(mis)valuation and investment

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(Mis)valuation and Investment

Vojislav Maksimovic, Gordon Phillips** and Liu Yang***

COMMENTS WELCOME

This Version: November 17, 2010

We investigate the relation between market (mis)valuation and the subsequent pro-

ductivity of rms and industries. We also examine whether mergers and capital expen-ditures that take place in periods of high (mis)valuation predict higher or lower future

productivity of investing rms or acquired assets. We show that high Tobins q and also

high estimated misvaluation (using measures developed in the literature) are associated

with higher productivity in the future. Examining acquisitions across industries, we nd

that the productivity of targets plants increases after the acquisition. The increase is not

smaller when the acquirer has higher valuation or is from a relatively more overvalued

industry. Thus, while industry (mis)valuation predicts acquisitions, there is no evidence

that high valuation or high estimated misvaluation lead to inecient diversication or

a misallocation of investment.

University of Maryland, **University of Maryland and NBER, and ***UCLA. Maksimovic can be reachedat [email protected]. Phillips can be reached at [email protected]. Liu Yang can be reached [email protected]. This research was supported by the NSF. The research in this paper was conductedwhile the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Center for EconomicStudies. Research results and conclusions expressed are those of the authors and do not necessarily reect the viewsof the Census Bureau. This paper has been screened to ensure that no condential data are revealed.


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(Mis)valuation and Investment

ABSTRACT

We investigate the relation between market (mis)valuation and the subsequent pro-

ductivity of rms and industries. We also examine whether mergers and capital expen-

ditures that take place in periods of high (mis)valuation predict higher or lower future

productivity of investing rms or acquired assets. We show that high Tobins q and also

high estimated misvaluation (using measures developed in the literature) are associated

with higher productivity in the future. Examining acquisitions across industries, we nd

that the productivity of targets plants increases after the acquisition. The increase is not

smaller when the acquirer has higher valuation or is from a relatively more overvalued

industry. Thus, while industry (mis)valuation predicts acquisitions, there is no evidencethat high valuation or high estimated misvaluation lead to inecient diversication or

a misallocation of investment..


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1 Introduction

With the recent debates about "irrational exuberance" and pricing bubbles in nancial markets, therole of the stock market in inuencing corporate investment decisions has assumed important policy

implications. If markets are subject to mispricing, and if market signals drive corporate investment

and mergers, then there exists the potential for nancial markets to systematically divert scarce

resources to unproductive uses. Several studies have investigated the link between market valuations

and corporate decisions. These papers have identied two areas as being of greatest interest: the

eect of market valuation on investment and merger decisions. While we have a very well developed

theory of the link between valuation and investment in ecient capital markets, there is controversy

given potential misvaluation of rms in nancial markets about whether rms do, or even should,

take market signals into account in determining the amount of their capital investments.

Several researchers have argued that many acquisitions occur because bidding rms attempt to

take advantage of temporary valuation anomalies. These studies strongly suggest that such mergers

may result in misallocated resources. Shleifer and Vishny (2003) propose a story based on irrational

stock market. They argue that if market believes that acquirer can transfer its productivity to target,

overvalued rms can buy assets from other rms and get the combined rm valued upwards even if

there is no synergy in transaction. Using a rational model, Rhodes-Kropf and Vishwanathan (2004)

show that if errors in valuing potential takeover synergies is correlated with the overall valuation

error, then mergers are more likely to occur during valuation waves when synergies tend to be over

estimated.

In this paper we rst examine the relation between market valuation and the subsequent pro-

ductivity of rms and industries. We then investigate how valuations aect real decisions such as

capital expenditure and acquisitions. We use the existence of bond rating as a proxy for nancial

constraints and test whether nancially constrained rms exhibit higher valuation investment sen-

sitivity. We also investigate whether mergers and capital expenditures that take place in periods of

high valuation predict higher or lower future productivity of investing rms or acquired assets. We

analyze these questions using plant-level data from US Bureau of Census between 1972 and 2004.

This dataset allows us to track productivities of individual plants, facilitating direct benchmarking

of plant productivities within each manufacturing industry on an annual basis. It also tracks own-

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ership changes from year to year, allowing us to test whether an acquirers valuation predicts the

productivity growth of acquired plants.

We use Tobins q as our valuation measure and employ two additional measures that capture the

deviation of valuation from the predicted level recently introduced by Pastor and Veronesi (2003)

and Rhodes-Kropf, Robinson and Vishwanathan (2005). In both papers, regressions are used toestimate the predicted a rms value based on observable characteristics, and the dierence between

the actual value and the predicted level is referred to as "misvaluation." Since this measure captures

the portion in Tobins q that cannot be explained by current characteristics, and as we show later

can be driven by rational expectation rather than mispricing, in the rest of this paper we will refer to

it as unexplained valuation. Similar to Rhodes-Kropf, Robinson and Vishwanathan (2005), we also

decompose the total level of Tobins q and unexplained valuation into a time-series cross-sectional

component and a rm-specic component to separate industry valuation eect from rm valuation

eect. Given the potential complexity of determining the structural relations between valuation,

productivity and investment in the presence of potential adjustment costs and costs of nancing,

rather than specifying a structural model, our approach is to follow the literature and use reduced

form specications to explore rst order relations between these variables.

We nd that for US manufacturing rms, valuation measured by Tobins q predicts future

productivity measured by Total Factor Productivity (TFP), both at the rm and industry level.

This result is consistent with macro-evidence, obtained using dierent measures of productivity,

that stock market valuation predicts increases in productivity in Beaudry and Portier (2006). This

eect is stronger in industries with more public rms, bigger rms, and higher concentration ratios.

Since higher investment drives down the marginal product of capital, as a check, we test whether

the relation between Tobins q and TFP is stronger in industries with lower capital expenditure and

nd that, as expected, that it is.. These results sets up a benchmark, showing that market signals

convey positive information about future productivity and that the relations between them accord

with predictions.

Next, we use measures of unexplained valuation instead of Tobins q to predict productivity

and nd similar results. Firms with higher unexplained valuations have higher productivity going

forward. The result holds both at the industry level and at the rm level. They also hold when

we Tobins q and unexplained variation are used together to predict future productivity. These

ndings suggest that stock prices do convey relevant information about future productivity, and

that unexplained valuation, which has been interpreted as misvaluation in several previous studies,

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does not have dierent eects than productivity.

Both Tobins q and measures of misvaluation similarly predict capital expenditure. Industries

with higher Tobins q and unexplained valuation have higher CAPEX and rms that are relatively

more valued than industry peers invest more. When we use the existence of bond rating as a proxy

for nancial constraints, we nd that nancially constrained rms exhibit higher valuation capitalexpenditure sensitivity than constrained rms. This is consistent with ndings in Campello and

Graham (2007).

We next examine decisions to buy or sell assets related to valuation. Firms with higher rm-

specic valuation are more likely to buy assets, and they buy assets in their existing industries

when industry Tobins q or unexplained valuation is high, and diversify into other industries when

current industry Tobins q or unexplained valuation is low. On the other hand, sellers tend be rms

with lower rm-specic Tobins q or misvaluation, but in industries with high valuation. We alsond that buyers have higher productivity while sellers have lower productivity.

More insight on how valuation drives acquisition decisions is provided by examining the outcomes

of acquisitions. The productivity of targets plants increases after the acquisition. The improvement

in productivity is not smaller when acquirers have higher unexplained valuation. On the other hand,

the productivity gain is bigger when acquirers are more productive in their home industry and when

the dierence in industry Tobins qs between the acquirers and targets industry is high. We nd

little evidence that dierences in the acquirers and targets industry valuation predict productivitygains of the acquired plants. Thus, while industry valuation predicts acquisitions, there is no

evidence that it leads to inecient acquisitions. Examining a sample of diversifying acquisitions, we

nd that Tobins q and valuation decline in acquirers home industries after acquisitions, especially in

cases when acquirers home industry has higher valuation around the transaction. This is consistent

with our earlier ndings that rms use acquisitions to pursue better opportunities.

Our paper contributes to the literature on the relation between rational and irrational market

valuation and rms investment decisions. On one hand, managers may be better informed aboutthe investment opportunities of their own rms than are outside investors, and therefore can ignore

stock market movement as it provides no new knowledge. On the other hand, as pointed out in Dow

and Gordon (1997) and Subrahmanyam and Titman (1999), managers may rely on stock market as

a source of information and improve their investment decisions. In addition, managers can also take

advantage of market mispricing of their stock by issuing equity and investing proceeds (Fischer and

Merton (1984)). The empirical evidence of how valuation aects rms investment decisions is at

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best mixed. Morck, Shleifer and Vishny(1990) nd that, although returns can predict investment,

the predictive power disappears once they control for fundamentals. Similarly, Blanchard, Rhee and

Summers(1993) nd that the stock market does not aect investment, conditional on fundamentals,

even though it changes the composition of external nance. Meanwhile, Baker, Stein and Wurgler

(2003) show a high sensitivity of investment to Tobins q for nancially constrained rms, concluding

that stock market mispricing leads these rms to issue equity and invest proceeds in marginal

projects. Using data around the 1990s tech bubble, Campello and Graham (2007) show that

constrained non-tech rms issued equity in response to apparent mispricing to invest, but no such

pattern is observed for unconstrained non-tech or tech rms. In addition, Gilchrist, Himmelberg,

and Huberman(2005) and Polk and Sapienza (2009) both nd that measures of mispricing matter

in investment-q regressions. In contrast, recent paper by Bakke and Whited (2010) nd that rms

with high levels of mispricing and large rms consider mispricing irrelevant for investment using

errors-in-variables estimators. Like these authors, we also nd a positive relation between CAPEX

and Tobins q. Unlike them, we also explore and nd a positive relation between Tobins q and

productivity.

Our work is also related to the recent work in nance on the eect of market misvaluation on

rms investment decisions. The existence of sentiment driven mispricing is investigated by Delong,

Shleifer, Summers, and Waldmann (1990), Shleifer and Summers (1990), and Baker and Wurgler

(2006). Stein (1996) and Baker, Stein and Wurgler (2003) discuss models of sentiment driven

investment. We show that, using measures suggested by Pastor and Veronesi (2003) and Rhodes-

Kropf, Robinson, and Vishwanathan (2005), misvaluation does indeed predict both CAPEX and

acquisitions, but importantly we also show that what is believed as misvaluation, like high Tobins

q, is associated with increases in productivity.

Recent work, in particular by Shleifer and Vishny (2003) and Rhodes-Kropf, Robinson, and

Vishwanathan (2005) has argued that merger activity is to a signicant extent driven by misval-

uation. One implication of this work is that a signicant fraction of mergers might result in the

misallocation of assets to acquirers who may not be able to operate them eciently. Like these

authors, we nd that conventional measures of misvaluation do predict merger activity. Somewhat

surprisingly, we do not nd misvaluation associated with adverse post-merger productivity changes.

The positive association between valuation and future productivity documented in our paper

suggests that deviation in valuation from past level may be driven by rational expectation on

future changes in industry or rm conditions, rather than reecting changes in investor sentiment

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unmoored from rm and industry fundamentals. Due to market imperfections such as nancial

constraints or time to build, rms may not be able to expand their capacity immediately to respond

a positive shock. Expecting future improvement, valuation increases beyond a level that is predicted

by current rm characteristics, resulting a seemingly unjustiable misvaluation. In the meantime,

interpreting high unexplained valuation as a positive signal, its creditors and suppliers may exhibit

greater willingness to extend nancing to the rm which ultimately lead to ecient investment

and higher productivity. Ovtchinnikov and McConnell (2009) present numerical examples and

empirical tests supporting the view that in an imperfect but rational market high valuations relax

constraints, leading to increased investment that mimics the pattern that would be predicted by

irrational overvaluation. In addition, high unexplained valuation at the rm level can also convey

a positive signal of the rms prospects to competitors and consumers which may help the rm to

gain advantage on the product market. Alternatively, high unexplained valuation may just reect

unusually good prospects for the rm and industry, and thereby have no causal signicance. In all

three cases, measures of high unexplained valuation, like Tobins q, can be associated with increased

productivity.

The rest of the paper is organized as follows. Section 2 describes our empirical strategy and

measures of misvaluation. Section 3 discusses the data and the construction of our productivity

measure. Section 4, 5 and 6 presents our results on the relation between valuation and productivity,

capital expenditure and acquisition decisions, respectively. Section 7 concludes.

2 Methodology

We draw on the existing literature on valuation and investment to motivate our empirical tests.

Historically the literature on Tobins q and investment has examined regressions like the following:

Investmenti;t+1 = A + 1qit + 2Xit + "it (1)

with additional terms added to this model to examine whether other factors such as protability,

cash holdings, or nancial constraints impact investment. Recently, the literature has moved to

include valuation relative to predicted valuation rather than Tobins q itself. For example, both

Rhodes-Kropf, Robinson, and Vishwanathan (2005) and Dong, Hirshleifer, Richardson, and Teoh

(2006) analyze how deviation from predicted valuation (or "misvaluation") aect mergers. Hoberg

and Phillips (2010) use deviation from predicted valuation to measure industry booms and examine

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how real and nancial factors interact in business cycles. These articles construct estimates of

predicted valuation using dierent models and then compare the actual valuations to predicted

valuations to dene relative valuation (or misvaluation).

In this paper, we use Tobins q and deviations from the predicted valuation as our variables of

interest on the right hand side. We begin with a model based on Rhodes-Kropf, Robinson, andVishwanathan (2005) (model (3)). For each industry, we regress rm is log market value of equity

on its log book value of equity, net income, an indicator for negative net income and leverage ratio:

log(Mi;t) = 0 + 1 log(Bit) + 2 log(N Iit)+ + 3I


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log(MB

) for year t as follows:

U nexplained V aluationi;t = log(M

B)i;t Predicted( log(

M

B)i;t) (5)

We refer to this measures of unexplained valuation as U VPV.

Although both Tobins q and UVs are aected by stock market movement, there is a subtle

dierence. For example, a rm with a high protability should be more valuable and hence have

a high Tobins q. If the relationship between protability and valuation remains constant, then all

the valuation can be explained by current protability, and UV will be equal to zero. In contrast, a

rm with low protability now but high growth potential may have a Tobins q that cannot be fully

explained by current protability, and therefore a positive UV. Because UVs are estimated using

historical information, it captures not only the current level but also how Tobins q has changed or

will change over time.

For all three measures (Tobins q, U VRKRV, and U VPV), we break the total valuation into an

industry component (computed as the weighted average using market capitalization of all rms in

that industry) and a component that is rm specic. The industry component captures changes

in industry overall valuation and the rm-specic component measures whether a rm is relatively

over- or under-valued compared to the predicted industry average.

3 Data

We use data from the Longitudinal Research Database (LRD) maintained by the Center for Eco-

nomic Studies (CES) at the Bureau of the Census to estimate total factor productivity and to

identify and track mergers and asset sales. The LRD tracks approximately 50,000 manufacturing

plants every year from the Annual Survey of Manufactures (ASM). It contains detailed plant-level

data on the value of shipments produced by each plant, investments broken down by equipment and

buildings, and the number of employees.1

The ASM covers all plants with more than 250 employees.Smaller plants are randomly selected every fth year to complete a rotating ve-year panel. Even

though it is called the Annual Survey of Manufactures, reporting is mandatory for large plants and

is mandatory for smaller plants once they are selected to participate. All data are reported to the

government by law and nes are levied for misreporting.

1 For a more detailed description of the Longitudinal Research Database (LRD) see McGuckin and Pascoe (1988)and also Maksimovic and Phillips (2002).

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The data we use covers the period from 1972 to 2004. To be included in our sample, rms

must have manufacturing operations in SIC codes 2000-3999. We require each plant to have a

minimum of three years of data. For each rm, we also exclude all its plants in an industry (at the

three-digit SIC code) if its total value of shipments in that industry is less than $1 million in real

1982 dollars. Since we construct measures of productivity (described later) using up to 5 years of

lagged data, our regressions cover the period between 1976 and 2004. Since we compute the rate of

capital expenditure by dividing capital expenditure on lagged capital stock and calculate change of

sales using lagged sales, we lose the initial year a rm or a rm-segment enters the database and

observations that are non-continuous. Our nal sample has about 520,000 rm-industry years and

more than 1 million plant years.

3.1 Productivity Measures

We estimate total factor productivity (TFP) using a translog production function. This functional

form is a second-degree approximation to any arbitrary production function, and therefore takes

into account interactions between inputs. Specically, for each industry, we estimate the following

model using an unbalanced panel with plant-level xed eects:

ln Qit = A + fi +NX

j=1

cj ln Ljit +NX

j=1

NX

k=j

cjk ln Ljit ln Lkit; + "it (6)

where Qit represents output for plant i in year t and Lijt is the quantity of input j used in production

for plant i for time period t. A is the technology shift parameter, assumed to be constant by industry,

fi is a plant-rm specic xed eect,2 and "it is the residual term. The input factors included are

capital, labor, and material and energy costs.

We obtain our measure of plant-level TFP from adding two components from the equation (6)

above: a plant-rm xed eect, fi, and a plant residual term "it. The xed eect captures persistent

productivity eects, such as those arising from managerial quality (Griliches (1957) and Mundlak

(1978)), and a rms ability to price higher than the industry average. The residual term measures

the deviation of the actual output from the predicted output. The industry average TFP of a

given year is computed as the average of all plant-level TFPs in that year. We then decompose the

plant-level TFP into an industry-year component and a plant-specic component. The industry

component captures changes in productivity in the industry while the plant-specic component

2 If a plant changes owners, a new xed eect is estimated.

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measures whether a plant is relatively more or less productive compared to the average plant in the

industry.

3.2 Data Summary

The aggregate TFP has been trending up in the past thirty years, especially during the 1990s.

Figure 1 presents the time series plot of TFP, Tobins q and unexplained valuation. The correlation

between our TFP series and the TFP series obtained from NBER-CES Manufacturing Productivity

Database is about 90% on the annual level.3 Tobins q is positively related to U VRKRV and UVPV,

and the two UV measures have a correlation of71%. During our sample period, there are two peaks

for UVs, one in the 80s (82-97), and the other one in the 90s (92-97).

[Insert Figure 1 Here]

4 Valuation and Productivity - The General Pattern

To examine how valuation in general aects productivity, we rst construct an industry year panel

(based on three-digit SIC). For each industry in a given year, we calculate the average TFP, Tobins

q, U VRKRV and UVPV. Then, we estimate an industry xed eect model using the following

specication:

T F Pi;t+1 = ai + b V aluationi;t + c T F Pi;t

If stock market captures changes in agents expectation about future economic conditions, then

a higher valuation will predict higher productivity going forward. We also include the lagged

productivity on the RHS because productivity has been shown to exhibit strong persistence over

time.

Table 1 Panel A shows the estimated coecients. Both Tobins q and UVs have positive signs

and are statistically signicant at 1% level. It is well documented that when lags of the dependent

variable are included as covariates, they may correlate with the unobserved panel-level eects and

therefore make standard estimators inconsistent. As a remedy, we use the GMM estimator derived

in Arellano and Bond (1991) as an alternative estimation and results are reported in Table 1 Panel

B. Adjusting for potential correlation between lagged dependent variable and panel-level eects

3 TFPs in the NBER-CES Manufacturing Productivity Database are computed using a dierent method based onchanges in output and input shares (http://www.nber.org/nberces/t0205.pdf).

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makes the eect of valuation on productivity even stronger. The estimated coecient on Tobins

q or UV increases by more than 40%. One standard deviation increase in Tobins q leads to a

7:5% increase in TFP in the next period, and one standard deviation increase in U VRKRV or UVPV

leads to an increase of TFP of 6:5% and 8:3%, respectively. When both Tobins q and UV are

included, the coecient on UV remains largely unchanged, but the coecient on Tobins q shrinks

signicantly, suggesting that changes in productivity is mainly predicted by the unexplained portion

of valuation.

In unreported regressions, we also use the TFP series obtained from the NBER-CES Manu-

facturing Productivity Database to run the same specication, but for a longer time series (from

1960-2005) and results are qualitatively the same. Our nding that valuation predicts future pro-

ductivity is consistent with the view that future changes in productivity are preceded by stock

market (Beaudry and Portier (2006)).

[INSERT TABLE 1 HERE]

If stock market movement signals changes in opportunities for the industry, does valuation

predict productivity for both public and private rms? To answer this question, we break industries

into two equally-sized categories based on the average percentage of public rms over our sample

period, and estimate the same specication using Arellano and Bond GMM estimator for both

categories. Table 2A reveals that most of the eect between valuation and productivity is driven

by high-public industries which also show stronger persistence in TFP. This is consistent with

Maksimovic, Phillips and Yang (2010) who suggest that public status is a reection of rm quality

and better rms choose to become public and later are more responsive to changes in investment

opportunities.


Table 2 Panel B to D presents estimated coecients based on Arellano and Bond GMM estima-

tors using additional sample splits. Panel B separates all industries into two equally-sized categoriesbased on industry concentration ratio. We dene an industry to be concentrated (competitive) if

the average Herndhal index based on sales is greater(less) then sample median. Tobins q has a

bigger eect predicting productivity in competitive industries while both UV measures point to the

dierent direction. Since rms in concentrated industries have more bargaining power with suppli-

ers and enjoy more entry barrier, given the same industry-wide opportunity, they may be able to

better capture rents as compared to rms in competitive industries. We also nd that productivity

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is more persistent in concentrated industries. This is consistent with ndings in Hoberg and Phillips

(2010) who show that cash ows are also more persistent in concentrated industries.

In Panel C, we compare the eect for small- and large-rm industries. We dene a rm to be

small if it has less than 50 employees4 and break industries into equally-sized categories based on

average percentage of small rms over our sample. Valuation has a stronger predicting eect forfuture productivity in large-rm industries.

Since higher investment drives down the marginal product of capital, as a check, we test whether

the relation between Tobins q and TFP is stronger in industries with lower capital expenditure.

We separate all industries based on the rate of capital expenditure over lagged assets into two

equally-size category, and nd that coecients on Tobins q and UVs are much bigger for low-capex

industries than for high-capex industries.

So far, we show that high valuation predicts high productivity at the industry level. Next, we

investigate the same relationship at the rm level. That is, when a rm is relatively overvalued

compared to its industry peers, does it predict relatively higher productivity in the future? For this

purpose, we estimate a rm xed-eect model as follows:

T F Pj;i;t+1 T F Pi;t+1

= ai + b

V aluationj;i;t V aluationi;t

+ c

T F Pj;i;t T F Pi;t

where we take the industry factor away in both productivity and valuation and regress rm-specic

productivity of rm j in industry i at t + 1 on lagged rm-specic valuation and rm-specic

productivity. As in the industry level analysis, we use Arellano and Bond GMM estimator since the

unobserved rm xed eect may be correlated with lagged dependent variable. We exclude rms

with less than 10 years of data to have a reasonable panel for dierencing.5 Due to data availability,

for this test, only public rms are included in the regression. Table 3 reports our ndings.


Tobins q consistently predicts future productivity, individually or jointly with UV measures.

High U VRKRV also predicts high productivity in the future, but becomes marginally signicant

when we also include Tobins q. Meanwhile, we dont nd evidence that U VPV predicts future

productivity. One reason for the dierence observed between U VRKRV and U VPV can be due to the

dierence in the rst-stage estimation. To estimate U VPV, changes of valuation due to changes in

4 This is similar to the denition used by Small Business Association (http://www.sba.gov).

5 This is not crucial for our results and we obtain qualitatively the same results without exclusion.

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risk has been ltered out by including the volatility of protability on the RHS (VOLP in (4)) while

the estimation ofU VRKRV does not include similar measures. When valuation goes up corresponding

to a drop in volatility, the increase in valuation will show up in U VRKRV, but not in U VPV. As

volatility decreases, rms are more likely to make investment. If those investments lead to more

ecient capital(new equipment or structure), then productivity will rise in the future.

Firms may not be aected uniformly given changes in the industry. In Table 4, we regress the

rm-specic TFP on industry valuation, together with the interaction of industry valuation and

rm characteristics:

T F Pj;i;t+1 T F Pi;t+1

= ai + ft + b V aluationi;t + c Xj;i;t V aluationi;t

where Xj;i;t includes rm characteristics such as size and productivity, ai is the industry xed

eect and ft captures year xed eect. If large and more productive rms are better positioned to

take advantages when opportunities rise in the industry, then we should observe higher growth in

productivity for those rms when industry valuation is high (i.e. c > 0). On the other hand, if

growth opportunities give small and less productive rms bigger room to expand, then we should

observe growth in productivity negatively correlated with size and productivity when industry

valuation is high (i.e., c < 0). We nd that for Tobins q and UVRKRV, both interaction terms are

positive and statistically signicant at 1% level. Large and more productive rms experience higher

productivity growth when there is growth opportunity in the industry. Analyzing how changes inindustry valuation aect rm-specic productivity allows us to separate the level eect from the

re-distribution eect. Our nding suggests that large and more productive rms benet more when

industry experiences high valuation. We do not nd the same pattern when we use UVPV.


5 Valuation and Capital Expenditure

If valuation is indeed capturing the growth opportunity of an industry, then we should observe

rms invest more when valuation is high. Table 5 examines the relationship between valuation and

capital expenditure on the industry level:

Capexi;t+1 = ai + ft + b V aluationi;t + c Xit

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The dependent variable is the average rate of capital expenditure in industry i at t+1. In addition to

the valuation measure which is our main focus, we also include other industry characteristics (Xit)

for control purposes. For example, we use operating margin (opmarg) as a proxy for protability,

asset turnover rate (atturn) as a proxy for asset utilization, and include an dummy variable (d_econ)

to indicate the demand condition - it is equal to one if changes in shipment are negative in the

past two consecutive years, three if changes are positive in the past two consecutive years and two

otherwise. Other studies have suggested that rms with higher asset turnover ratio, more cash in

hand and positive demand are more likely to invest. All independent variables are lagged by one

year and we include year and industry xed eects in all specications.


Capital expenditure is positively related to valuation, signicant at 1% level. Both Tobins q

and UVs predict future capital expenditure, separately or jointly. Industries with higher Tobins

q or UVs have higher capital expenditure, suggesting that rms invest more when productivity is

perceived to improve. In addition, we nd that capital expenditure is high when asset turnover rate

is high and when the industry is experiencing positive demand shocks.

Table 6 estimates the eect of valuation on capital expenditure on the rm level using the sub-

sample of public rms and nd similar results. To separate the industry eect from the rm eect,

for each valuation measure, we break it into an industry component and a rm-specic compo-

nent and then regress capital expenditure ration on lagged industry and rm valuation measures

controlling for other rm characteristics:

Capexj;i;t+1 = ai + ft + b V aluationi;t + c


+ c Xj;i;t (7)

for rm j in industry i at time t + 1. We also include year and industry eects (a0is and f0

ts) in our

regression. Table 6 presents our ndings. Both industry and rm-specic valuation matter. Firms

invest more when industry valuation is high and when rm-specic valuation is high. Both Tobins

q and UVs are statistically signicant at 10% level, individually or jointly. In addition, we nd

that rms with higher productivity and higher protability also invest more. Capital expenditure

is also higher in rms main segment (as compared to peripheral segment)6 and when demand is

high (increase in shipment). It is worth noting that in both industry and rm-level regressions,

the coecients on UVRKRV are consistently bigger than coecients on UVPV. Since UVPV has

6 We dene a segment to be main segment if the segment sales account for more than 25% of rms total sales.

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ltered out changes in valuation due to changes in volatility while UVRKRV has not, it implies that

investments are higher when volatility decrease and therefore is consistent with our earlier ndings

that UVRKRV predicts future productivity at the rm level while UVPV does not.

In sum, we show using both industry and rm-level evidence that capital expenditure is related

to industry and rm valuation measures. Our nding here further supports the view that marketvaluation reects changes in expectation on fundamentals and that rms make investment decisions

to take advantage of new opportunities.


A few papers in the literature have documented that the eect of valuation on capital expenditure

is mainly driven by nancial constrained rms. Next, we test whether this pattern is also present

in our dataset. We use the existence of bond rating as a proxy for nancial constraint followingKashyap, Stein and Wilcox (1993). Table 7 shows that although non-rated rms invest more when

valuation measures are high, our results from Table 6 remain hold for all rms. That is, rms on

average invest more when valuation measures (both industry and rm-specic) are high although

the valuation - capital expenditure sensitivity is bigger for nancially constrained rms.


6 Valuation and Mergers

If high valuation signals better growth opportunity and not all rms are aected uniformly, high

valuation may also lead to higher intensity of asset reallocation through mergers and acquisition.

In this section, we investigates how valuation aects the rate of asset sales using both industry and

rm-level panels. For industry panel, we regress the rate of transaction in the next year on valuation

measures while controlling for other industry characteristics such as protability, asset turnover rate

and dummy for demand conditions (as dened above). Since mergers tend to cluster over time, we

also include a wave dummy which indicates years with abnormally high merger activity following

Maksimovic, Phillips and Yang (2010). All independent variables are lagged by one year and we

include industry xed eects in all specications:

% of Transactionsi;t+1 = ai + b V aluationi;t + c Xit

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Table 8 reports our ndings. Panel A shows that all three valuations (Tobins q, UVRKRV and

UVPV) predict high rate of transaction. When we include both Tobins q and UV measure in the

regression, the eect of q becomes smaller. In panel B, we split the total rate of transaction into

within and outside industry transactions based on whether the acquirer has operated in the same

industry before. On average, 60% of all transactions happen between rms in the same industry.

Interestingly, within and outside transactions are driven by dierent factors. Tobins q has a positive

relationship for within industry transactions, signicant at 1% level; but is insignicant in predicting

outside transactions. On the other hand, UV measures consistently predict outside acquisitions but

not within industry transactions. Firms are more likely to buy or sell assets within the industry

when Tobins q is high while industries with higher UVs are more likely to attract acquirers from

other industries.


To better understand how merger decisions relate to industry and rm-specic valuation on both

sides of the trade, we analyze decisions to buy or sell assets on the rm level using the subsample

of public rms with the following specication:

Dj;i;t+1 = ai + ft + b V aluationi;t + c


+ c Xj;i;t (8)

For purchase decisions, the dependent variable is an indicator variable which equals 1 if a rm

buys in the existing industry (Within Buy), 2 if it buys in a new industry(Outside Buy) and 0

otherwise. For sales decisions, the dependent variable equals 1 if a rm sells assets and 0 otherwise.

Table 9 Panel A presents our results on purchase decisions. Higher rm-specic valuation leads

to higher probability to purchase assets although the eect is stronger for within buy. On the

other hand, rms are more likely to buy assets in their existing industries if industry valuation

is high and they are more likely to buy assets in new industries when their existing industries

have lower valuation. In all specications, TFP has a positive coecient, suggesting that acquirers

have higher productivity. Although the signs are consistent using either UVRKRV or UVPV, the

statistical signicance is higher when UVRKRV is used as valuation measure. In Table 9 Panel B,

we present our ndings on decisions to sell assets. Sellers have lower rm-specic valuation and

lower productivity. In addition, rms are more likely to sell assets when they have high industry

valuation. Combining ndings from both panels, our result is consistent with the view that asset

sales serve as a channel to move resources from less to more productive rms within or across

15


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industries. As such, it helps more productive rms to exit their own industry when opportunity is

not present and enter into industries when future is much brighter. Consistent with evidence from

our industry level analysis, industry valuation (both total and unexplained) is positively related to

the rate of transaction.


Recent work, in particular by Shleifer and Vishny (2003) and Rhodes-Kropf, Robinson, and

Vishwanathan (2005) has argued that merger activity is to a large extent driven by misvaluation.

One implication of this work is that a signicant fraction of mergers might result in the misallocation

of assets to acquirers who may not be able to operate them eciently. If acquisition is driven by

misvaluation rather than eciency consideration, we would observe that target plants acquired by

overvalued buyers realize less or even negative change in productivity. To test this hypothesis,

we split the sample into three categories: no transaction, buyers with low-UV, and buyers with

high-UV, and compare changes in TFP in each group. We dene a buyer to have high-UV if its

unexplained valuation is above the median of all rms in our sample (based on RKRV). Table 10

Panel A presents our nding. The productivity of targets plants increases after the acquisition, and

the improvement is not smaller when acquirers have higher unexplained valuation. Similar results

are found when we use the level of UV - Panel B shows that the interaction between the indicator

variable for transaction (D_SALE) and UV is insignicant for all time windows.


The problem would be the most severe in diversifying mergers when rms in overvalued industries

buy assets in other industries to take advantage of the mispricing between industries, as suggested by

models of misvaluation-driven mergers. To examine how the dierence in industry valuation aects

merger outcomes, we construct a sample with only diversifying acquisitions in which acquirers did

not have presence in the industries they purchase assets from prior to the transaction. We further

divide the sample into two equally-sized groups based on the relative industry UV measures (basedon RKRV) between the buyer and seller. The low-RUV group includes transactions in which the

relative industry valuation between the buyer and seller is below the sample median and the high-

RUV group includes transaction in which the relative valuation is above the sample median. Table

11 Panel A compares change of TFP for transacted plants between low- and high-RUV groups one,

two and three years after the transaction. On average, transacted plants experience an increase

in productivity and the dierence is not signicantly dierent if the buyer comes from a relatively

16


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highly valued industry than the seller. In other words, although higher unexplained valuation leads

to more acquisitions, it does not aect the post-merger productivity changes for transacted plants.

Table 11 Panel B show that among transacted plants in diversifying acquisitions, the improvement

of productivity is positively related to acquirers TFP in the home industry and the dierence in

Tobins q between the acquirer and the target rms.


Our earlier analysis (in particular in Table 8 and 9) suggests that better rms use acquisitions to

enter new industries when their home industry have limited growth opportunity. Given that there is

about 40% of the cases in which buyers come from industries with higher relative valuation (mostly

in high-RUV group), a natural question to ask is that if valuation signals growth opportunity, why

would rms exit their home industry to enter a new industries with lower valuation? To answer

this question, for our sample of diversifying mergers, we track the change of valuation in acquirers

home industry after the acquisition and Table 11 Panel C reports our ndings. On average, after

the transaction, acquirers home industry experience drops in UV and the decrease is much bigger

in the high-RUV group in which acquirers are from relatively more valued industries. Although

preliminary, our nding points to a direction such that rms choose to exit from their existing

industries in expectation of lower future growth opportunities.

7 Conclusions

In this paper we investigate the relation between market (mis)valuation and the subsequent produc-

tivity of rms and industries. Using measures recently introduced by Pastor and Veronesi (2003)

and Rhodes-Kropf, Robinson and Vishwanathan (2005), we decompose rm valuation, measured by

Tobins q, into a portion that is predicted by observable characteristics and a portion, unexplained

valuation, that measures the deviation from the predicted level. In the literature, unexplained

valuation has been interpreted as "misvaluation," unrelated to the rms fundamental value.

We show that both Tobins q and the unexplained valuation predict future productivity similarly.

Firms with high valuation, are positively associated with higher changes in productivity in the

future. We also investigate how valuation, both Tobins q and the unexplained portion, predict

investment activities such as capital expenditure and acquisition decisions. Both Tobins q and

unexplained valuation predict increased capital expenditures. Firms invest more both when they

17


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have higher valuations and when their industry has a higher valuation. Financially constrained

rms show a stronger sensitivity of capital expenditures to valuation.

High valuations, both Tobins q and unexplained, also predict acquisitions and sales. Examining

both within and across industry acquisitions, we nd that rms with high rm-specic valuation are

more likely to buy assets in their existing industries when industry valuation is high, and are morelikely to acquire assets to enter other industries when their existing industry has a low valuation.

Sales of assets are more likely when a rm has low rm-specic valuation, but industry valuation

is high. Our ndings suggest that asset sales serve as a channel to move resources from less to

more productive rms within or across industries. It helps more productive rms to exit their own

industry when opportunity is not present and enter into industries when future is much brighter.

Examining acquisitions across industries, we nd that the productivity of targets plants in-

creases after the acquisition. The increase is not smaller when the acquirer has higher unexplainedvaluation or is from a relatively more overvalued industry. Thus, while industry (mis)valuation

predicts acquisitions, there is no evidence that high industry valuation leads to inecient diversi-

cation or a misallocation of investment. We interpret the positive association between unexplained

valuation and Total Factor Productivity documented in our paper (even controlling for Tobins q)

as an indication that unexplained valuation might be proxying for a relaxation of either nancing or

informational constraints facing rms, rather than being a measures of misvaluation and reecting

changes in investor sentiment unmoored from rm and industry fundamentals.

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REFERENCES

Andrade G., Mark Mitchell and Eric Staord, 2001, New Evidence and Perspectives on Mergers,Journal of Economic Perspectives 15, 103-120.

Asquith, P., Bruner, R. F., Mullins, Jr., D. W., 1983. The gains to bidding rms from merger,Journal of Financial Economics 11, 121139.

Baker, M., Stein, J. and J. Wurgler, 2003, When Does the Market Matter? Stock Prices and theInvestment of Equity Dependent Firms. Quarterly Journal of Economics 118, 969-1006

Baker, M., and Wurgler, J., 2006, Investor Sentiment and the Cross-Section of Stock Returns,Journal of Finance 61(4), 1645-1680

Bakke, T. and Whited, T., 2010, Which Firms Follow the Market? An Analysis of CorporateInvestment Decisions. Review of Financial Studies 23, 1941-1980

Bartelsman, E. and Gray, W., 1994, The NBER manufacturing productivity database, NBERTechnical Working Papers 0205, National Bureau of Economic Research, Inc.

Beaudry, P. and Portier, F. , 2006, Stock Prices, New, and Economic Fluctuations. American

Economic Review 96, 1293-1307Betton, S., B. Espen Eckbo and Karin S. Thorburn, 2008, Corporate Takeovers, in B.E. Eckbo

(ed.) Handbook of Corporate Finance: Empirical Corporate Finance, Vol. 2, Elsevier/North-Holland, Ch. 15, 289-427.

Blanchard, O., Rhee, C. and Summers, L., 1993, The Stock Market, Prot, and Investment.Quarterly Journal of Economics 108, 115-136

Campello, M. and Graham, J. , 2007, Do Stock Prices Inuence Corporate Decisions? Evi-dence from the Technology Bubble. Working Paper, Duke University

Davis, Stephen, John Haltiwinger, Ron Jarmin, Joshua Lerner and Javier Miranda, 2010, PrivateEquity and Employment, Working Paper, Center for Economic Studies.

Delong, J., Shleifer, A., Summers, L., and Waldmann, R., 1990, Noise Trader Risk in FinancialMarkets, Journal of Political Economy 98, 703-738.

Dong, M., Hirshleifer, D., Richardson, S., and Teoh, S., 2006, Does Investor Misvaluation Drivethe Takeover Market, Journal of Finance 61, 725-761

Dow, J. and G. Gorton, 1997, Stock Marke Eciency and Economic Eciency: Is There a Con-nection? Journal of Finace 52, 1087-1129

Eisfeldt, A., and A. Rampini, 2006, Capital Reallocation and Liquidity, Journal of MonetaryEconomics 53, 369 - 399

Fazzari, S., R. Hubbard and B. Petersen, 1988, Financing Constraints and Corporate Investment.

Brookings Papers on Economic Activity 1: 141-195.Fischer, S. and R. Merton, 1984, Macroeconomics and Finance: The Role of Stock Market.

Carnegie-Rochester Conference Series on Public Policy 21, 57-108.

Gilchrist, S., C. Himmelberg, and G. Huberman, 2005, Do Stock Price Bubbles Inuence CorporateInvestments? Journal of Monetary Economics 4, 805-827

Harford, J., 2005, What drives merger waves? Journal of Financial Economics 77, 529-560.

Hoberg, G., and G. Phillips, 2010, Real and Financial Industry Booms and Busts, Journal ofFinance.

19


22/37

Kashyap, A. K., Stein, J., and Wilcox, D. W., Monetary Policy and Bank Lending, AmericanEconomic Review 83, 78-98

Kovenock, D., and G. Phillips, 1997, Capital structure and product market behavior: An exami-nation of plant exit and investment decisions, Review of Financial Studies 10, 767803.

Maksimovic, Vojislav and Gordon Phillips, 2001, The Market for Corporate Assets: Who Engagesin Mergers and Asset Sales and are there Gains?, Journal of Finance.

, 2002, Do Conglomerate Firms Allocate Resources Ineciently Across Industries?, Journal ofFinance.

Maksimovic, Vojislav and Gordon Phillips, 2007, Conglomerate Firms and Internal Capital Mar-kets, in B. Espen Eckbo (ed.): Handbook of Corporate Finance - Empirical Corporate Finance,North Holland Handbooks in Finance, Elsevier Science B.V.

Makimovic, V., G. Phillips, and L. Yang, 2010, Private and Public Merger Wages, Working Paper

Morck, R., A. Shleifer and R. Vishny, 1990, The Stock Market and Investment: Is the Marketa Side Show? Brookings Paper on Economic Activity 2: 157-215

Mundlak, Y., 1978, On the Pooling of Time Series and Cross Section Data, Econometrica, 69-85

Ovtchinnikov, A. and J. McConnell, 2009, Capital Market Imperfections and the Sensitivity ofInvestment to Stock Prices, Journal of Financial and Quantitative Analysis 44, 551-578

Pastor, L. and P. Veronesi, 2003, Stock Valuation and Learning about Protability, Journal ofFinance 58

Polk, C. and P. Sapienza, 2009, The Stock Market and Corporate Investment: A Test of CateringTheory. Revie of Financial Studies 22, 435-480

Rhodes-Kropf, Matthew, and S. Vishwanathan, 2004, Market Valuation and Merger Waves, Jour-nal of Finance 59, 2685-2718.

Rhodes-Kropf, Matthew, and David Robinson, 2006, The Market for Mergers and the Boundaries

of the Firm, forthcoming in the Journal of Finance.Schlingemann, Frederik, P., Rene M. Stulz, and Ralph A. Walkling, 2002, Asset liquidity and

segment divestitures, Journal of Financial Economics.

Shleifer, Andrei, and Robert W. Vishny, 2003, Stock Market Driven Acquisitions, Journal ofFinancial Economics 70, 295-311.

Shleifer, A. and L. Summers, 1990, The Noise Trader Approach to Finance, Journal of EconomicPerspectives 4, 19-33

Stein, J. , 1996, Rational Capital Budgeting in an Irrational World. Journal of Business 69,429-455

Subrahmanyam, A. and S. Titman, 1999, The Going Public Decision and Development of Financial

Markets. Journal of Finance 54, 1045-1082

Yang, Liu, 2008, The real determinants of asset sales, Journal of Finance, 63, 2231-2262.

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Panel A

(1) (2) (3) (4) (5)

Tobin's Q 0.014 *** 0.007 0.010 **

(0.004) (0.005) (0.005)

UV_RKRV 0.012 *** 0.009 **

(0.003) (0.004)

UV_PV 0.016 *** 0.013 ***

(0.004) (0.004)

Lagged TFP 0.888 *** 0.897 *** 0.897 *** 0.894 *** 0.893 ***(0.011) (0.011) (0.011) (0.011) (0.011)

Constant -0.001 0.014 *** 0.015 *** 0.006 0.005

(0.004) (0.002) (0.002) (0.005) (0.005)

R-Square 0.8051 0.8082 0.8113 0.8087 0.8121

N 2764 2627 2456 2627 2456

Panel B

(1) (2) (3) (4) (5)

Tobin's Q 0.020 *** 0.005 0.013 **

(0.006) (0.006) (0.005)

UV_RKRV 0.018 *** 0.016 ***

(0.005) (0.006)

UV_PV 0.025 *** 0.020 ***

(0.005) (0.005)

Lagged TFP 0.782 *** 0.782 *** 0.805 *** 0.781 *** 0.801 ***

(0.064) (0.069) (0.068) (0.069) (0.067)

Constant 0.004 0.026 *** 0.025 *** 0.02 ** 0.012

(0.010) (0.006) (0.006) (0.009) (0.008)

Chi Sq. 154 131 154 132 170

N 2612 2473 2320 2473 2320

This table reports regression results from industry level regressions. Panel A reports the estimated

coefficient using industry fixed effect models and panel B reports estimated coefficients using

dynamic panel models based on GMM estimator derived in Arellano and Bond (1991). In both

panels, the dependent variables are TFP in the next period and independent variables include Tobin's

Q, MISV based on RhodesKropf, Robinson and Vishwanathan (2005) and Pastor and Veronesi

(2003) respectively and current period TFP. We include industry and year fixed effects in all

specifications. The Robust standard errors allow clustering at the industry level and are reported in

parentheses. *, ** and *** represent significance at 10%, 5%, and 1% level, respectively.

Table 1: TFP and Valuation (Industry Level)


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Panel A:

(1) (2) (3) (4) (5) (6)

Tobin's Q -0.004 0.023 ***

(0.010) (0.006)

UV_RKRV -0.002 0.020 ***

(0.008) (0.005)

UV_PV 0.012 0.022 ***

(0.007) (0.005)

TFP 0.658 *** 0.593 *** 0.626 *** 0.862 *** 0.865 *** 0.881 ***

(0.033) (0.035) (0.035) (0.016) (0.016) (0.016)Constant 0.040 *** 0.043 *** 0.039 *** -0.008 0.018 *** 0.018 ***

(0.010) (0.004) (0.004) (0.006) (0.002) (0.002)

Chi Sq 401 306 319 2977 3004 3127

N 812 732 633 1800 1741 1687

Table 2: TFP and Valuation (Industry Level) - Robustness Checks

This table reports regression results from industry level regressions based on different sample splits. Panel A splits the

sample based on percentage of the public firms, Panel B splits the sample based on concentration ratio using

Herfindahl Index (of sales); Panel C splits the sample using the percentage of small firms (


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Panel B

(1) (2) (3) (4) (5) (6)

Tobin's Q 0.025 *** 0.009

(0.005) (0.010)

UV_RKRV 0.014 *** 0.016 *

(0.004) (0.008)

UV_PV 0.012 *** 0.030 ***

(0.004) (0.009)

TFP 0.625 *** 0.588 *** 0.611 *** 0.858 *** 0.876 *** 0.900 ***

(0.024) (0.024) (0.025) (0.021) (0.020) (0.019)

Constant 0.013 ** 0.044 *** 0.043 *** 0.011 0.019 *** 0.019 ***

(0.006) (0.003) (0.003) (0.011) (0.003) (0.003)

Chi Sq 704 578 589 1754 1939 2317

N 1608 1550 1501 1004 923 819

Panel C

(1) (2) (3) (4) (5) (6)

Tobin's Q 0.005 0.030 ***

(0.006) (0.008)

UV_RKRV 0.009 0.021 ***(0.005) (0.005)

UV_PV 0.009 * 0.031 ***

(0.005) (0.006)

TFP 0.571 *** 0.540 *** 0.574 *** 0.889 *** 0.891 *** 0.904 ***

(0.029) (0.029) (0.028) (0.017) (0.016) (0.016)

Constant 0.047 *** 0.057 *** 0.052 *** -0.017 ** 0.014 *** 0.016 ***

(0.008) (0.004) (0.004) (0.008) (0.002) (0.002)

Chi Sq 392 359 424 3060 2938 3053

N 1201 1136 1060 1411 1337 1260

Panel D

(1) (2) (3) (4) (5) (6)

Tobin's Q 0.032 *** 0.005

(0.007) (0.007)

UV_RKRV 0.022 *** 0.012 *

(0.005) (0.007)

UV_PV 0.025 *** 0.017 **

(0.005) (0.007)

TFP 0.688 *** 0.640 *** 0.673 *** 0.873 *** 0.875 *** 0.882 ***

(0.025) (0.025) (0.026) (0.018) (0.018) (0.018)

Constant -0.001 0.034 *** 0.033 *** 0.013 0.019 *** 0.019 ***(0.007) (0.003) (0.003) (0.009) (0.003) (0.003)

Chi Sq 766 656 714 2234 2268 2520

N 1290 1207 1123 1322 1266 1197

Low Capex Industries High Capex Industries

Competitive Industries Concentrated Industries

Small Firm Industries Large Firm Industries


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(1) (2) (3)

ITobin'Q 0.0237 ***

(0.001)

Large * ITobin's Q 0.0148 ***

(0.001)

HighTFP * ITobin's Q 0.0375 ***

(0.001)

IUV_RKRV 0.0191 ***

(0.002)

Large * IUV_RKRV 0.0202 ***

(0.002)

HighTFP * IUV_RKRV 0.0162 ***

(0.002)

IUV_PV -0.0007

(0.002)

Large * IUV_PV 0.0001

(0.002)

HighTFP * IUV_PV -0.0056 **

(0.002)TFP 0.5453 *** 0.6086 *** 0.6093 ***

(0.002) (0.001) (0.001)

Constant 0.0009 -0.0012 *** -0.001 ***

(0.001) (0.000) (0.000)

R-Square 0.37 0.36 0.36

N 463944 454937 442847

Table 3 TFP, Misvaluation and Firm Characteristics

This table reports firm level regression results. The dependent variable is firm-level TFP

(demeaned from industry average) in the next period. ITobin's Q is the industry average

Tobin's q, IUV_RKRV and IUV_PV are industry average misvaluation measures based on

RhodesKropf, Robinson and Vishwanathan (2005) and Pastor and Veronesi (2003),

respectively. Large is a dummy variable that equals to 1 if firm size is greater than the

industry median, and HighTFP is a dummy variable that equals to 1 if firm TFP is greater

than the industry median. All independent variables are lagged. We include industry and year

fixed effects in all specifications. The Robust standard errors allow clustering at the industry

level and are reported in parentheses. *, ** and *** represent significance at 10%, 5%, and

1% level, respectively.


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(1) (2) (3) (4) (5)

Tobin's Q 0.0101 *** 0.0095 ** 0.0124 ***

(0.003) (0.004) (0.004)

UV_RKRV 0.0078 ** 0.0046

(0.003) (0.003)

UV_PV -0.0036 -0.0051 *

(0.003) (0.003)

Lagged TFP 0.3901 *** 0.3857 *** 0.3783 *** 0.3854 *** 0.3780 ***

(0.008) (0.008) (0.008) (0.008) (0.008)

Constant 0.0291 *** 0.0294 *** 0.0318 *** 0.0293 *** 0.0315 ***

(0.001) (0.001) (0.001) (0.001) (0.001)

Chi Sq. 2657 2491 2186 2500 2199

N 45155 43151 39784 43151 39784

Table 4: TFP and Valuation (Firm Level)

This table reports results from firm level regressions. It reports estimated coefficients using dynamic

panel models based on GMM estimator derived in Arellano and Bond (1991). In both panels, the

dependent variables are TFP in the next period, and independent variables include Tobin's Q, MISV

based on RhodesKropf, Robinson and Vishwanathan (2005) and Pastor and Veronesi (2003)

respectively and TFP in the current period. For Panel B, we exclude firms with less than 10 years of

data. The Robust standard errors allow clustering at the industry level and are reported in



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(1) (2) (3)

Tobin's Q 1.050 *** 0.684 ** 1.542 ***

(0.294) (0.312) (0.302)

UV_RKRV 1.344 ***

(0.300)UV_PV 1.098 ***

(0.264)

OPMARG 0.103 -0.527 -0.781

(1.107) (1.033) (1.091)

ATTURN 0.153 *** 0.185 *** 0.203 ***

(0.038) (0.036) (0.036)

D_ECON=2 0.244 0.25 0.215

(0.181) (0.169) (0.171)

D_ECON=3 0.751 *** 0.683 *** 0.66 ***

(0.207) (0.193) (0.196)

CONSTANT 11.952 *** 12.84 *** 11.853 ***

(0.514) (0.547) (0.519)

R-Square 0.4289 0.4853 0.5037

N 2494 2368 2209

Table 5 Valuation and Capital Expenditure (Industry Level)

This table reports industry level regression results. The dependent variable is the

average capital expenditure ratio (over lagged assets) in the industry (in percentage).

Tobin's Q is the average Tobin's Q in the industry. MISV_RKRV and MISV_PV are

industry misvaluation measures based on RhodesKropf, Robinson and Vishwanathan

(2005) and Pastor and Veronesi (2003), respectively. OPMARG is the industry average

operating margin (computed as the ratio of operating income over sales), ATTURN is

the rate of asset turnover (computed as the sales over total assets). D_Econ is a dummy

variable which equals 1 if changes in industry shipments are negative in the past two

consecutive years, 3 if industry shipments are positive in the past two consecutive years

and 2 otherwise. All independent variables are lagged. We include industry fixed effects

and year fixed effects in all specifications. The Robust standard errors allow clustering

at the industry level and are reported in parentheses. *, ** and *** represent

significance at 10%, 5%, and 1% level, respectively.


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(1) (2) (3) (4) (5)

ITobin's Q 0.0444 *** 0.0273 *** 0.0388 ***

(0.005) (0.006) (0.006)

Firm Tobin's Q 0.0248 *** 0.0217 *** 0.0220 ***

(0.001) (0.002) (0.002)

IUV_RKRV 0.0528 *** 0.0372 ***(0.005) (0.006)

Firm UV_RKRV 0.0260 *** 0.0108 ***

(0.002) (0.002)

IUV_PV 0.0338 *** 0.0239 ***

(0.005) (0.006)

Firm UV_PV 0.0103 *** 0.0038 *

(0.002) (0.002)

Ind_TFP 0.0406 *** 0.0295 ** 0.0097 0.0369 *** 0.0243 *

(0.014) (0.014) (0.015) (0.014) (0.015)

Firm TFP 0.0302 *** 0.0266 *** 0.027 *** 0.0259 *** 0.0257 ***

(0.004) (0.004) (0.004) (0.004) (0.004)

Size -0.0203 *** -0.0208 *** -0.0182 *** -0.0206 *** -0.0186 ***

(0.001) (0.001) (0.001) (0.001) (0.001)

OPMARG 0.0151 *** 0.0225 *** 0.0242 *** 0.0183 *** 0.0187 ***

(0.005) (0.005) (0.006) (0.005) (0.006)

D_Main 0.0165 *** 0.0173 *** 0.0083 *** 0.0165 *** 0.0093 ***

(0.002) (0.002) (0.002) (0.002) (0.002)

Change in Shipments 0.013 ** 0.0114 * 0.0172 *** 0.0098 0.0138 **

(0.006) (0.006) (0.007) (0.006) (0.007)

Constant 0.3314 *** 0.3837 *** 0.3525 *** 0.356 *** 0.3221 ***

(0.010) (0.009) (0.009) (0.011) (0.010)R-Square 0.031 0.029 0.026 0.032 0.030

N 62922 59104 52375 59104 52375

Table 6: Valuation and Capital Expenditure (Firm Level)

This table reports firm level regression results. The dependent variable is the rate of capital expenditure

(over lagged assets). ITobin's Q is the industry Tobin's Q. IUV_RKRV and IUV_PV are industry

misvaluation measure based on RhodesKropf, Robinson and Vishwanathan (2005) and Pastor and

Veronesi (2003), respectively. Tobin's Q, UV_RKRV and UV_PV are firm-level demeaned Tobin's Q

and misvaluation measures. Firm TFP and Ind_TFP measure the demeaned firm level TFP and the

industry average TFP. Size is the log of firm output. OPMARG is the operating margin computed as the

operating income over total sales. D_main is a dummy variable which equals to 1 if it is firm's main

segment. Change in Shipments is the percentage change of shipments from the last to the current year.

All independent variables are lagged. We include industry fixed effects and year fixed effects in all

specifications. The Robust standard errors allow clustering at the industry level and are reported in



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(1) (2) (3)

ITobin's Q 0.0448 ***

(0.005)

Firm Tobin's Q 0.0358 ***

(0.002)

Rated * Tobin's Q -0.0166 ***

(0.002)IUV_RKRV 0.0525 ***

(0.005)

Firm UV_RKRV 0.0327 ***

(0.002)

Rated * Firm UV_RKRV -0.0128 ***

(0.002)

IUV_PV 0.0337 ***

(0.005)

Firm UV_PV 0.0136 ***

(0.002)

Rated * Firm UV_PV -0.0059 **(0.002)

Rated 0.0025 ** 0.003 ** 0.0036 ***

(0.001) (0.001) (0.001)

Ind_TFP 0.0415 *** 0.0303 ** 0.01

(0.014) (0.014) (0.015)

Firm TFP 0.0305 *** 0.0264 *** 0.0269 ***

(0.004) (0.004) (0.004)

Size -0.0206 *** -0.021 *** -0.0188 ***

(0.001) (0.001) (0.001)

OPMARG 0.0151 *** 0.0226 *** 0.0238 ***

(0.005) (0.005) (0.006)D_Main 0.0171 *** 0.0182 *** 0.0105 ***

(0.002) (0.002) (0.002)

Change in Shipments 0.0125 ** 0.0111 * 0.0172 ***

(0.006) (0.006) (0.007)

Constant 0.3338 *** 0.3848 *** 0.3554 ***

(0.010) (0.009) (0.009)

R-Square 0.0323 0.0299 0.0257

N 62922 59104 52375

Table 7: Valuation, Rating and Capital Expenditure (Firm Level)

This table reports firm level regression results. The dependent variable is the capital expenditure

ratio (over lagged assets). ITobin's Q is the industry Tobin's Q. IUV_RKRV and IUV_PV are

industry misvaluation measure based on RhodesKropf, Robinson and Vishwanathan (2005) and

Pastor and Veronesi (2003), respectively. Tobin's Q, UV_RKRV and UV_PV are firm-level

demeaned Tobin's Q and misvaluation measures. Rated is a dummy variable which equals to 1 if a

firm has a debt rating and 0 otherwise. Firm TFP and Ind_TFP measures the demeaned firm level

TFP and the industry average TFP. Size is the log of firm total output. OPMARG is the operating

margin computed as the operating income over total sales. D_main is a dummy variable which

equals to 1 if it is firm's main segment. Change in Shipments is the percentage change ofshipments from the last to the current year. All independent variables are lagged. We include

industry fixed effects and year fixed effects in all specifications. The Robust standard errors allow

clustering at the industry level and are reported in parentheses. *, ** and *** represent


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Panel A (1) (2) (3)

Tobin's Q 0.888 *** 0.287 0.537 **

(0.198) (0.232) (0.214)

UV_RKRV 0.877 ***

(0.209)

UV_PV 0.462 **

(0.204)

OPMARG -0.546 -1.257 -1.006

(0.972) (0.971) (0.980)

ATTURN -0.062 ** -0.074 ** -0.079 **

(0.031) (0.030) (0.031)

D_ECON=2 0.005 0.042 0.037

(0.152) (0.153) (0.153)

D_ECON=3 -0.195 -0.168 -0.182

(0.159) (0.160) (0.160)

GW_N 1.919 *** 2.023 *** 2.027 ***(0.379) (0.403) (0.398)

CONSTANT 3.314 *** 4.092 *** 3.832 ***

(0.379) (0.403) (0.398)

R-Square 0.1381 0.1457 0.15

N 2070 1996 1907

Table 8: Valuation and Asset Sales (Industry Level)

This table reports industry level regression results. In Panel A, the dependent variable is the rate of

transaction in the industry (in percentage). In Panel B, we separate transactions in which acquirer firm is

from the same industry (within industry transaction) from transactions in which acquirer firm is from a

different industry (outside industry transactions) and the dependent variable is the percentage of

transaction in the corresponding category. Tobin's Q is the industry Tobin's Q. UV_RKRV and UV_PV are

industry misvaluation measures based on RKRV(2005) and PV(2003), respectively. OPMARG is the

industry average operating margin (computed as the ratio of operating income over sales), ATTURN is the

rate of asset turnover (computed as the sales over total assets). D_Econ is a dummy variable which equals

1(3) if changes in industry shipments are negative(positive) in the past two consecutive years and 2otherwise. GW_N is a dummy variable that indicates high transaction volume years (based on

Maksimovic, Phillips and Yang (2010). All independent variables are lagged. We include industry fixed

effects and year fixed effects in all specifications. The Robust standard errors allow clustering at the

industry level and are reported in parentheses. *, ** and *** represent significance at 10%, 5%, and 1%


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Panel B

(1) (2) (3) (4)

Tobin's Q 0.534 *** 0.466 *** -0.247 0.071

(0.166) (0.151) (0.167) (0.157)

UV_RKRV -0.104 0.981 ***

(0.149) (0.150)

UV_PV -0.135 0.597 ***

(0.143) (0.149)

OPMARG -1.311 * -1.266 * 0.054 0.260

(0.694) (0.689) (0.699) (0.716)

ATTURN -0.039 * -0.043 * -0.034 -0.036

(0.022) (0.022) (0.022) (0.023)

D_ECON=2 0.128 0.061 -0.086 -0.024

(0.109) (0.107) (0.110) (0.112)

D_ECON=3 0.018 -0.021 -0.186 -0.161

(0.114) (0.113) (0.115) (0.117)

GW_N 1.175 *** 1.194 *** 0.849 *** 0.833 ***

(0.090) (0.089) (0.091) (0.092)

CONSTANT 1.451 *** 1.553 *** 2.641 *** 2.279 ***

(0.288) (0.280) (0.290) (0.291)R-Square 0.110 0.119 0.064 0.054

N 1996 1996 1907 1907

Within Industry Transactions Outside Industry Transactions


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Panel A

Firm Tobin's Q 0.88 *** 0.12

(0.10) (0.10)ITobin's Q 0.01 -1.18 ***

(0.20) (0.20)

Firm UV_RKRV 1.48 *** 0.23 *

(0.20) (0.10)

IUV_RKRV 0.71 ** -0.60 **

(0.40) (0.30)

Firm UV_PV 0.27 0.03

(0.20) (0.10)

IUV_PV 0.42 -0.34

(0.40) (0.30)

SIZE 1.48 *** -0.60 *** 1.43 *** -0.59 *** 1.44 *** -0.49 ***

(0.10) (0.00) (0.10) (0.00) (0.10) (0.00)

TFP -0.15 0.27 *** -0.15 0.25 *** -0.08 0.26 ***

(0.10) (0.10) (0.10) (0.10) (0.10) (0.10)

D_Main 5.51 *** -4.18 *** 5.49 *** -4.35 *** 5.45 *** -4.11 ***

(0.20) (0.10) (0.20) (0.10) (0.20) (0.10)

Change in Shipments -1.53 *** 1.49 *** -1.67 *** 1.40 *** -1.55 *** 1.26 ***

(0.50) (0.30) (0.50) (0.30) (0.50) (0.30)

HERF -13.68 *** 8.03 *** -13.38 *** 8.38 *** -13.48 *** 7.96 ***

(2.10) (1.20) (2.10) (1.20) (2.20) (1.20)

CI_Spread -1.53 *** -1.11 *** -1.43 *** -0.81 *** -1.52 *** -0.78 ***(0.30) (0.20) (0.30) (0.20) (0.30) (0.20)

SP Ret 1.78 *** 0.18 1.40 ** 0.38 1.35 * 0.19

(0.60) (0.40) (0.70) (0.50) (0.70) (0.50)

Chi Square 1900 1800 1600

N 61705 58657 52614

Diver. Buy

(1) (2) (3)

Within Buy Diver. Buy Within Buy Diver. Buy Within Buy

Table 9: Valuation and Decision to Buy Assets (Firm Level)

This table reports the marginal effect(multiplied by 100) of probit models. In Panel A, the dependent variable

equals to 1 if a firm buys assets in existing industries, 2 if a firms buys assets in new industries, and 0 otherwise.

In Panel B, the dependent variable is a binary variable that equals to 1 if a firm sells assets in the next period.

ITobin's Q is the industry Tobin's Q. IUV_RKRV and IUV_PV are industry misvaluation measure based on

RhodesKropf, Robinson and Vishwanathan (2005) and Pastor and Veronesi (2003), respectively. Tobin's Q,

UV_RKRV and UV_PV are firm-level demeaned Tobin's Q and misvaluation measures. Firm TFP measures the

demeaned firm level TFP . Size is the log of firm total output. D_main is a dummy variable which equals to 1 if

it is firm's main segment. Change in Shipments is the percentage change of shipments from the last to the current

year. HERF is the Herfindahl index based on sales. CI_Spread is the credit spread and SP Ret is the return of

S&P 500. All independent variables are lagged. The Robust standard errors allow clustering at the industry level

and are reported in parentheses. *, ** and *** represent significance at 10%, 5%, and 1% level, respectively.


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Panel B: Decision to Sell Assets

(1) (2) (3) (4) (5)

Firm Tobin's Q -1.49 *** -1.93 *** -1.56 ***

(0.20) (0.20) (0.20)

ITobin's Q 0.15 -0.28 -0.07

(0.30) (0.30) (0.30)

Firm UV_RKRV -0.22 1.05 ***

(0.20) (0.20)IUV_RKRV 2.48 *** 2.47 ***

(0.40) (0.40)

Firm UV_PV -0.16 1.78 ***

(0.20) (0.50)

IUV_PV 1.95 *** 0.28

(0.50) (0.20)

SIZE 1.38 *** 1.39 *** 1.40 *** 1.39 *** 1.43 ***

(0.10) (0.10) (0.10) (0.10) (0.10)

TFP -2.09 *** -2.40 *** -2.63 *** -2.19 *** -2.40 ***

(0.40) (0.40) (0.40) (0.40) (0.40)

D_Main -5.34 *** -5.17 *** -5.16 *** -5.08 *** -5.21 ***

(0.20) (0.20) (0.20) (0.20) (0.20)

Change in Shipments -0.93 -1.11 * -0.85 -1.06 * -0.87

(0.60) (0.60) (0.60) (0.60) (0.60)

HERF -7.12 *** -7.48 *** -6.52 *** -8.00 *** -7.18 ***

(2.30) (2.40) (2.50) (2.40) (2.50)

CI_Spread -3.39 *** -3.39 *** -3.23 *** -3.44 *** -3.35 ***

(0.30) (0.30) (0.30) (0.30) (0.30)

SP Ret 1.19 -0.07 0.28 -0.08 0.23

(0.70) (0.80) (0.90) (0.80) (0.80)

R-Square 0.032 0.031 0.031 0.034 0.033N 64925 61520 54906 61520 54906


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Panel A

Dependent Variable

Variable Name (1) (2) (3) (4) (5) (6)

D_Sale = 1 & D_UV=0 0.026 *** 0.023 *** 0.039 *** 0.037 *** 0.026 *** 0.027 ***

(0.00) (0.00) (0.01) (0.01) (0.01) (0.01)

D_Sale = 1 & D_UV=1 0.050 *** 0.033 *** 0.060 *** 0.044 *** 0.065 *** 0.054 ***

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

TFP -0.021 *** -0.034 *** -0.038 ***

(0.00) (0.00) (0.00)

Ln(Output) 0.057 *** 0.067 *** 0.068 ***

(0.00) (0.00) (0.00)

Constant -0.012 ** -0.621 *** -0.020 *** -0.748 *** -0.028 *** -0.774 ***

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Number of Obs 769,431 769,431 643,675 643,675 529,646 529,646

R-Square 0.1% 1.1% 0.1% 1.3% 0.2% 1.2%

Panel B

Dependent Variable

Variable Name (1) (2) (3) (4) (5) (6)

D_Sale 0.029 *** 0.025 *** 0.042 *** 0.038 *** 0.032 *** 0.031 ***

(0.00) (0.00) (0.00) (0.00) (0.01) (0.01)

D_Sale * UV 0.012 0.002 0.019 0.009 0.008 -0.001

(0.01) (0.01) (0.02) (0.02) (0.02) (0.02)

TFP -0.021 *** -0.034 *** -0.038 ***

(0.00) (0.00) (0.00)

Ln(Output) 0.057 *** 0.067 *** 0.068 ***

(0.00) (0.00) (0.00)

Constant -0.012 ** -0.621 *** -0.020 *** -0.748 *** -0.028 *** -0.774 ***

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Number of Obs 769,431 769,431 643,675 643,675 529,646 529,646

R-Square 0.1% 1.1% 0.1% 1.3% 0.2% 1.2%

TFP (-1,1) TFP(-1,2) TFP(-1,3)

Table 10: Robustness Checks: Change of TFP and Valuation

This table reports regression estimates on changes of TFP on the establishment level controlling for buyer's valuation.

D_Sale is an indicator variable that equals to 1 if the establishment is sold and 0 otherwise. D_UV is an indicator variable

that equals to 1 if buyer's UV is above the sample median and 0 otherwise. We calculate UV using the procedure of

Rhodes-Kropft, Robinson and Viswanathan (2005) as updated by Hoberg and Phillips (2009). TFP(-1, 1) is the change of

TFP from t-1 to t+1 with t being the current year. Similarly, TFP(-1,2) and TFP(-1,3) measure change of TFP from t-1 to

t+2 and t+3, respectively. We control for industry fixed effects and robust standard errors are reported in the parentheses.

*, ** and *** represent significance at 10%, 5%, and 1% level, respectively.

TFP (-1,1) TFP(-1,2) TFP(-1,3)


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Panel A: Change of TFP

Chgtfp (-1, 1) ChgTFP(-1, 2) ChgTFP(-1, 3)

Low RUV Group 3.67% 3.67% 5.50%

High RUV Group 4.42% 3.14% 4.81%

Difference -0.74% 0.53% 0.68%

T-Stat -0.342 0.292 0.262

Pvalue 0.732 0.826 0.794

Panel B: Change of TFP (Regressions)

Chgtfp (-1, 1) ChgTFP(-1, 2) ChgTFP(-1, 3)

Acquirer TFP 0.202 *** 0.292 *** 0.181 **

-0.067 -0.077 -0.085

B_IUV - S_IUV -0.007 -0.093 -0.043

-0.059 -0.065 -0.073

B_ITobinQ - S_ITobinQ 0.06 0.124 ** 0.131 **

-0.046 -0.052 -0.059

Constant 0.032 *** 0.023 * 0.047 ***

-0.011 -0.013 -0.014

R-Square 0.002 0.004 0.002N 5652 4867 4220

Panel C: Changes of Valuation in the Home Industries

Chg_MISV(-1, 1) Chg_Q(-1, 1)

Low RUV Group 2.34% 2.89%

High RUV Group -5.09% 1.07%

Difference 0.074 0.018

T-Stat 11.84 3.6159

Pvalue

(mis)valuation and investment

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