Fundamentals, Misvaluation, and Business Investment☼

December 28, 2010

Robert S. Chirinko (corresponding author) University of Illinois at Chicago, CESifo, and the Federal Reserve Bank of San Francisco

Department of Finance, University of Illinois at Chicago, 2333 University Hall, 601 South Morgan (MC 168), Chicago, Illinois 60607-7121 USA.

Email: chirinko@uic.edu; Phone: 312 355 1262; Fax: 312 413 7948.

Huntley Schaller Carleton University

Department of Economics, Carleton University, Loeb Building, 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 CANADA

Email: schaller@ccs.carleton.ca; Phone: 613 520 3751; Fax: 613 520 3906

☼ We would like to thank seminar participants at the AFA, Bank of England, Bank of International Settlements, Bank of Italy, Cass Business School, Chicago/London Conference on Financial Markets, Dutch National Bank, Emory Business School, Frankfurt, Groningen, Institute for Advanced Studies (Vienna), Illinois at Chicago, Iowa, Kentucky, Loyola, MIT, Paris-I, NBER Behavioral Finance group, NBER Capital Markets and the Economy group, NBER Macroeconomics and Individual Decision-Making group, Saskatchewan, Toronto, and Urbino. We also thank the editor, three referees, and Olivier Blanchard, Jason Cummins, Steve Fazzari, S.P. Kothari, Sydney Ludvigson, and Linda Vincent for helpful comments and discussions and Mark Blanchette, Rose Cunningham, Hans Holter, Sadaquat Junayed, and Heidi Portuondo for research assistance. Chirinko and Schaller thank the European University Institute and MIT, respectively, for providing excellent environments in which to begin this research, Chirinko thanks the Bank of England for financial support under a Houblon-Norman/George Senior Fellowship, and Schaller thanks the SSHRC for financial support. All errors and omissions remain the sole responsibility of the authors, and the conclusions do not necessarily reflect the views of the supporting institutions.

Keywords: investment, real effects of financial markets, misvaluation, stock market bubbles, fundamentals JEL codes: E44, E22, G3

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Abstract

Does stock market misvaluation affect business fixed investment? To answer this question, we

provide evidence based on U.S. firm-level panel data. We examine the orthogonality conditions

for the investment Q and Euler equations, and our qualitative tests reject the null hypothesis that

investment is unaffected by misvaluation (this result is not driven exclusively by the late 1990s).

To measure the quantitative effects on investment, we introduce a measure of misvaluation into

standard investment equations. Our estimates imply that a one-standard-deviation increase in

misvaluation increases investment between 20% to 60% relative to the mean level of investment

in the sample.

Keywords: investment, real effects of financial markets, misvaluation, stock market bubbles, fundamentals JEL codes: E44, E22, G3

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"Perhaps the crucial, and relatively neglected, issues have to do with real consequences of financial markets. ... Does market inefficiency have real consequences, or does it just lead to the redistribution of wealth from noise traders to arbitragers and firms?" (Shleifer 2000, p. 178)

Some observers believe that the 2001 U.S. recession was the culmination of a stock

market bubble that led to unusually high levels of business fixed investment in the late 1990s

(especially in the sectors of the economy that were most affected by the bubble) and the collapse

of investment as some firms attempted to reverse bubble-induced excesses. If correct, this

account has important implications for economic theory and public policy. In fact, there has

been a lively debate about the appropriate policy response to a possible bubble and, more

generally, the role of asset prices in policy formulation (see Bean 2004, Bernanke 2003,

Bernanke and Gertler 1999, Borio and White 2003, Cecchetti 2006, Dupor 2002, 2005, Gilchrist

and Leahy 2002, Hunter, Kaufman, and Pomerleano 2003, and references cited therein for a

discussion of these policy issues). This debate has been intensified by developments in

industrialized economies beginning in 2008. In this paper, we examine whether stock market

misvaluation affects business fixed investment.

For most economists, some skepticism seems appropriate about the idea that misvaluation

distorts investment. Before the late 1990s, many economists and financial economists doubted

that stock prices deviated much from fundamentals. Even if one is now prepared to recognize

that the shares of firms may sometimes be misvalued (see, among other studies, Hong,

Scheinkman, and Xiong 2008 and Shleifer 2000, and the papers collected in Thaler 2005), it is

far from obvious that misvaluation will have any meaningful effect on investment. The existing

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empirical evidence mirrors this ambiguity. Some papers have found evidence that misvaluation

affects investment, while others have not.1

We focus on the question of whether misvaluation affects investment. There are a variety

of theoretical contributions that are relevant to the question of whether misvaluation affects

investment (see the survey by Baker, Ruback, and Wurgler 2004). Stein (1996) provides a

leading theoretical model of how -- and under what circumstances -- misvaluation can affect

investment. In the Stein model, an overvalued firm uses a lower discount rate than it would in

the absence of misvaluation if the manager has a short horizon (e.g., due to short-term

shareholding) and an undervalued firm uses a higher discount rate if it faces a binding finance

constraint. Polk and Sapienza (2009) introduce a “catering” mechanism through which

misvaluation could affect investment. In their model, if there are times when the market

misprices firms based on their investment expenditures, managers may try to boost short-term

share prices by catering to this sentiment. Blanchard, Rhee, and Summers (1993) and Morck,

Shleifer, and Vishny (1990) suggest that firms may engage in financial arbitrage without letting

misvaluation affect investment. In contrast, in the De Long, Shleifer, Summers, and Waldmann

(1989) model, firms must precommit to their investment plans, and it is rational for managers to

let misvaluation influence investment. Baker, Stein, and Wurgler (2003) and Gilchrist,

Himmelberg, and Huberman (2005) also provide models in which rational managers may issue

equity and increase investment in response to overvaluation of their firm's shares. The link

between misvaluation and investment has also been discussed by Chirinko and Schaller (1996,

2001), Fischer and Merton (1984), Galeotti and Schiantarelli (1994), Panageas (2005) and, less

formally, by Bosworth (1975), and Keynes (1936), among others.

1 We review the empirical evidence in more detail in Section 3.

3

Before proceeding to our formal tests, we examine a general implication of models of

stock market misvaluation. If a firm is overvalued, it may be tempted to issue equity. In

contrast, if a firm is undervalued, it will be reluctant to issue equity. Do high stock price firms

issue more shares than low stock price firms? Using a large, unbalanced panel data set of almost

100,000 observations of U.S. firms over the period 1980-2004, we find that, for the median high

stock price firm, the ratio of new share issuance to investment spending or the capital stock is

substantially greater than for low stock price firms. The median low stock price firm issues no

shares. While the data on equity issuance is consistent with the idea that misvaluation exists and

affects investment, they do not provide definitive evidence.

To provide further evidence, we examine Q and Euler equations. If there is no

misvaluation in the stock market, the time t error terms in both equations should be orthogonal to

variables in the information set at time t-1 (under otherwise standard assumptions). If there is

misvaluation but it does not affect investment, it can be shown that misvaluation will enter the

error term of the Q equation. If there is misvaluation and it affects investment, misvaluation will

enter the error terms of both the Q and Euler equations. We use the Hansen (1982) and

Eichenbaum, Hansen, and Singleton (1988) tests of orthogonality conditions to determine which

of the three possible cases best describes the data: 1) no misvaluation; 2) misvaluation that does

not affect investment; or 3) misvaluation that does affect investment. Our tests are consistent

with case (3).

While the above qualitative evidence is suggestive of a role for misvaluation, it does not

address the question of how large an effect misvaluation has on investment. An additional

contribution of the paper is to directly estimate the effect of misvaluation on investment. We

estimate four standard investment equations -- a generic investment specification, the

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neoclassical model, the flexible accelerator model, and the marginal Q model. We also use

instrumental variables estimation to address concerns about measurement error in our

misvaluation variable. The estimates from each model suggest that misvaluation has a

statistically and economically significant effect on investment.

The paper is organized as follows. Section 1 describes the data and provides summary

statistics for the full sample. Section 2 evaluates the orthogonality conditions associated with the

Q and Euler equations. Section 3 presents quantitative estimates of the effect of misvaluation on

investment. Section 4 concludes and discusses the implications of the results.

1. DATA DESCRIPTION

1.1 Data sources and construction

This sub-section briefly describes the variables used in this study. Details concerning

data construction and sources are provided in the Appendix, which is included in the working

paper version of this paper available on the authors’ websites.

Nonfinancial variables are drawn from two sources. Nominal values of investment in

property, plant, and equipment (I$), sales (SALES$), and the cost of goods sold plus selling,

general, and administrative expenses (COST$) are based on data from CompuStat. Correspond-

ing real values (I, SALES, and COST) are defined by deflating I$ by the price of investment

goods (pI) and SALES$ and COST$ by the price of output (pY). These price deflators are based

on data from the Bureau of Economic Analysis. The real net capital stock (K) is calculated using

a standard perpetual inventory algorithm, which is described in detail in Section A of the

Appendix. These variables are analyzed as ratios -- I/K, SALES/K, and COST/K. There are a

few extreme outliers in the data. This is a common issue in panel data studies, resulting from

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mergers and other accounting changes. We use standard techniques to address the issue and trim

the sample by deleting the 1% tails of these ratios, as well as real sales growth.

Financial variables are drawn from three sources. Stock market returns are taken from

CRSP, stock market prices from CompuStat, and interest rates and risk factors from Kenneth

French’s website. We use a standard asset pricing model (the three-factor model of Fama and

French (1993)) to compute the equity risk premium (see Section B of the Appendix for details).

The sample period is 1980-2004. To minimize survivorship bias, we use unbalanced

panel data. Summary statistics for the main variables used in this paper are presented in Table 1a.

1.2 Equity finance

Overvaluation is associated with equity issuance in several models of market timing (e.g.,

Baker and Wurgler 2000, Lamont and Stein 2006, and references therein). In particular, the

Stein (1996) model identifies two relevant situations. When the manager has long horizons and

capital structure is not a binding constraint, the firm will issue shares when it is overvalued

(though overvaluation will not affect its investment decisions). However, if the manager has

short horizons, the overvalued firm will again issue equity, but, in contrast to the preceding

situation, the overvaluation will affect its discount rate (and therefore its investment). In either

case, overvaluation is linked to equity issuance, and it may therefore be of interest to begin our

analysis by examining the latter.

How much equity is issued by high stock price firms compared with low stock price

firms?2 Since firm size varies considerably, we need to normalize equity issuance. One natural

2 We categorize a firm as a high or low stock price firm in a given year using the price/sales ratio (i.e., the ratio of market value of equity to sales). Portfolios are formed by sorting all the firms for which the necessary data are available in a given year by the price/sales ratio. The two deciles with the highest price/sales ratio in a given year define high stock price firms. The next six deciles define "typical" firms and are not used in this analysis. The two deciles with the lowest price/sales ratio define low stock price firms. See Section C of the Appendix for further details.

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choice is investment spending, so we report new share issuance (NSI$) divided by nominal

investment spending. If a firm always financed current-year investment spending with current-

year new share issuance, we could interpret this ratio as the percent of investment spending

financed by new equity issuance. In practice, both debt and equity financing are fungible across

time, so it is safer to simply interpret the ratio as a measure of equity issuance (scaled by a

measure of the firm's size). As shown in row 1 of Table 1b, the ratio of new share issuance to

investment spending is about 0.75 for the median high stock price firm. In contrast, the ratio is

0.00 for the median low stock price firm. In row 2, we also report new share issuance normalized

by the nominal capital stock (another measure of firm size). The substantial contrast between

high and low stock price firms remains.

A similar result holds in the aggregate. In row 3 of Table 1b, we take the cross-sectional

mean of new share issues for all firms in a given category in a particular year and divide by the

cross-sectional mean of investment spending for all firms in the category in that year. (In contrast

to the entries in the previous two rows, this statistic does not give equal weight to the ratio for

each firm.) These computations generate a time series of 25 ratios for each category. The entry in

each row is the time series mean. For high stock price firms, the mean ratio is 0.56. For low

stock price firms, the ratio is 0.12. The difference is highly statistically significant; the t-statistic

(based on the 25 annual observations) is 6.23.

The evidence on equity issuance is interesting, but only suggestive about the relation

between misvaluation and investment. The next two sections examine whether misvaluation

affects investment.

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2. THE ORTHOGONALITY TESTS

Chirinko and Schaller (1996) develop a testing framework -- based on a formal

optimization problem and the associated orthogonality conditions -- that is designed to assess

whether misvaluation exists and, if so, whether it affects fixed investment. The essential insight

of the paper is that investment spending can be characterized by two different equations – the Q

and Euler equations. These equations differ in how they are affected by misvaluation.

Misvaluation -- and the distortions to investment decisions that may result -- will lead to a failure

of the orthogonality between the error terms and variables in the information set at time t-1. If

there is no misvaluation, both the Q and Euler equations will pass the specification test. If there

is misvaluation but it does not affect the firm’s discount rate, the Q equation will be rejected, but

the Euler equation will pass the specification test. If there is misvaluation and it affects the

firm’s discount rate, both the Q and Euler equations will be rejected.

The orthogonality tests in Chirinko and Schaller (1996) indicate that misvaluation exists

but does not affect investment, based on aggregate U.S. data. There are at least two reasons for

applying the Chirinko and Schaller (1996) to the panel data. First, the panel data used in the

current paper contains substantially more variation than aggregate data and may therefore lead to

more powerful tests. Second, some observers have suggested that substantial misvaluation in the

late 1990s affected investment. The Chirinko and Schaller (1996) data do not include the late

1990s, but data for these years are included in the panel dataset used in the current paper.

Our orthogonality tests are based on the following specifications of the Q and Euler

equations, which follow from the first-order conditions for optimal investment by a firm that

faces convex capital adjustment costs,

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, , ,MA Qi t i t i tQ MAC ε− = (1)

( ) ( ), , , 1 , 1 , 1 ,1 1 δ ε+ + +− + + + − = Ei t i t i t s t i t i tMIC r MRPK MIC , (2)

where ,MAi tQ is marginal Q (the expected present value of future marginal products of capital less

the relative price of investment goods, IYtp ) for firm i at time t, ,i tMAC is marginal adjustment

costs, ,i tMIC is marginal installation costs (the sum of ,i tMAC and IYtp ), ,i tr is the real, risk-

adjusted market discount rate, ,i tMRPK is the marginal revenue product of capital (including the

marginal effect of capital on adjustment costs), , 1+s tδ is the depreciation rate for sector s, and ,ε Qf t

and ,ε Ef t , are the disturbances for the Q and Euler Equations, respectively.3 Equation (1) cannot

be estimated because ,MAi tQ is not directly observable. However, Hayashi (1982) shows that, in

the absence of stock market misvaluation (along with other conditions), , ,=MA SMi t i tQ Q , where

,SMi tQ is stock market Q , the stock market value of the firm’s shares divided by the replacement

cost of the firm’s capital stock less IYtp .4

The Q and Euler equations are estimated by GMM. The tests are based on the

orthogonality conditions associated with the Q and Euler equations. We evaluate these

3 The Q and Euler equations displayed in equations (1) and (2) follow from a formal optimization problem defined in terms of three technologies: a production technology ( ( ),, 1 ,Π −K Li t i t , where ,Li t represents variable factors),

an adjustment cost technology ( ( ),, , 1−G I Ki t i t ), and a capital accumulation technology

( ( )1, , , , 1δ= + − −K I Ki t i t s t i t ). In terms of these technologies, the marginal revenue product of capital and the

marginal adjustment costs are written as ( ) ( ), ,, , 1 , , , 1= Π −− −MRPK K L G I Ki t K i t i t K i t i t and

( ),, , , 1= −MAC G I Ki t I i t i t , where the subscript denotes partial differentiation.

4 Our ,

SMi tQ variable is closely related to the eponymous Q variable introduced by Brainard and Tobin (1968) and

Tobin (1969).

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orthogonality conditions using specification tests based on the Hansen (1982) J statistic and the

testing procedure described by Eichenbaum, Hansen, and Singleton (1988).5 The null hypothesis

is orthogonality between instruments dated t-1 and the error terms of the Q and Euler equations.

In the first case, there is no misvaluation, so equations (1) and (2) describe the Q and Euler

equations. Under standard assumptions, the null hypothesis will not be rejected.

In the second case, there is misvaluation (,i t

M ) in the stock market, but the misvaluation

has no effect on the firm’s discount rate. In this case, , , ,= +SM MAi t i t i tQ Q M ,and

,i tM is added to both

sides of equation (1) to obtain

, , , ,SM Qi t i t i t i tQ MAC Mε− = + . (3)

The Euler equation is unchanged. The ,i t

M variable appears in the error term of the Q equation

but not the Euler equation, and the Q equation, but not the Euler equation, should fail the

specification test.

In the third case, there is misvaluation in the stock market and the misvaluation affects

the firm’s discount rate, so ,i tr is replaced by ( ), ,+i t i tr Mμ , where ( ),i tMμ is negative for

overvalued firms and positive for undervalued firms. The distortion in the discount rate affects

the present value of expected future marginal products of capital. We define ( ), ,λMi t i tM as the

5 The J statistic can be used to test the system of equations (Q and Euler). It is equal to the criterion for the estimated system of equations and is distributed 2χ with degrees of freedom equal to the number of orthogonality conditions, which is the number of instrumental variables times the number of equations, minus the number of estimated parameters. The Eichenbaum, Hansen, and Singleton (1988) test evaluates the validity of a subset of the orthogonality conditions. The test statistic is equal to the difference between the criterion for the system of equations (since both equations are valid under the null) and the criterion for the equation that is valid under the alternative hypothesis. Both estimates are based on elements of the weighting matrix for the overall system. The test statistic is distributed 2χ with degrees of freedom equal to the number of instruments minus the number of parameters that appear only in the equation that is invalid under the alternative hypothesis.

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wedge between the present value of future marginal products of capital evaluated at

( ), 1 ,+ +i t i tr Mμ and the present value evaluated at , 1+i tr . The Q and Euler equations become

( ), , , , , ,− = − +SM Q Mi t i t i t i t i t i tQ MAC M Mε λ , (4)

( ) ( ), , , 1 , 1 , 1 , , ,1 1 ( )δ ε μ+ + +− + + + − = +Ei t i t i t s t i t i t i t i tMIC r MRPK MIC M MIC . (5)

In this third case, ,i t

M appears in the error term of both the Q and Euler equations, and both

should fail the specification test.6

To complete our econometric specification, we make the following five assumptions.

First, we incorporate taxes by adjusting IYtp for investment tax credits and tax depreciation and

the MRPK and marginal adjustment costs for the corporate income tax rate. Second, we add the

parameter ψ to capture the effects of unmodeled factors that affect the discount rate and are

common to all firms. Third, the marginal adjustment cost function is specified as

( ), 1 , , 1* / −≡ +i t o i t i tMAC I Kα α . (6)

Fourth, the marginal revenue product of capital (including the marginal effect of capital on

adjustment costs) is specified as

( ) ( ) ( ), , , 1 , , 1 , , , 1* / / * /ζ − − −≡ − +i t i t i t i t i t i t i t i tMRPK SALES K COST K MAC I K , (7)

6 In equation (4), misvaluation affects the error term for the Q equation through two channels. First, because the firm uses a distorted discount rate, the present value of future marginal products of capital, evaluated at ( )r Mμ+ , is not the same as the usual marginal Q (in which the present value of future marginal products of capital is evaluated at r ). The term ( ), ,

Mi t i tMλ in equation (4) is equal to the wedge between the present value of future

marginal products of capital evaluated at distorted and non-distorted discount rates. Second, as in equation (2),

,i tM is added to both sides of the equation in order to obtain stock market Q on the left-hand side of the equation. See Section E of the Appendix for details. These two channels reflect different implications of misvaluation, and

( ), ,Mi t i tMλ and M do not cancel.

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where the ζ parameter captures deviations from constant returns to scale in production and

perfect competition in the product market. (When returns to scale are constant and markets are

perfectly competitive, 1=ζ .) Fifth, we use the following set of instruments:

1 , 1 , 2(1 )( / )τ − − −− t i t i tSALES K , 1 , 1 , 2(1 )( / )τ − − −− t i t i tI K , 1 , 1(1 )(1 )τ − −− +t i tr , 1 1 , 1(1 )− − −− − IYt t i titc z p , and

, 1−SMf tQ . These assumption lead to the following Q and Euler equations that are the basis for our

estimates,

, , ,SM Qi t i t i tQ MAC u− = , (8)

( ) ( ) ( ), , 1 , 1 , 1 , 1 ,1 1 1ψ τ δ+ + + +− + + + − + − = Ei t i t t i t i t i t i tMIC r MRPK MIC u , (9a)

( ) ( ), ,1 1τ≡ − + − − IYi t t i t t t tMIC MAC itc z p , (9b)

where itct is the investment tax credit rate, zt is the present value of depreciation allowances per

dollar of investment spending, tτ is the marginal corporate income tax rate, and ,Qi tu and ,

Ei tu are

the disturbance terms. These terms are key to our orthogonality tests, and they differ depending

on how misvaluation affects the firm:

Case 1: { }, , , ,;= =Q Q E Ei t i t i t i tu uε ε ,

Case 2: { }, , , , ,;ε ε= + =Q Q E Ei t i t i t i t i tu M u , (10)

Case 3: ( ){ }, , , , , , , , ,; ( )= − + = +Q Q M E Ei t i t i t i t i t i t i t i t i tu M M u M MICε λ ε μ .

Table 2 presents joint estimates of the Q and Euler equations for the full sample. Both

the Q and Euler equations are rejected. The parameter estimates are reasonable. For example,

both the marginal adjustment cost and the curvature of the adjustment cost function are positive.

The parameter ζ , which reflects returns to scale and product market competition, is consistent

with a modest amount of decreasing returns to scale and/or imperfect competition.

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Chirinko and Schaller (1996), who work with aggregate U.S. data, find that the Q

equation is rejected, but their data fail to reject the Euler equation. Some observers have argued

that there was substantial misvaluation during the late 1990s and that this affected investment.

One possible explanation for the difference in the results is the inclusion of the late 1990s in the

sample used in the current paper. To check this possibility, we re-estimate the Q and Euler

equations without the years 1996 through 2000 (inclusive). The test statistics decline, but both

the Q and Euler equations are still rejected. The finding -- that misvaluation affects investment --

is not driven by the data for the late 1990s.

3. HOW LARGE AN EFFECT OF MISVALUATION ON INVESTMENT?

The evidence in preceding sections is qualitative in nature with respect to investment

spending. In this section, we provide quantitative estimates of the effect of misvaluation on

investment. Our measure of misvaluation is Stock Market Q minus Fundamental Q (both

measured at the beginning of the period). Stock Market Q is the stock market value of the firm’s

shares divided by the replacement cost of the firm’s capital stock (less the relative price of

investment goods); Fundamental Q is the expected present value of future marginal products of

capital (less the relative price of investment goods). Expected future values are defined by a

vector autoregression containing the marginal product of capital and other variables influencing

investment decisions (see Abel and Blanchard 1986, Gilchrist and Himmelberg 1995, and

Section F of the Appendix for details).

A number of earlier empirical studies have examined the relationship between stock

market misvaluation and investment, and conclusions vary widely. Using aggregate U.S. data,

Blanchard, Rhee, and Summers (1993) estimate a model containing Stock Market Q and

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Fundamental Q. In such a specification, the coefficient on Stock Market Q is interpreted as the

effect of misvaluation (or non-fundamentals) on investment. Both Qs are statistically significant,

but, based on economic significance of the coefficients and other considerations, they conclude

that misvaluation has little effect on investment. Also using U.S. aggregate data, Chirinko and

Schaller (1996) find no evidence that misvaluation affects investment. In contrast, Galeotti and

Schiantarelli (1994) using aggregate U.S. data and Chirinko and Schaller (2001) using aggregate

Japanese data find that misvaluation has a significant effect on investment. Based on firm-level

U.S. data, Morck, Shleifer, and Vishny (1990) find that movements in relative share prices are

associated with statistically significant investment changes, but they conclude that misvaluation

has a minor impact on investment because of low incremental 2R s. Baker, Stein, and Wurgler

(2003) find that the investment of equity-dependent firms is relatively more sensitive to Stock

Market Q; their results suggest a role for misvaluation in affecting investment. Gilchrist,

Himmelberg, and Huberman (2005) find that shocks to dispersion in analysts' forecasts (a proxy

for misvaluation) affect investment. Polk and Sapienza (2009) report that their key proxy for

misvaluation, discretionary accruals, affects investment after controlling for fundamentals. In

contrast to these recent studies, Bond and Cummins (2001) and Cummins, Hassett, and Oliner

(2006) find a statistically weak effect of Stock Market Q, after controlling for fundamentals

using analysts' forecasts. Bakke and Whited (2010) take a different approach to separating

fundamentals and non-fundamentals. Using a measurement-error-consistent estimator, they find

that the non-fundamental information in stock prices (misvaluation) only influences small firms

that have low levels of market mispricing and rely on equity finance.

In standard models of investment, key variables determining investment are the interest

rate, the relative price of investment goods, and sales. Table 3 presents a generic investment

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specification in which the investment/capital ratio (I/K) is regressed on misvaluation and the

lagged percentage changes in real sales, the relative price of investment goods, and the interest

rate for the full sample of observations for which all the necessary data are available.7 The

coefficient on misvaluation in the generic investment specification is positive and highly

significant. The coefficient estimate of 0.0031 implies that a one-standard-deviation increase in

misvaluation increases I/K by 0.039 (about 22% of the mean I/K of 0.174).

Much recent research has suggested that investment is sensitive to cash flow. A leading

interpretation is that cash flow enters due to finance constraints, as suggested by Fazzari,

Hubbard, and Petersen (1988). (This interpretation has been contested by, among others, Abel

and Eberly 2003, Gomes 2001, Hennessy and Whited 2007, and Kaplan and Zingales 1997,

2000. See Fazzari, Hubbard, and Petersen 2000 for a reply to the Kaplan and Zingales critique

and Hubbard 1998 and Schiantarelli 1995 for surveys.) In the second column of Table 3, we

estimate a similar specification, this time including the ratio of cash flow to the capital stock.

Including cash flow has little effect on the misvaluation coefficient.

The neoclassical investment model (Jorgenson 1963, Eisner and Nadiri 1968, and Hall

and Jorgenson 1971) suggests a specification in which investment is regressed on distributed lags

of the percentage change in sales and the user cost of capital (see Section G of the Appendix for

details of the calculation of user cost). In Table 4, we add misvaluation to a neoclassical

investment specification. The coefficient on misvaluation is slightly smaller than the coefficient

7 The regression includes both fixed firm effects and time effects. The inclusion of fixed effects raises an econometric issue. Sufficiently high serial correlation of the errors may lead to biased estimates of the coefficient on the misvaluation term with fixed effects estimation. Since the residual serial correlation coefficient (about 0.25 in Tables 3 to 5) is approximately equal to the coefficient on the lagged dependent variable, we can use the simulation results of Judson and Owen (1999, Table 1) to evaluate coefficient bias. For T equal to five (about the average number of observations per firm in our sample), the bias in the coefficient on the regressor is less than 1%.

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in the generic investment specification but again highly significant. Including cash flow in the

specification has little effect on the misvaluation coefficient.

The flexible accelerator model is similar to the neoclassical model except that the cost-of-

capital terms are omitted. As columns 3 and 4 of Table 4 show, misvaluation also has an

economically and statistically significant effect on investment in the flexible accelerator model.

Finally, we estimate a Q model of investment. A conceptual advantage of the Q model is

that Q, unlike the variables that appear in the generic, neoclassical, or flexible accelerator

specifications, is explicitly forward-looking. A potential problem with the Q model is that Stock

Market Q will be affected by any misvaluation in the stock market.8 To avoid this problem, we

use Fundamental Q in the regression. Like Stock Market Q, Fundamental Q reflects expectations

of future discount rates and future marginal products of capital. The coefficients on misvaluation

in the Q specification presented in Table 5 are statistically significant and are economically close

to the estimated coefficients in the prior investment specifications.

As a robustness check, we also consider instrumental variable estimation of the

specification in Table 5. There are two motivations for IV estimation. The first is the possibility

that M (misvaluation) might inadvertently pick up the effect of fundamentals (despite the

inclusion of FQ in the regression). The second is the possibility of measurement error in M that

could bias the coefficient on M toward 0. An instrument for M should have two

characteristics: 1) orthogonal to fundamentals (because our measure of fundamentals may not

capture all of the variation in true fundamentals); and 2) correlated with misvaluation.

8 Erickson and Whited (2000) provide evidence of the importance of measurement error in Stock Market Q and cite misvaluation as a possible source of the measurement error.

16

Our starting point is Baker and Wurgler (2007), who propose a measure of investor

sentiment and show that it has predictive power for future returns.9 Baker and Wurgler (2007)

combine six time series variables that, on the basis of prior research, have each shown some

ability to capture investor sentiment – the closed-end fund discount (CEFD), the natural log of

the raw turnover ratio, detrended by a five-year moving average (TURN), the number of IPOs

(NIPO), average first-day IPO returns (RIPO), the dividend premium, the log difference between

the average market-to-book ratios of dividend-paying and non-dividend-paying firms (PDND),

and the equity share of new issues, defined as gross equity issuance divided by gross equity

issuance plus gross long-term debt issuance (SHARE). In order to capture the component of

these variables that is not readily explained by business cycle variation, each of these six

variables is regressed on the industrial production index, growth in consumer durables, non-

durables, and services, and a dummy variable for NBER recessions. The resulting sentiment

index ( tS ) is

12

12 12

0.23 0.23 0.24

0.29 0.32 0.23 ,t t t t

t t t

S CEFD TURN NIPO

RIPO PDND SHARE

⊥ ⊥ ⊥−

⊥ ⊥ ⊥− −

= − + +

+ − + (11)

where the superscript ⊥ on, e.g., CEFD, denotes the residual from the regression of CEFD on

the business cycle variables. (Turnover, average first-day IPO returns, and the dividend premium

are lagged 12 months.) The coefficients on the variables are chosen so that the sentiment index

represents the first principal component of this set of time series variables.

We begin by regressing firm-level stock market returns on fundamental risk factors and

sentiment:

β β β= + + +, 0

F Si t i t i t itR F S e , (12)

9 See also Baker and Wurgler (2006). We focus on Baker and Wurgler (2007) because they provide a sentiment index at monthly frequency, which we use in the regression in equation (12).

17

where ,i tR is excess returns on the stock of firm i at time t,

0β is the constant,

tF is a vector of

fundamental risk factors, Fi

β is a vector of factor loadings (betas) on the fundamental risk

factors, tS is the Baker-Wurgler measure of investor sentiment, S

iβ is firm i's sensitivity to

sentiment, and ,i te is the error term in the regression. Excess returns are equal to returns minus

the risk-free rate. We use a standard asset pricing model (the Fama-French three-factor model)

for tF (see Section B of the Appendix for details).

Equation (12) makes it possible to decompose returns into the part that is due to investor

sentiment ( =,S Si t i tR Sβ ) and the part that is due to fundamental risk factors ( = −

, ,F Si t i t i tR R Sβ ).

The third and fourth columns of Table 5 report IV estimates, using SitR as an instrument for

,i tM .

Before discussing the estimates, we note that the F statistic for the first stage regression of ,i t

M

on SitR is 229.7, so the econometric problems associated with weak instruments are not an issue

here. The point estimates of the coefficients on Fundamental Q (and cash flow) are similar to the

OLS estimates. According to the IV estimates, misvaluation has a statistically significant effect

on investment. The IV point estimates of the effect of misvaluation are larger than the OLS

estimates.10

4. CONCLUSION AND IMPLICATIONS

This paper evaluates whether investment is affected by stock market misvaluation. The

orthogonality tests reject the null hypothesis that investment is unaffected by misvaluation.

10 Since instrumental variables estimation is one of the leading econometric techniques for dealing with measurement error (and classical measurement error biases a coefficient toward 0), the IV point estimate is consistent with downward bias in the OLS estimate of the effect of misvaluation.

18

Given this qualitative evidence suggesting an important role for misvaluation, we

estimate its quantitative effect on investment using standard investment equations. We estimate

four types of investment equations – generic, neoclassical, accelerator, and Q. Both the

fundamental variables (such as user cost and marginal Q) and the misvaluation variable have

highly significant effects on investment. OLS estimates of the coefficients are robust across the

four investment models and imply that a one-standard-deviation increase in misvaluation

increases investment by more than 20% relative to the mean level of investment in the sample.

We also use instrumental variables estimation to address concerns about measurement

error in the misvaluation variable. Our IV approach has two advantages. First, we design an

instrument that reflects investor sentiment and is orthogonal to the fundamental component of

stock market returns. Second, it is inherently difficult to measure misvaluation precisely, but the

classical econometric analysis shows that the coefficient estimate on an imprecisely measured

variable can suffer from attenuation bias (i.e., bias toward zero). The IV estimate of the effect of

misvaluation on investment is statistically significant and larger than the OLS estimate; a one-

standard-deviation increase in misvaluation increases investment by more than 60% relative to

the mean level of investment in the sample.

Recognizing the possibility that misvaluation affects investment opens up interesting

research questions. How much distortion in capital allocation results from misvaluation? Are

there institutional changes that would make misvaluation less likely? Are there institutional or

policy changes that could decrease the misallocation of capital induced by misvaluation? One

example of such an institutional change would be greater transparency, perhaps through reform

of accounting procedures. Blanchard and Watson (1982) and Baker and Wurgler (2006), among

others, argue that misvaluation is more likely when agents do not know economic

19

fundamentals.11 Another example is reforms that would reduce conflicts of interest that might

tempt financial intermediaries to misleadingly promote securities. Many have alleged that

opacity and conflicts of interest with housing assets are at the heart of the recent financial crisis.

There are also implications for how we understand the economics of financial markets,

growth, and economic fluctuations. Cochrane (1994) argues that is hard to account for

macroeconomic fluctuations with conventional monetary and technology shocks. The evidence

presented in this paper suggests that misvaluation shocks have an effect on real variables. Can

misvaluation shocks help to account for aggregate economic fluctuations? Are there other

channels through which misvaluation shocks significantly affect real variables (e.g., stock price

or housing price misvaluations affecting consumption; commercial property misvaluations

affecting investment)? Have misvaluation shocks played a significant role in noteworthy

macroeconomic episodes, such as the Great Depression or the late 1980s boom in Japan and the

subsequent decade of stagnation?

There are important policy implications of evidence that misvaluation has real

consequences. Central banks may need to pay some attention to possible stock market

misvaluation. Misvaluation shocks affect asset prices but may have relatively little impact on

broad measures of inflation. If overvaluation shocks lead to overinvestment and subsequent,

possibly dramatic, retrenchment, central banks may need to move beyond Taylor rules (which

treat inflation and unemployment as the only legitimate inputs for monetary policy rules).

Tax policy may also have a role to play if misvaluation distorts the efficient allocation of

capital. Both informal accounts and sophisticated recent asset pricing models suggest that the

prospect of capital gains plays a key role in driving misvaluation. Agents may buy assets not for

11 Consistent with this, there is evidence in Polk and Sapienza (2009) that misvaluation has a larger effect on investment for opaque firms.

20

their intrinsic value but because they believe that they will be able to resell the asset at a still

higher price to some other agent (e.g., Scheinkman and Xiong 2003). Stiglitz (2003) argues that

a relatively simple way to defuse this sort of misvaluation is by increasing the capital gains tax

rate and thus reducing the speculative motive for asset acquisition. A securities transaction tax

has also been proposed (Stiglitz, 1989; Summers and Summers, 1989).

Simply recognizing the possibility of misvaluation -- and that misvaluation can have real

consequences -- might conceivably lead to more prudent fiscal decisions. Projections of tax

revenues based on periods of significant misvaluation (and the associated increase in real

activity) may be misleading and may result in subsequent fiscal imbalances.

A very large question is whether overvaluation can sometimes play a positive role in

attenuating finance constraints and fostering economic growth. Caballero, Fahri, and Hammour

(2006), for example, discuss the possibility that speculative episodes may be associated with a

relaxation of finance constraints. Jermann and Quadrini (2007) provide a model in which small

firms are finance constrained. Overvaluation lowers the cost of finance and relaxes finance

constraints for these firms, leading to more investment and the reallocation of capital and labor to

constrained firms. The result is an increase in productivity. Much earlier, in a discussion of the

1920s, Keynes (1931) expressed similar views: "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 quinquennium from 1925 to 1929." Can

we measure the extent to which overvaluation reduces finance constraints? If we could, it might

be possible to more carefully investigate possible trade-offs between the advantages of

overvaluation (fostering growth) and the disadvantages (misallocation of capital, potential for a

subsequent crash and resulting recession or depression).

21

The evidence presented in this paper suggests that the questions raised here are relevant

for developed economies with strong capital markets (such as the United States). Arguably, they

are even more important for developing economies with relatively weak capital markets.

22

References Abel, Andrew B., and Blanchard, Olivier J. (1986) "The Present Value of Profits and Cyclical Movements in Investment." Econometrica, 54, 249-274. Abel, Andrew B., and Eberly, Janice C. (2003) "Q Theory without Adjustment Costs & Cash Flow Effects without Financing Constraints." Wharton and Northwestern.

Baker, Malcolm, Ruback, Richard S., and Wurgler, Jeffrey. (2004) "Behavioral Corporate Finance: A Survey," in Espen Eckbo (ed.), Handbook of Corporate Finance: Empirical Corporate Finance. Amsterdam: Elsevier, 145-185. Baker, Malcolm, Stein, Jeremy C., and Wurgler, Jeffrey. (2003) "When Does The Market Matter? Stock Prices and the Investment of Equity-Dependent Firms." Quarterly Journal of Economics, 118, 969-1005. Baker, Malcolm, and Wurgler, Jeffrey. (2000) “The Equity Share In New Issues and Aggregate Stock Returns.” Journal of Finance, 55, 2219-2257. Baker, Malcolm, and Wurgler, Jeffrey. (2006) “Investor Sentiment and the Cross-Section of Stock Returns.” Journal of Finance, 61, 1645-1680. Baker, Malcolm, and Jeffrey Wurgler. (2007) "Investor Sentiment in the Stock Market." Journal of Economic Perspectives, 21, 129-151. Bakke, Tor-Erik, and Whited, Toni M. (2010) “Which Firms Follow the Market?: An Analysis of Corporate Investment Decisions.” Review of Financial Studies, 23, 1941-1980. Bean, Charles R. (2004) “Asset Prices, Financial Instability, and Monetary Policy,” American Economic Review, 94, 14-18. Bernanke, Ben S. (2003) “Monetary Policy and the Stock Market: Some Empirical Results.” Banking and Finance Lecture, Widener University. Bernanke, Ben, and Gertler, Mark. (1999) “Monetary Policy and Asset Price Volatility,” in Federal Reserve Bank of Kansas City (ed.), New Challenges For Monetary Policy. Kansas City: Federal Reserve Bank of Kansas City 77-128. Blanchard, Olivier J., Rhee, Changyong, and Summers, Lawrence H. (1993) "The Stock Market, Profit and Investment." Quarterly Journal of Economics, 108, 115-136.

Blanchard, Olivier J., and Watson, Mark. (1982) "Bubbles, Rational Expectations and Financial Markets," in Paul Wachtel (ed.), Crises in the Economic and Financial Structure. Lexington, MA: Lexington Books, 295-315.

23

Bond, Stephen R., and Cummins, Jason G. (2001) "Noisy Share Prices and the Q Model of Investment.," Oxford. Borio, Claudio, and White, William R. (2003) "Whither Monetary and Financial Stability? The Implications of Evolving Policy Regimes," in Federal Reserve Bank of Kansas City (ed.), Monetary Policy and Uncertainty: Adapting to a Changing Economy. Kansas City: Federal Reserve Bank of Kansas City, 131-211. Bosworth, Barry. (1975) "The Stock Market and the Economy." Brookings Papers on Economic Activity 1, 257-290. Brainard, William C., and Tobin, James. (1968) "Pitfalls in Financial Model Building," American Economic Review, 58, 99-122.

Caballero, Ricardo J., Farhi, Emmanuel, and Hammour, Mohamad L. (2006) “Speculative Growth: Hints from the US Economy.” American Economic Review, 96, 1159-1192.

Cecchetti, Stephen G. (2006) “The Brave New World of Central Banking: Policy Challenges Posed by Asset Price Booms and Busts.” Economic Review of the National Institute of Economic and Social Research, 196, 107-119. Chirinko, Robert S., and Schaller, Huntley. (1996) “Bubbles, Fundamentals, and Investment: A Multiple Equation Testing Strategy.” Journal of Monetary Economics, 38, 47-76. Chirinko, Robert S., and Schaller, Huntley. (2001). "Business Fixed Investment and 'Bubbles': The Japanese Case," American Economic Review, 91, 663-680. Cochrane, John H. (1994) "Shocks." Carnegie-Rochester Conference Series on Public Policy, 41, 295-364. Cummins, Jason G., Hassett, Kevin A., and Oliner, Stephen D. (2006) “Investment Behavior, Observable Expectations, and Internal Funds.” American Economic Review, 96, 796-810. De Long, J. Bradford, Shleifer, Andrei, Summers, Lawrence H., and Waldmann, Robert J. (1989) "The Size and Incidence of the Losses from Noise Trading." Journal of Finance, 44, 681-696. Dupor, Bill, (2002) “The Natural Rate of Q.” American Economic Review, 92, 96-101. Dupor, Bill. (2005) “Stabilizing Non-Fundamental Asset Price Movements under Discretion and Limited Information.” Journal of Monetary Economics, 52, 727-747. Eichenbaum, Martin S., Hansen, Lars Peter, and Singleton, Kenneth J. (1988) "A Time Series Analysis of Representative Agent Models of Consumption and Leisure Choice Under Uncertainty." Quarterly Journal of Economics, 103, 51-78.

24

Eisner, Robert, and Nadiri, M. Ishaq. (1968) "Investment Behavior and Neo-Classical Theory." Review of Economics and Statistics, 50, 369-382. Erickson, Timothy, and Whited, Toni M. (2000) “Measurement Error and the Relationship between Investment and q.” Journal of Political Economy, 108, 1027-1057. Fama, Eugene F., and French, Kenneth R. (1993) "Common risk factors in the returns on stocks and bonds." Journal of Financial Economics, 33, 3-56. Fazzari, Steven M., Hubbard, R. Glenn, and Petersen, Bruce C.. (1988) "Financing Constraints and Corporate Investment.” Brookings Papers on Economic Activity, No. 1, 141-195. Fazzari, Steven M., Hubbard, R. Glenn, and Petersen, Bruce C. (2000) "Investment-Cash Flow Sensitivities Are Useful: A Comment on Kaplan and Zingales." Quarterly Journal of Economics, 115, 695-705. Fischer, Stanley, and Robert C. Merton. (1984) "Macroeconomics and Finance: The Role of the Stock Market." Carnegie-Rochester Conference Series on Public Policy, 21, 57-108. Galeotti, Marzio, and Schiantarelli, Fabio. (1994) "Stock Market Volatility and Investment: Do Only Fundamentals Matter?" Economica, 61, 147-165. Gilchrist, Simon, and Himmelberg, Charles. (1995) "Evidence on the Role of Cash Flow for Investment." Journal of Monetary Economics, 36, 541-572. Gilchrist, Simon, Himmelberg, Charles, Huberman, Gur. (2005) “Do Stock Price Bubbles Influence Corporate Investment?” Journal of Monetary Economics, 52 , 805-827. Gilchrist, Simon, and Leahy, John V. (2002) "Monetary Policy and Asset Prices." Journal of Monetary Economics, 49, 75-97. Gomes, Joao F. (2001) "Financing Investment." American Economic Review, 91, 1263-1285. Hall, Robert E., and Jorgenson, Dale W. (1971) "Application of the Theory of Optimum Capital Accumulation," in Gary Fromm (ed.), Tax Incentives and Capital Spending. Washington: Brookings Institution, 9-60. Hansen, Lars Peter. (1982) "Large Sample Properties of Generalized Method of Moments Estimators." Econometrica, 50, 1029-1054. Hayashi, Fumio. (1982) "Tobin's Marginal q and Average q: A Neoclassical Interpretation." Econometrica, 50, 213-224.

25

Hennessy, Christopher A., and Whited, Toni M. (2007) “How Costly Is External Financing? Evidence from a Structural Estimation.” The Journal of Finance, 62, 1705-1745. Hong, Harrison, Scheinkman, José, and Xiong, Wei. (2008) “Advisors and Asset Prices: A Model of the Origin of Bubbles.” Journal of Financial Economics, 89, 268-287. Hubbard, R. Glenn. (1998) "Capital-Market Imperfections and Investment." Journal of Economic Literature, 36, 193-225. Hunter, William C., Kaufman, George G., and Pomerleano, Michael (eds.). (2003) Asset Price Bubbles: The Implications for Monetary, Regulatory, and International Policies. Cambridge: MIT Press. Jermann, Urban J., and Quadrini, Vincenzo. (2007) “Stock market boom and the productivity gains of the 1990s.” Journal of Monetary Economics, 54, 413-432. Jorgenson, Dale W. (1963) "Capital Theory and Investment Behavior." American Economic Review, 53, 247-259; reprinted in (1996) Investment, Volume 1: Capital Theory and Investment Behavior. Cambridge: MIT Press, 1-16. Judson, Ruth A. and Owen, Ann L. (1999) “Estimating Dynamic Panel Data Models: A Guide for Macroeconomists.” Economics Letters, 65, 9-15. Kaplan, Steven N., and Zingales, Luigi. (1997) "Do Investment-Cash Flow Sensitivities Provide Useful Measures of Finance Constraints?" Quarterly Journal of Economics, 112, 169-215. Kaplan, Steven N., and Zingales, Luigi. (2000) "Investment-Cash Flow Sensitivities Are Not Valid Measures of Financing Constraints." Quarterly Journal of Economics, 115, 707-712. Keynes, J. M. (1931) “An economic analysis of unemployment,” In Collected Writings, Volume XII. London: Macmillan. Keynes, J. M. (1936) The General Theory of Employment, Interest, and Money. London: Macmillan. Lamont, Owen, and Stein, Jeremy C. (2006) “Investor Sentiment and Corporate Finance: Micro and Macro.” American Economic Review, 96, 147-151. Morck, Randall, Andrei Shleifer, and Robert W. Vishny. (1990) "The Stock Market and Investment: Is the Market a Sideshow?" Brookings Papers on Economic Activity, No. 2, 157-202. Panageas, Stavros. (2005) “The Neoclassical Theory of Investment in Speculative Markets.” Booth School.

26

Polk, Christopher, and Sapienza, Paola. (2009) "The Stock Market and Corporate Investment: a Test of Catering Theory." Review of Financial Studies, 22, 187-217. Scheinkman, Jose A., and Xiong, Wei (2003) "Overconfidence and Speculative Bubbles." Journal of Political Economy, 111, 1183-1219. Schiantarelli, Fabio. (1995) “Financial Constraints and Investment: A Critical Review of Methodological Issues and International Evidence,” in Joe Peek and Eric S. Rosengren (eds.), Is Bank Lending Important For The Transmission of Monetary Policy? Boston: Federal Reserve Bank of Boston, 177-214. Shleifer, Andrei. (2000) Inefficient Markets: An Introduction To Behavioral Finance. Oxford: Oxford University Press.

Stein, Jeremy. (1996) “Rational capital budgeting in an irrational world.” Journal of Business, 69, 429-55. Stiglitz, Joseph E. (1989). “Using Tax Policy to Curb Speculative Short-Term Trading.” Journal of Financial Services Research, 3, 101-115. Stiglitz, Joseph E. (2003) The Roaring Nineties: A New History of the World's Most Prosperous Decade. New York: W.W. Norton. Summers, Lawrence H., and Summers, Victoria, P. (1989) “When Financial Markets Work Too Well: A Cautious Case for a Securities Transactions Tax.” Journal of Financial Services Research, 3, 261-286. Thaler, Richard H. (ed.). (2005) Advances in Behavioral Finance Volume II. New York and Princeton: Russell Sage Foundation and Princeton University Press. Tobin, James. (1969) "A General Equilibrium Approach to Monetary Theory." Journal of Money, Credit, and Banking, 1, 15-29.

27

______________________________________________________________________________ TABLE 1a SUMMARY STATISTICS FOR THE FULL SAMPLE

==================================================================== Panel A: Statistics for the Full Sample

N Mean 25% 50% 75% Standard

Deviation Skewness Kurtosis

(1) (2)

(3) (4) (5) (6) (7) (8)

I 116952 130.664 1.236 6.947 43.584 695.395 23.742 1275.623K 116952 5005.512 12.160 62.960 469.226 31423.724 21.450 851.760

I/K 116952 0.174 0.043 0.106 0.214 0.213 2.717 9.373 Sales/K 97915 3.902 0.807 2.307 4.865 5.064 2.932 11.376 Cost/K 97915 3.643 0.753 2.154 4.542 4.758 2.973 11.800 NSI$/I$ 114718 20.828 0.000 0.246 0.475 159.012 42.206 3047.134

R 71968 0.151 -0.256 0.039 0.357 0.826 8.564 194.038 C 114356 0.233 0.167 0.210 0.274 0.098 1.640 4.246

NOTES: I is investment in millions of 1996 dollars. K is the replacement value of the capital stock in 1996 dollars. Sales/K is the ratio of real sales to K. Cost/K is the ratio of the real cost of goods sold to K. NSI$/I$ is new share issues scaled by nominal investment spending. R is nominal annual stock market returns. C is the user cost of capital. N is the number of firm/year observations. Details concerning data construction and sources are provided in Section 1.1 and the Appendix.

28

______________________________________________________________________________ TABLE 1b New Share Issues by Category ====================================================================

High Stock

Price Low Stock

Price Difference Test

Statistic [p-value]

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

Median Firm (normalized by investment)

0.7534 0.0000 0.7534 91.15 [0.000]

Median Firm (normalized by the

capital stock)

0.1220 0.0000 0.1220 93.90 [0.000]

Aggregated (standard deviation)

0.5556 (0.3460)

0.1194 (0.0544)

0.4362 (0.2916)

6.23 [0.000]

NOTES: The entries in rows 1 and 2 are based on the median values of new share issues (normalized by investment spending in row 1 and the capital stock in row 2) per firm per year in a given category. The test statistic (column 4) for the difference in medians is a nonparametric test based on the analysis of variance on ranks. The entries in row 3 are based on the mean values of the cross-section average of new share issues divided by the cross-section average of investment spending, where the cross-section averages are computed for all firms in a given category in a particular year. The test statistic for the aggregated variable is a t-test based on 25 annual observations for each category (1980-2004). We categorize a firm as a high or low stock price firm in a given year using the price/sales ratio (i.e., the ratio of market value of equity to sales). The two deciles with the highest price/sales ratio in a given year define high stock price firms (column 1). The two deciles with the lowest price/sales ratio define low stock price firms (column 2). See the Appendix (especially Sections C and D) for details of variable definitions.

29

______________________________________________________________________________ TABLE 2 ORTHOGONALITY TESTS ====================================================================

, , ,SM Qi t i t i tQ MAC u− = ,

( ) ( ) ( ), , 1 , 1 , 1 , 1 ,1 1 1ψ τ δ+ + + +− + + + − + − = Ei t i t t i t i t i t i tMIC r MRPK MIC u ,

-----------------------------------------------------------------------------------

( ) ( ), ,1 1≡ − + − − IYi t t i t t t tMIC MAC itc z pτ ,

( ), 1 , , 1* / −≡ +i t o i t i tMAC I Kα α

( ) ( ) ( ), , , 1 , , 1 , , , 1* / / * /− − −≡ − +i t i t i t i t i t i t i t i tMRPK SALES K COST K MAC I Kζ

Parameters and Statistics

Full Sample Excluding Late 90s

(1) (2)

Ψ

0.057

(0.019) 0.085

(0.023) ζ 0.945

(0.021) 0.987

(0.018) α0 -1.680

(0.228) -0.912 (0.269)

α1 28.715 (1.744)

18.310 (2.308)

MAC 1.114 0.673

Curvature

0.377 0.251

N 40527 29799 Test System 208.882

[0.000] 189.933 [0.000]

Test Q 208.787 [0.000]

189.743 [0.000]

Test Euler 24.410 [0.000]

20.105 [0.000]

30

NOTES: Variable definitions are provided in Section 2 and the Appendix. The first four rows report the point estimates and corresponding standard errors for the listed parameters. MAC is the marginal adjustment cost and Curvature is the curvature of the adjustment cost function, both evaluated at the medians of I/K and K, respectively. N is the number of observations. “Test System” is the Hansen (1982) J test of overidentifying restrictions. “Test Q” and “Test Euler” are the Eichenbaum, Hansen, and Singleton (1988) tests for the validity of the Q and Euler equations, respectively. See Chirinko and Schaller (1996) for further discussion of these tests.

31

______________________________________________________________________________

TABLE 3 QUANTITATIVE ESTIMATES OF THE EFFECT OF MISVALUATION ON INVESTMENT GENERIC INVESTMENT SPECIFICATION

(1)

(2)

Misvaluation

0.0031 (0.0001) [42.35]

0.0031 (0.0001) [42.82]

Sales

0.1022 (0.0021) [48.39]

0.1011 (0.0021) [47.92]

Relative Price of Investment Goods

-0.1059 (0.0064) [-16.68]

-0.1058 (0.0063) [-16.70]

Interest rate

-0.0021 (0.0011) [-1.95]

-0.0021 (0.0011) [-1.91]

Cash Flow

0.0101 (0.0064) [15.77]

Number of Observations Number of Firms R2

58260 8340

0.5087

58192 8335

0.5111

NOTES: Each cell shows the point estimate, standard error (in parenthesis), and t statistic (in brackets). Sales, the relative price of investment goods, and the interest rate enter as lagged percentage changes. Cash flow is the ratio of cash flow to the capital stock (beginning of period). Misvaluation is the difference between Stock Market Q and Fundamental Q (both beginning of period), as defined in the text. The regressions include both firm and year effects (whose explanatory power is included in the numerator of R2). Variable definitions are provided in Section 1 and the Appendix.

32

TABLE 4 QUANTITATIVE ESTIMATES OF THE EFFECT OF MISVALUATION ON INVESTMENT NEOCLASSICAL AND ACCELERATOR SPECIFICATIONS =====================================================================

Neoclassical Accelerator

(1)

(2)

(3)

(4)

Misvaluation 0.0026

(0.0001) [33.30]

0.0026 (0.0001) [33.34]

0.0026 (0.0001) [33.51]

0.0026 (0.0001) [33.55]

Sales t 0.1101 (0.0022) [49.75]

0.1079 (0.0022) [48.34]

0.1072 (0.0022) [49.05]

0.1050 (0.0022) [47.65]

Sales t – 1 0.0928 (0.0022) [42.87]

0.0925 (0.0022) [42.70]

0.0874 (0.0021) [40.99]

0.0871 (0.0021) [40.82]

Sales t – 2 0.0530 (0.0020) [26.37]

0.0529 (0.0020) [26.27]

0.0519 (0.0020) [25.82]

0.0518 (0.0020) [25.73]

Cost of Capital t -0.0313 (0.0031) [-9.94]

-0.0310 (0.0032) [-9.82]

Cost of Capital t – 1 -0.0483 (0.0033) [-14.58]

-0.0482 (0.0033) [-14.55]

Cost of Capital t – 2 -0.0132 (0.0012) [-10.94]

-0.0132 (0.0012) [-10.93]

Cash Flow 0.0041 (0.0006)

[6.38]

0.0042 (0.0006)

[6.54] Number of Observations Number of Firms R2

49891 7261

0.5395

49835 7256

0.5395

49891 7261

0.5367

49835 7256

0.5367 NOTES: Each cell shows the point estimate, standard error (in parenthesis), and t statistic (in brackets). Sales and the cost of capital enter as percentage changes. Cash flow is the ratio of cash flow to the capital stock (beginning of period). Misvaluation is the difference between Stock Market Q and Fundamental Q (both beginning of period), as defined in the text. The regressions include both firm and year effects (whose explanatory power is included in the numerator of R2). Variable definitions are provided in Section 1 and the Appendix.

33

______________________________________________________________________________ TABLE 5 QUANTITATIVE ESTIMATES OF THE EFFECT OF MISVALUATION ON INVESTMENT Q SPECIFICATION =====================================================================

OLS IV

(1) (2) (3) (4) Misvaluation 0.0029

(0.0001) [39.56]

0.0030 (0.0001) [39.93]

0.0091 (0.0011)

[7.99]

0.0090 (0.0011)

[7.90]

Fundamental Q 0.0303 (0.0006) [52.04]

0.0294 (0.0006) [49.58]

0.0339 (0.0006) [56.69]

0.0333 (0.0006) [54.82]

Cash Flow 0.0050 (0.0006)

[7.74]

0.0033 (0.0007)

[4.90]

Number of Observations Number of Firms R2

58260 8340

0.5118

58192 8335

0.5123

54507 7808

0.5000

54443 7804

0.5002 NOTES: Each cell shows the point estimate, standard error (in parenthesis), and t statistic (in brackets). Cash flow is the ratio of cash flow to the capital stock (beginning of period). Misvaluation is the difference between Stock Market Q and Fundamental Q (both beginning of period), as defined in the text. The regressions include both fixed effects and year effects (whose explanatory power is included in the numerator of R2). The last two columns are estimated by IV, using −, 1

Si tR (the component of returns that is associated with investor sentiment, as measured

by the Baker-Wurgler (2007) index and that is orthogonal to fundamental risk) as an instrument for

,i tM . The first stage F-statistic for the regression of

,i tM on −, 1

Si tR and a constant is 229.7 (p-

value of 0.000). Variable definitions are provided in Section 3 and the Appendix (especially Section F).