Ineffcient Markets, Effcient Investment

8/2/2019 Ineffcient Markets, Effcient Investment

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Inefficient Markets, Efficient Investment?

(Job Market Paper)

Justin Birru

NYU Stern School of Business

[email protected]

January 2012

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 ag-

gregate, 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. The results

are consistent with stock market mispricing relaxing financial constraints and facilitating

closer to first-best investment levels. I also find that times of relative overpricing are

followed by relatively higher GDP growth and lower unemployment.

I thank my committee, Jeffrey Wurgler (Chair), Stephen Figlewski, and Andrea Frazzini for valuablediscussions. I would also like to acknowledge helpful comments from Yakov Amihud, Andre de Souza,

William Greene, Marcin Kacperczyk, Anthony Lynch, Holger Mueller, and seminar participants at NYUStern.

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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 far-

reaching implications. In this paper, I examine 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 ques-

tion: Is mispricing-motivated investment welfare-enhancing?

A link may exist between mispricing and investment for a number of reasons. Irrational vari-

ations in stock prices might 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, as

of now there is no empirical evidence that market-wide mispricing encourages welfare-enhancing

investment. Furthermore, no consensus has emerged regarding an answer to the more funda-

mental 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 overcomethis challenge, I identify 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 the first empirical evidence that market-wide mispricing can result

in welfare-enhancing investment through its effect on the cost of capital, as well as the first

evidence of links between market-wide mispricing and the macroeconomy.

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

1A 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).


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times of overpricing, but will be less likely to do so in the presence of underpricing. Uncon-

strained 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 irra-

tionally 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

the ability of financing constraints to strongly affect aggregate investment, the potential exists

for stock market overpricing to play a welfare-enhancing role by alleviating imperfections in

financial markets.

The belief that mispricing can induce welfare-enhancing investment is actually not a new

revelation. In describing the investment boom that accompanied the stock market boom in the

years preceding the Great Depression, Keynes (1931) noted:

While some part of the investment which was going on...was doubtless ill judged and

unfruitful, there can, I think, be no doubt that the world was enormously enriched

by the constructions of the quienquennium from 1925 to 1929; its wealth expanded

in those five years by as much as in any other ten or twenty years in its history... A

few more quinquennia of equal activity might, indeed, have brought us near to the

economic Eldorado where all our reasonable economic needs would be satisfied.

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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 invest-

ment. 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, allowing efficient in-

vestment by firms that otherwise would have to pass up positive-NPV investment projects, and

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 self-

aggrandizing 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 or-

der to maximize the firms short-term share price. Ultimately, whether mispricing-motivated

investment is welfare enhancing or welfare decreasing 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 mispric-

ing, 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 for all firms in the country. Conversely, if differences

in twin prices do reflect relative market mispricing, then the relaxation of financing constraints

channel predicts that it will only be constrained firms that increase investment. The opposing

theories provide clear cross-sectional hypotheses. Both the rational risk sharing story, and the

inefficient mispricing-motivated stories predict that investment of both constrained and uncon-

strained firms will respond to twin price premiums, while the relaxation of financing constraint

channel predicts that only the investment of constrained firms will respond to twin price pre-

miums. Exploiting cross-sectional heterogeneity in firm-level financing constraints, I find that

consistent with the relaxation of financing constraint channel, and inconsistent with the ineffi-

cient 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.

While there currently exists no empirical evidence that market-wide overpricing encour-

ages investment by relaxing financing constraints, there are three papers that examine whether

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firm-specific mispricing can alleviate constraints in the cross-section of firms. These three pa-

pers arrive at three very different conclusions. Examining firm-level mispricing and investment,

Baker, Stein, and Wurgler (2003) find evidence that mispricing affects investment by relaxing

financing constraints, while Campello and Graham (2007) find that this is only the case during

the internet bubble period, and Bakke and Whited (2010) arrive at a contradictory conclusion,

finding little evidence that constrained firms have investment that responds to mispricing. Given

that the results of these studies are conditional on the chosen measures of firm-level mispricing,

and financial constraint used, it is perhaps not surprising that three studies examining the same

question have arrived at conflicting conclusions.

One disadvantage of using a firm-level measure of mispricing is the likelihood of the measure

being correlated with underlying firm characteristics, making it difficult to attribute any ob-

served empirical relationship to mispricing rather than fundamentals. While my study primarily

focuses on the response of aggregate investment to market-level mispricing, I also confirm my

results at the firm level, taking advantage of the added identification that a firm-level analysis

offers. By utilizing firm-level data, in conjunction with my market-wide measure of mispricing,

I can explicitly control for measures of fundamentals at the firm level, while utilizing a mea-

sure of mispricing that is not firm-specific, alleviating the concern that the results are driven

by correlation between my measure of mispricing and fundamentals that are relevant for the

firm-specific investment decision.

The results can be summarized as follows. Using a novel measure of market-specific mis-

pricing 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 coun-

tries, and consistent with the relaxation of financing constraints, this link arises through equity

and debt issuance. However, this does not rule out alternative inefficient explanations, 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 distin-

guish 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 undertake this test

I employ a relatively new methodology, seldom utilized in the finance literature, 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.

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Next, I provide evidence of links between mispricing and the macroeconomy which are also

consistent with the relaxation of financial constraint channel, though not a definitive test as

such. The reallocation of capital to high-return projects of constrained firms will increase the

market-wide efficiency of capital allocation. Additionally, recent work (Love (2003), Brown,

Fazzari, and Petersen (2009)) finds that relaxing economy-wide financing constraints results in

increased innovation and productivity. Both of these forces predict an increase in subsequent

GDP growth. Consistent with this, I find that high relative mispricing portends an increase in

relative GDP growth that is larger than can be explained by an increase in investment alone.

Acemoglu (2001) presents a model in which financing frictions hinder the creation of jobs.

Consistent with overpricing relaxing financing frictions, I find that overpricing predicts lower

unemployment. 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 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, and is consistent with the behavior of a long-run value-maximizing man-

ager. 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 mis-

pricing used in the study. Section 3 discusses the data, research methodology, and econometric

issues. In Section 4, I provide 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

The measure of mispricing I employ does not hinge on a model of market equilibrium. In-

stead, I rely on a measure that literally represents a textbook violation of the law of one price

(Brealey, Myers, and Allen (2008), Bodie, Kane, and Marcus (2008)). I utilize dual-listed com-

pany 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 (of-

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ten 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.

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 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 com-

pany 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 re-

ceive 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

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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. 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 been able to explain the observed variation.

Rather, twin deviations from parity seem to reflect country-level investor demand that affects

local market valuations.

Froot and Dabora (1999) undertake a comprehensive study of potential rational explana-

tions for Siamese twin deviations from parity. They 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. 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 Aus-

tralian 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. The most widely proposed 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. Froot and Dabora conclude that market-wide noise shocks from irrational

traders, which infect locally traded stocks more than foreign traded stocks, can explain the

comovements.

The persistence and magnitude of the mispricing reflects limits to cross-country arbitrage,

and the lack of fungibility of twin shares, hampering arbitrageurs in restoring prices to parity.

As a result, there is no identifiable date at which prices will 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. Indeed, 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, ultimately resulting in a loss of

$286 million for LTCM on equity pairs trading between the beginning of 1998 and its eventual

bailout. Bedi, Richards, and Tennant (2003) provide additional anecdotal evidence motivating

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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.

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 500.2 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 PLCs

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).

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 = ift + ei,t + si SA + i,t (1)

where i indexes firm, t denotes time, and A indexes country. ft is a vector of systematic risk

factors, and i represents factor loadings for stock i. ei,t represents the firm-specific funda-

mental shock, reflecting firm-specific news that is diversifiable across stocks. SA is aggregate

market sentiment in country A, and si represents stock is loading on the market sentiment

factor, which is assumed to be orthogonal to the fundamental news and not diversifiable across

firms. i,t is the firm-specific sentiment shock, reflecting firm-specific changes in noise trader

sentiment regarding stock i.

2Because Royal Dutch and Unilever NV were members of the AEX index, and until 2002 also members ofthe S&P 500 index, the prices of these shares reflected both Dutch and American noise trader risk, preventingRoyal Dutch/Shell and Unilever NV/Unilever PLC from being clean measures of relative mispricing betweeenthe Netherlands and the United Kingdom. Fortunately, the shares of Elsevier/Reed International also reflectmispricing between the Netherlands and the United Kingdom. Prior to 2002, I use Elsevier/Reed Internationalas the measure of relative mispricing between the Netherlands and the United Kingdom. Post 2002, I splice theUnilever series to the existing Elsevier/Reed International series. I dont use the post-2002 Royal Dutch/Shellsample, as it announced the unification of its share structure on 10/28/2004.

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The fundamental components, ift and ei,t, will be the same for twin stocks:

ift = jft

ei,t = ej,t.

The difference in returns between twin shares is equal to

ri rj = si,j(SA SB) + (i j) (2)

I assume constant s across twin stocks.3 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 (i = j), 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.

Two other assets, ADRs and single country closed-end funds, both can trade at discounts

from the prices of their foreign traded underlying securities.4

These discounts seem like naturalchoices as additional measures of cross-country market sentiment, however this is not the case.

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,

and forces ADR discounts to reflect little more than the transaction costs of undertaking such

arbitrage.

Single country closed-end funds are 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

3Baker and Wurgler (2006) suggest that s is larger for stocks whose valuations are highly subjective, suchas small, young, high growth, unprofitable, and highly volatile stocks. All of the twin stocks are large, well-established companies that are likely to have very similar loadings on the aggregate sentiment factor.

4In order to simplify terminology I refer to deviations from underlying asset prices in terms of a discount. Apremium is simply a negative discount.

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the underlying securities is determined in the home country of the assets. The fund discount is

therefore a representation of US sentiment regarding the foreign market on which the underlying

assets are listed, rather than a measure of the foreign countrys domestic investor sentiment.

Indeed, Hwang (2011) finds that the single country closed-end fund discount is associated with

the countrys 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 regarding a foreign market will not be informative about the actions of domestic firms

in these foreign markets.

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 Dijks

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 thetheoretical price ratio. The measure of mispricing that I employ is the deviation of dual-listed

shares from parity, calculated as

PremiumA,B,t = log(PA,t

PB,t) log(Theoretical Parity Ratio),

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. 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.

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3.2 Empirical Methodology

I structure my empirical tests 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,

I examine whether investment in country A is 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, Im interested in examining

InvestmentA,t = 1MispricingA,t1 + 2XA,t1 + A + t (3)

where A indexes country, A represents country fixed effects, t represents time fixed effects,

MispricingA 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, PremiumA,B, is

a relative measure of mispricing, rather than an absolute measure of mispricing, I can not iden-

tify whether increases to PremiumA,B are attributable to an increase in overpricing in country

A, or an increase in underpricing in country B.5 The latter of which should have no effect on

investment in country A, but should affect investment in country B. Given this relative mea-

sure 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,tInvestmentB,t = 1PremiumA,B,t1 + 2(XA,t1XB,t1) + A - B. (4)

This is the baseline structure of the regression specifications used throughout the paper.

3.3 Econometric Methodology

One 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 contem-

poraneously 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

5or some combination of both

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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.

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 over-

looked 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 ap-

proaches 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.6 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.

6See Roodman (2007) for a great discussion of the dangers of utilizing system GMM without first assessingthe validity of the procedure for the data being used. It also documents the potential for instrument profilerationto vitiate tests of instrument validity, leading to amplification of the potential for type I error.

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4. Results

4.1 Investment

Table 3 analyzes the relationship between mispricing and aggregate investment. For each

country, aggregate investment is defined as gross fixed capital formation as a percent of GDP.

This variable is obtained from the World Bank (World Development Indicators). All indepen-

dent 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 begin-

ning of period relative overvaluation. The univariate results indicate that investment responds

strongly to mispricing. In terms of economic significance, a one-standard-deviation increase in

Premium predicts an increase in the difference in investment between country A and countryB 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 invest-

ment 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 general methodology of including profitability as a measure of fundamen-

tals.7 The results in column 2 show that Premium is still a significant predictor of investment

after controlling for fundamentals. In columns 3 and 4 I replace profitability with cash flow

and Tobins Q respectively, with no change in outcome.8 In column 6 I include cash flow and

Q together as controls, and consistent with the investment literature cash flow is a stronger

predictor of investment than Q. The twin premium continues to be economically and statis-

tically significant in all specifications, consistent with the existence of a mispricing channel in

investment.9 10

Because all of the independent variables in the analysis are standardized to have zero mean

7Aggregate profitability is defined as aggregate EBITDA scaled by the aggregate book value of total assets.8Aggregate Tobins 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.9The 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.10I have also verified that specifying the controls or dependent variable as log differences, rather than differences,

does not affect the results.

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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.11

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 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 fun-

damentals. The two most well-established proxies for the marginal product of capital, Tobins

Q and profitability, have been used to mitigate this concern. I have also included GDP growth

to further capture country-level growth opportunities. Additionally, I include future realized

values of profitability, Q, and cash flow to control for investment opportunities not fully cap-

tured by the use of lagged and contemporaneous proxies for investment opportunities. Abel and

Blanchard (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 that in addition to capturing true mispricing, Premium is also measur-

ing aggregate Q, that is, Premium can be rewritten as

Premium = (Q + Mispricing), (5)

where Mispricing is the true level of mispricing. In this case, the regression of interest is

Y = 1Mispricing + 2Q, (6)

11I have also tried using household consumption expenditure and gross domestic income as controls, as they aretwo variables that have been used in the past literature as measures of aggregate fundamentals. These variableshave virtually no predictive power after controlling for Q and cash flow. In alternate specifications I have alsoincluded variables to capture market-wide interest rates, index levels, and inflation. The results are unaffectedby the inclusion of any of these variables

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however, the model I am estimating is

Y = 1Premium + 2Q. (7)

This can be rewritten as

Y = 1(Mispricing + Q) + 2Q. (8)

By the Frisch-Waugh theorem, 1 will be identical in both estimations. Therefore, using

Premium to proxy for Mispricing will not bias the results.

4.2 Financing

This section explores whether the effect of mispricing on investment arises through an eq-

uity issuance channel. If overpricing relaxes financing constraints, then we should expect to 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 = 1PremiumA,B,t1 + 2(XA,t1 XB,t1) + A B (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 Premium as a financing predictor in a

univariate setting.

The results in column 1 show that Premium 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 twin 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.

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4.3 Firm-Level Analysis: Propensity Score Matching

I next explore a number of robustness issues while utilizing a different data set. I exam-

ine whether the investment and equity issuance results are robust to analysis using firm-level

investment and equity issuance data from Worldscope. The aggregate regression results show

that overpricing predicts increased investment and financing. These results are based on a rel-

atively 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.12 To the extent that this is a legitimate concern, I should include additional country-

specific interaction terms to account for this. Given the relatively small sample size, this isnt

a feasible solution.

Another concern is that the firms in the two pair countries arent directly comparable to each

other as a whole. To the extent that this is the case, it is not appropriate to compare aggregate

data from the two countries, and the aggregate results may be misleading. For these reasons,

and the inherent noise in aggregate data, I utilize a generalized propensity score matching pro-

cedure (Hirano and Imbens (2004)) to analyze firm-level data. An added benefit of examining

firm-level data is the ability to directly control for firm-specific investment opportunities, while

focusing on the effect of the market-level mispricing common to all firms.

One method to control for differences in firm characteristics is to match firms in pair coun-

tries that are similar along a number of observable dimensions. I would ideally like to match

firms on a number of dimensions, such that their expected outcome variables (investment or

equity issuance) in the next period are equal. Because matching on a large number of covari-

ates is impractical, I employ a relatively new procedure first introduced by Hirano and Imbens

(2004), that is seldom used in the finance literature. The generalized propensity score matching

approach 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 is-

suance. This 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

12For example, McLean, Zhang, and Zhao (2011) find that investment-Q sensitivity is higher for firms incountries with strong legal protection of investors.

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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 variables and outcome variable across countries. To

avoid this potential bias, the appropriate first-stage regression should be estimated separately

for each country.

Ideally, I want to identify two firms at time t, one in country A and one in country B,

that are predicted to have the same 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 pre-

dicted level of investment or equity issuance, based on observables. Because propensity score

methods have been used previously in the literature, I refrain from a detailed discussion here.13

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 Im 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 dif-

ferences 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 cross-

13See Villalonga (2004) and Drucker and Puri (2005) for early examples of propensity score use in finance, aswell as detailed discussions of the propensity score methodology.

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country 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.

I next take the sample of firms matched on predicted investment and examine how realized

investment differs as a function of mispricing. In Table 5, I regress firm-level differences in

investment on the twin price premium. Standard errors are clustered by two dimensions, firm i

and firm j, in order to correct for the correlation induced by the presence of multiple firm ob-

servations that arises 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 Premium predicts an increase in the difference in investment between firm i and firm

j of about 0.4% of total assets, which is about 5.5% of average annual firm-level capital expen-

diture in the sample. 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 doesnt 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 is-

suance results. In the first stage I match firms on their predicted level of equity issuance, where

issuance is defined as stock issuance net of repurchases as a fraction of the firms 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, I estimate a separate regres-

sion for each country, and find four matches 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. A comparison of pre and

post-match means of the predicted value of the dependent variable is shown in Panel B.

Table 6 examines the effect of market-specific sentiment on equity issuance using the propen-

sity score matched sample. The results of the matched sample resemble the aggregate results.

In terms of economic significance, a one-standard-deviation increase in Premium 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.

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4.4 Debt Issuance

Next, I turn briefly to debt issuance. The market timing literature overwhelmingly focuses

on equity market timing, and while the measure of mispricing employed is a measure of aggregate

equity mispricing, and should therefore be expected to be more closely linked to equity market

timing, there are a number of reasons why it may also predict debt market issuance. Morck,

Shleifer, and Vishny (1990) predict that mispricing-motivated financing will be manifested by

increases in equity and debt issuance, as mispricing will affect not only the attractiveness of stock

financing, but also the attractiveness of debt financing given the role of market valuations in

affecting the cost of debt capital. Spiess and Affleck-Graves (1999) and Bradshaw, Richardson,

and Sloan (2006) find that future stock returns are unusually low following debt offerings,

suggesting that firms issue debt when they are overvalued, and that overpricing in the equitymarket is correlated with overpricing in the debt market. This is hardly surprising, as stock

market capitalization is an input to most credit risk models. Alternatively, as in Baker, Stein,

and Wurgler (2003), equity issuance resulting from overpricing may relax a binding leverage

constraint, making debt issuance possible.

The debt issuance regression uses the same controls as the equity issuance specification

above. Debt issuance is obtained from the SDC Global New Issues Database, and is equal

to the aggregate value of all domestic debt issuance as a fraction of GDP (where GDP is

measured in millions). The results in Table 7 indicate that debt issuance is relatively higher

in the relatively overvalued country. The introduction of various controls does not alter the

conclusion that relative overpricing predicts relatively higher debt financing. The existence

of a positive link between mispricing and equity or debt issuance is consistent with mispricing

motivating efficient investment, however doesnt completely rule out the possibility of inefficient

investment, as it could also be consistent with a story in which managerial and investor sentiment

is correlated, leading managers to invest and raise financing at the same time that investors

are overly optimistic. It also does not rule out the case of an empire-building manager that

uses investor overoptimism as a cover for investment, again leading managers to invest and

raise financing at the same time that investors are overly optimistic, and resulting in inefficient,

value-destroying investment. In the next section, I exploit heterogeneity in firm-level financing

constraints to determine whether the observed mispricing-motivated investment is consistent

with efficient or inefficient investment.

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5. Is mispricing-driven investment value maximizing?

5.1 Financial Constraint Heterogeneity

I test the relaxation of financing constraints channel by once again turning to the propensity

score matching technology. This test exploits heterogeneity in firm-level financing constraints

to 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 be of the same level

of financial constraint. To do so, I sort firms within countries 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. Furthermore, Morck,

Shleifer, and Vishny (1990) argue that the influence of the stock market should be greater for

smaller firms, since they rely on external financing to a greater extent than other 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 paper rating, and bond rating. As I dont have access to the latter two

variables in all countries of my analysis, Im 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 an index of financial constraints via an

investment Euler equation that classifies small, low cash flow, low dividend, high leverage firms

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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 Premium for the different financial constraint terciles. The table reports the coefficient on

Premium 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 matched 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 Premium 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 Premium 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 group of firms has the largest coefficient on Premium

of all terciles, while the coefficient on Premium is not significant at the 5% level in any of

the specifications for the least constrained group of firms. The coefficients on the Premium

variable from the full control specifications are displayed graphically in Figure 3 and Figure 4,

along with the 95% confidence intervals for the coefficient 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 that should affect all firms. Additionally, because the

identification of mispricing comes from stock price movements of extremely large firms (dual-

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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.14

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 how

investment and equity issuance respond to mispricing for constrained firms, conditional on

the external finance dependence of the industries in which they operate. Constrained firms

are further sorted conditional on 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 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

firms typically dependent on external finance to fund growth exhibit the greatest sensitivity to

mispricing.

5.3 Macroeconomic Links: GDP Growth and Unemployment

I next explore other potential links between market-wide mispricing and the macroeconomy.

If mispricing relaxes financing constraints, and capital is reallocated to high-return projects of

constrained firms, then this should be reflected by an increase in subsequent GDP. Addition-

ally, Love (2003), and Brown, Fazzari, and Petersen (2009), find that the relaxation of financing

constraints disproportionately affects constrained firms, leading to increased productivity and

innovation, and providing a second channel through which GDP growth will be affected.

I focus on subsequent 3 year real per capita GDP growth. This window seems appropriate

given that fixed capital investment does not become productive for at least 1-3 years (see Mayer

(1960) or Del Boca, Galeotti, Himmelberg, and Rota (2008) for evidence on gestation lags).

Following the growth literature, I use control variables from Barro (1996) and Mankiw, Romer,

14Almost all of the twin firms were at one point among the largest 100 firms in the world, measured either byassets or revenue.

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and Weil (1992). These variables include the population growth rate, initial GDP per capita

as a measure of initial physical capital to control for the convergence of growth, secondary

school enrollment as a measure of human capital stock, the sum of imports and exports as a

share of GDP as a control for government openness, and inflation and the ratio of government

consumption to GDP as measures of macroeconomic stability. The sum of imports and exports

as a share of GDP, the ratio of government consumption to GDP, the population growth rate,

and inflation are all measured contemporaneously with the 3 year GDP growth variable. I

instrument these endogenous variables with their third and fourth lags.15

As discussed above, system GMM has increasingly become the methodology of choice in the

growth literature. However, this procedure is not appropriate for my data sample.16 I instead

utilize an OLS instrumental variable methodology as in Barro (2001).

The results for the growth regressions are in Table 10. Premium is both statistically and

economically significant. A one-standard-deviation increase in relative overvaluation predicts

an increase in the difference in subsequent 3 year GDP growth between country A and B of

over 85 basis points in all specifications. The elasticity of output with respect to capital is

generally thought to be around 0.33; however, the magnitude of the growth increase relative

to the increase in aggregate investment seen earlier suggests an elasticity of output of about

0.5. The magnitude of the effect of mispricing on GDP growth is larger than that which can

be explained by the increase in investment alone, and is consistent with Schumpeterian-growth

models in which investment by constrained entrepreneur-type firms increases the rate of inno-

vation and productivity.

Due to its ability to relax financing constraints, a link may also exist between mispricing and

unemployment. Acemoglu (2001) presents a model in which credit market frictions hinder the

ability of entrepreneurs to exploit technological changes through the creation of new businesses

and the creation of jobs. It follows that a relaxation of financing constraints will remove this

impediment to new job creation. Table 11 examines the relationship between unemployment

and mispricing. Consistent with overpricing alleviating the effects of financing frictions, relative

overvaluation predicts lower relative unemployment.

The results thus far indicate that mispricing relaxes financing constraints, allowing for in-

creased investment. Another test of the hypothesis that overpricing relaxes financing constraints

is to examine the decision of firms to go public. New firms are more dependent on external

15The unreported overidentification test does not reject the null that the instruments are valid.16See Roodman (2007) for examples of incorrect applications of system GMM, specifically in the context of

growth regressions.

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financing than established firms. To the extent that overpricing relaxes financing constraints,

this should be reflected in an increased number of firms going public. In unreported results,

I also find that the level of relative overpricing is strongly positively related to the number of

firms opting to go public. While these macroeconomic links are consistent with overpricing re-

laxing financing constraints, the results are not necessarily inconsistent with alternative stories

one could tell, and as a result are not meant to be taken as evidence to distinguish between

the efficient and inefficient stories. Rather, the results reflect additional, heretofore unexplored,

links between mispricing and the macroeconomy.

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 variable of any contaminants that dont reflect broad-market sentiment. Specif-

ically, 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 deviated 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 ex-

penses 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.

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. How-

ever, anti-takeover provisions make it very difficult to accumulate large blocks of control for the

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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 twin premiums, this story would require substantial time variation in the value

of control. As I have no time varying measure of the value of corporate control, Im unable to

account for this potential explanation.

Finally, Froot and Dabora explore whether differences in taxation across countries can ex-

plain the observed premiums, and conclude that tax explanations can not explain the mispricing.

They find that 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

ri rj = si,j(SA SB) + (i j).

If i = j , 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

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 Ped-

ersen (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 =1

D

D

t=1(

|rt|

V olumet)

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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. These variables are obtained 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 twins industry,

and I obtain industry index level data from Datastream. I use the residual from a regression of

industry index level on market index level 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

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 idiosyncraticindustry 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 12. The firm-specific results, which are omitted for brevity, are consistent with,

and slightly stronger than, the results with the raw measure.

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7. 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

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. The results provide support for the efficient

mispricing-motivated investment channel. Lastly, I find that times of overpricing are followed

by increased GDP growth, lower unemployment, and an increased number of firms going public,

providing further evidence of links between market-wide mispricing and the macroeconomy. Thefindings suggest that stock market inefficiencies can spill over to the real economy by relaxing

financing constraints.

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AppendixA:

DescriptionofVariables

Variable

Description

So

urce

Q

[Mark

etvalueofcommonequity+totalassets-bookvalue

ofcom

monequity]/totalassets

W

orldscope(firm-level,value-weightedavg)

CF

Fundsfromoperationsscaledbylaggedassets

W


ROA

NetIncomescaledbylaggedassets

W


Tangibility

PPEscaledbylaggedassets

W


Cash

Cash

andshort-terminvestmentssca

ledbylaggedassets

W


Debt/Assets

Long-

termdebtasafractionofassets

W


Assets($)

Total

assetsmeasuredinU.S.dollars

W


Investment

Gross

fixedcapitalformationscaledbylaggedGDP

W

orldBank(WorldDevelopmentIndicators)

Equityissuance

Total

amountofequityissuedbyd

omesticcompaniesin

countryasafractionofGDP(GDP

measuredinmillions)

SD

C

BondIssuance

Total

amountofdebtissuedbydomesticcompaniesincoun-

tryas

afractionofGDP(GDPmeasuredinmillions)

SD

C

Unemployment

Share

ofthelaborforcewithoutwork,butseekingemploy-

ment

W


GDPGrowthPer

Capita

AnnualrealpercentagegrowthrateofGDPpercapita

W


Inflation

Inflationasmeasuredbytheconsum

erpriceindex

W


TaxRate

Corporatetaxrate

KPMGCorporateandIndirectTa

xRateSurvey

SecondarySchool

Enrollment

Ratio

oftotalenrollmenttothepo

pulationofsecondary

schoolage

W


Imports

ImportsofgoodsandservicesasapercentofGDP

W


Exports

ExportsofgoodsandservicesasapercentofGDP

W


GovernmentCon-

sumption

GovernmentconsumptionexpenditureasapercentofGDP

W


Population

GrowthRate

Growthrateofpopulation

W


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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 scaled-

price variable such as the dividend-price ratio, and although my regressions dont take this

specific form, the problem is most easily understandable in this framework. For example, in the

context of returns

Rt = + xt1 + ut, ut i.i.d.N(0, 2u) (10)

xt = + xt1 + t t i.i.d.N(0, 2) (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 t and ut.

Stambaugh (1999) shows that the bias in the OLS estimate of is

E[ ] =u,

2E[ ] (12)

where and are the OLS estimates of and . The bias is increasing in the absolute value of

the correlation in innovations, and is proportional to the bias in the OLS estimate of in (11).

It is also linked to the persistence of the predictive variable, as Kendall (1954) proves that

E[ ] =(1 + 3)

n+ O(n2) (13)

where n is the sample size. As a result, when the persistence of the predictive variable is large,

the bias in the OLS estimate of 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 coefficient. Although the regres-

sions here dont take this form, and its unclear the size or direction of the bias, the predictive

variables are nontheless persistent, although less so than variables in the equity premium fore-

casting literature. To generate coefficient estimates and p-values that correct for this spurious

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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 esti-

mate, I generate a series of pseudo-independent variables and dependent variables by drawing

with replacement from the empirical distribution of the errors, u and . I select a random X0,

and substitute the draws of u and 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

. The bias-adjusted estimate is equal to

( ). (14)

To generate p-values under the null of no predictability I undertake a second set of sim-

ulations. I run separate simulations for each predictor, similar to the ones above, but now

imposing the null that i = 0. This results in a second set of coefficients that form an empirical

distribution that is used to calculate p-values.

B.2 Dynamic Endogeneity

Applying fixed-effects estimation when sequential exogeneity is satisfied, but strict exogene-

ity is violated due to the presence of dynamic endogeneity leads to the following bias. As in

Wooldgridge (2002),

plim(FE) = +

1T

Tt=1

E(x

itxit)1

1T

Tt=1

E(x

itit)

where xit = xit xi.

The direction of the bias of FE depends on E(x

itit). Under the assumption of sequential

exogeneity,

E(x

itit) = E(xit xi)it = E(xiit).30


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Therefore,

1

T

Tt=1

E(x

itit) = 1

T

Tt=1

E(xiit) = E(xii).

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 (pos-

itively) biased.

In unreported analyses, I have examined the extent to which Premium is related to past

values of the dependent variables used in the analysis, in order to guage whether dynamic en-

dogeneity is likely to be a concern. Univariate regressions reveal that investment and bond

issuance are positively related to future values of Premium, while unemployment is negatively

related to future values of Premium. All of these relationships would lead to a bias that goes

in the opposite direction of the observed empirical results.

Equity issuance and 3 year GDP growth are negatively related to future Premium, leading

to fear that dynamic endogeneity could potentially bias the coefficient on Premium in these

regressions in the observed direction. However, in both cases, the univariate relationship is

extremely weak. In both specifications, Premium is insignificantly related to past values of the

dependent variable (t-stats of -0.27 and -1.03 in the equity issuance and GDP growth regres-

sions, respectively), 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 Premium. The magnitude of the relationship

for GDP growth is not much larger; a one-standard-deviation move in GDP growth is related to

a movement in future Premium equal to about 22% of one standard deviation. The relation-

ships are not nearly of the magnitude necessary to cause concern of bias arising from dynamic

endogeneity.

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AppendixC:

TableC1

Inv

estment1stStage

Thistabledisplayscoefficientsforfirm-levelinvestmentregressionsrunsepara

telyforeachcountryfortheperiod1989-2007.Eachcolumndisplayscoefficie

ntsfrom

aseparate

regression.Thedependentvariableiscapitalexpendituresscaledbylaggedassets.

Allindependentvariablesarebeginningofperiodvalues.Standarderrorsclusteredbyfirm

and

timeareinparentheses.***,

**,a

Ineffcient Markets, Effcient Investment

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