regression of twin deviation from parity on exchange rate,
Table 9
Premium Residual