Accounting Conservatism and Corporate Investment Decisions: Evidence from a Structural Assessment

Chen Chen The University of Auckland

Owen G Glenn Building 12 Grafton Road

Auckland Tel: (+64) 9.923 7321

Email: c.chen@auckland.ac.nz

Yi Hu Chinese Academy of Science Email: huyi1985@gmail.com

Karen Jingrong Lin University of Massachusetts Lowell

Manning School of Business 1 University Ave

Lowell, MA, 01854 Tel: 978.934.2406

Email: Jingrong_lin@uml.edu

2

Accounting Conservatism and Corporate Investment Decisions:

Evidence from a Structural Assessment

Abstract

We investigate whether accounting conservatism solves the misalignment of interest between managers and shareholders by increasing hurdle rates used by managers during project selections. We argue that accounting conservatism raises managerial cautiousness in project screening. By incorporating bad news timely into earnings, conservative accounting system increases the likelihood of early termination of unsuccessful projects, increasing personal costs of the manager and thus deterring managers from investing in projects merely to enjoy private benefits. Using a structural model and GMM estimation, we find that conservative accounting increases hurdle rates and such increases are more pronounced for firms that exhibit higher degree of agency problem. We also find conservatism adds value to firms when investment is under consideration. Our work sheds lights on the literature attempting to identify the relation between accounting conservatism and managers’ investment decisions.

Keywords: Accounting conservatism, Investment Euler equation, Hurdle rates, Corporate governance JEL classification: M41 G31 G34 E22

3

1. INTRODUCTION

We examine the effect of accounting conservatism on managers’ investment

decisions. We predict that managers evaluates projects with higher degree of

cautiousness and thus make less risky investment decisions when accounting

conservatism is in place. Our prediction is consistent with the view that

accounting conservatism reduces agency problem (Ball 2001; Ball and

Shivakumar 2005; Ahmed and Duellman 2007).

Corporate investment is an important decision made by managers. In 2009,

the total amount of corporate capital expenditures was $1,090.1 billion, and in

2008 it was close to $1,400 billion1 (Census Bureau 2011). However, corporate

investment decisions are not always made in the best interest of shareholders

because of the misalignment of managerial and shareholder interests: managers

invest in risky and even negative net present value projects because they enjoy

private benefit of investment (e.g., Jensen and Meckling 1976; Jensen 1986).

Accounting conservatism could potentially alleviate CEOs’ empire-building

incentives. Accounting conservatism is a reporting mechanism that reflects

accountants’ tendency to require a lower threshold to recognize unfavorable

1 The GDP (Gross Domestic Product) of the U.S. in 2008 was $14,369.1 billion and in 2009 was $13,939 billion. Corporat in the U.S. were about 10% of GDP, which is a significant portion.

4

news than favorable news (Basu 1997)2. We argue that accounting conservatism

improve corporate investment efficiency by changing managers’ investment

decisions. In the presence of accounting conservatism, losses from unsuccessful

investment projects are revealed timely in earnings, leading to early

terminations of such projects. The early terminations of projects penalize

managers by reduced compensation, impaired reputations, or even increased

likelihood of job turnover (Hirshleifer 1993; Weisbach 1995).

Ex ante, if managers are rational to anticipate the accelerated recognition of

losses and thus the timely termination of unsuccessful projects, they will be

more cautious to make the investment decisions from the outset. The increased

cautiousness to invest will be reflected in the hurdle rate used to evaluate an

investment project. In theory, hurdle rate just equals the cost of capital;

managers should not invest in a project if the return is lower than the cost of

financing. In practice, hurdle rate is observed to be either lower than the cost of

capital because of managerial discretion, or higher because of managerial

cautiousness of the irreversibility nature of the decision (e.g., Driver and Temple

2010).

2 We recognize that there are two types of accounting conservatism: conditional and

unconditional conservatism. In the context of investment, conditional conservatism is more

relevant because of the contingent nature of the turnouts of investment decisions. Therefore we

focus on the conditional conservatism.

5

To model the managerial cautiousness to invest, we following Whited and

Wu (2006) and use a structural approach to examine how accounting

conservatism affects hurdle rate and investment behaviors. In this intertemporal

investment model, the manager evaluates the options to invest today versus

tomorrow by comparing costs and benefits of the decision. An Euler investment

equation solves this utility-maximization decision problem: it is the first-order

condition in which the marginal cost of investing today equals that of tomorrow.

If a manager is unwilling to invest in new and risky projects, he/she will use a

lower discount factor to discount the cost of investing tomorrow, making

investing today less appealing compared to investing tomorrow. Hurdle rate is

the inverse function of this stochastic discount factor. In other words, a manager

who is more likely to reject the new risky investment projects today will have a

higher hurdle rate. In section 2, we present a model that formalizes this intuition.

To implement the Euler equation, we parameterized the stochastic discount

factor as a linear function of accounting conservatism and firm-level variables

that are shown to be correlated with the discount factor as in Kang et al. (2010).

We measure accounting conservatism using four proxies, TIMELINESS, CSCORE,

ACCRUAL and SKEWNESS. TIMELINESS captures the asymmetric timeliness of

bad news over good news as measured by the Basu (1997) model; CSCORE is

6

compiled following Khan and Watts (2009) to measure accounting conservatism

based on firm characteristics such as size, leverage, and market-to-book ratio.

ACCRUAL and SKEWNESS are constructed from the statistical properties of

accounting numbers (or of their components) following Beatty et al.(2008).

We find evidences supporting our hypothesis that accounting conservatism

improves firms’ investment efficiency by using a panel of firm-year observations

during 1982 -2009. We document that the presence of conservative accounting

significantly increases managers’ investment hurdle rates and induces

conservative investment decisions, and this effect is more pronounced when the

perceived agency problems are more severe. We also show that accounting

conservatism affects the stochastic discount factor in both financially

constrained and unconstraint firms, alleviating the concern that the observed

positive correlation between accounting conservatism and hurdle rate is mainly

driven by the effect of conservatism on external financing.

Prior studies have established the corporate governance role of accounting

conservatism in resolving information asymmetry, reducing agency costs, and

improving debt contracting efficiency (Ball 2001; Ball and Shivakumar 2005;

Ahmed and Duellman 2007; Beatty et al. 2008; Lafond and Roychowdhury 2008;

LaFond and Watts 2008; García Lara et al. 2009). On how accounting

7

conservatism affects corporate investment, Francis and Martin (2010)

(hereinafter FM2010) provide evidences that higher level of accounting

conservatism is associated with better M&A investment decisions measured by

greater market return around M&A announcements. Our study is closely related

to FM2010 but different in the following important ways. First, an expressed

concern about the relation between corporate investment and accounting

conservatism observed in FM2010 is that such relation could be associational

rather than causal3. We build on this study and provide a channel through which

accounting conservatism affects corporate investment behaviors: by altering

manager’s hurdle rate. This evidence more directly points to the relation

between accounting conservatism and corporate investment efficiency. Second,

FM2010’s results cannot be interpreted as supporting evidence on the impact of

conservatism on M&A profitability if managers cannot commit to use the same

accounting practice after the investment is made (Roychowdhury 2010). Our

theoretical framework avoids this pitfall because managers will not increase

hurdle rates if they anticipate future modification of accounting practices. Finally,

our study is generic in nature and does not rely on the market reaction of

3 As Francis and Martin pointed out, their result “cannot rule out the alternative explanation that

governance mechanisms are independently associated with both accounting conservatism and

better acquisition policies, in which case conservatism per se may not have a direct effect on

acquisition profitability” (Francis and Martin 2010, p.162).

8

investment decisions as FM2010 do; the latter often relays to other assumptions

such as market efficiency and market conditions such as bull or bear markets.

Our study contributes to the broader literature on how financial reporting

could affect firms’ real investment decisions (Biddle and Hilary 2006; Biddle et al.

2009; Beatty et al. 2010). We join the discussion of the role played by a specific

accounting mechanism, accounting conservatism, in managerial investment

decisions and highlight the monitoring role of conservatism (Francis and Martin

2010; Ma 2010; Roychowdhury 2010; Bushman et al. 2011). Our evidences

directly support the view that accounting conservatism has a deterrent effect on

managers’ empire-building incentives. The timely loss recognition imposes

personal costs to managers when investment projects turn out to be

unsuccessful, which in turn changes managers’ attitudes towards risks. Our

study, however, does not address the broader question on whether and how

other accounting quality proxies may change corporate investment behaviors.

This is because theoretically it is less clear how managers’ investment decisions

are related to other accounting quality proxies such as accrual quality, whereas

the ex post as well as the ex ante monitoring role of accounting conservatism is

clear (Roychowdhury 2010).

9

The remainder of this paper is organized as follows. Section 2 discusses prior

literature and develops testable hypotheses. Section 3 describes the model and

the research design. In Section 4 we discuss the construction of our variables and

describe our sample. Section 5 presents the empirical results, and Section 6

concludes the study.

2. HYPOTHESIS DEVELOPMENT

2.1The Effect of Accounting Conservatism on Hurdle Rate

Corporate investment is efficient when internal funds flow to investment

projects with the highest returns. However, managers deviate from the optimal

level of investment because of dysfunctional investment incentives (Stein et al.

2003). These dysfunctional incentives can be reflected in the hurdle rates used to

screen projects. Finance textbooks prescribe that managers should benchmark

the investment return to the cost of capital so that projects with returns lower

than the cost of capital are rejected. In practice, managers use hurdle rates that

are either higher or lower than the cost of capital, depending on the managerial

incentives to invest. For example, Driver and Temple (2010) show that a hurdle

rate that is higher than the cost of capital will be used if the manager is aware of

the embedded risk or is deterred by the irreversibility of the investment project;

10

a hurdle rate that is lower than the cost of capital will be used if the manager has

discretionary power and empire-building incentives such as those depicted in

the study by Jensen (1986). The low hurdle rate leads to overinvestment, which

is often at the cost of shareholders (Shleifer and Vishny 1989; Stulz 1990; Stein

et al. 2003).

Good corporate governance could alleviate the agency problem in

investment (e.g., Billett et al. 2011; Lin et al. 2011), and accounting conservatism

has been documented as an effective monitoring and contracting mechanism

(Ball 2001; Ball and Shivakumar 2005; Ahmed and Duellman 2007; Beatty et al.

2008; Lafond and Roychowdhury 2008; LaFond and Watts 2008; García Lara et

al. 2009). We argue that accounting conservatism improves corporate

investment efficiency by increasing hurdle rates and curbing managers’

incentives to invest in unsound projects merely to enjoy private benefits from

investment.

Accounting conservatism increases corporate investment efficiency in two

ways. On the one hand, by timelier incorporating bad news into earnings,

accounting conservatism triggers the board’s investigation of the unsuccessful

investment project that may lead to its early abandonment, saving shareholders

from potential future losses (Watts 2003). On the other hand, the expected

11

personal costs related to project abandonment such as compensation, reputation

and career concerns would increase managers’ cautiousness in choosing

investment projects at the outset.

If a manager is more cautious about project selections at the beginning, it

will be reflected in the hurdle rate used to evaluate projects. That is, the

stochastic discount factor, an inverse function of hurdle rate, used by the

manager to discount the adjustment costs of investing tomorrow will be lower so

that investing today appears to be less appealing. Therefore, we expect to

observe that the increased level of accounting conservatism is associated with

higher level of hurdle rates used by managers. The first hypothesis is as follows:

H1. The hurdle rate used in screening investment projects is increased with the

level of accounting conservatism.

2.2 Cross-sectional Variation of Ex Ante Agency Costs

The value of accounting conservatism in curbing managers’ incentives to

invest in unsound projects is likely to vary across firms depending on the ex ante

agency costs. Therefore, we consider three firm-level characteristics that could

be associated with higher ex ante agency costs: firms that are less transparent,

12

firms with more free cash flows, and firms that have higher growth

opportunities.

2.2.1 Information Transparency

Corporate transparency refers to the availability of information to market

participants outside the publicly-traded company. Transparency is beneficial to

capital allocation both at country and firm level (Bushman et al. 2004). It is

easier for managers in an opaque firm to cover up the unsuccessful projects and

operate loss-generating project at the cost of the shareholders (Watts 2003).

Therefore, conservatism in these firms is more valuable if it has the monitoring

role as we argued. Empirically, we use firm size and the number of analyst

following the firm are to proxy for the information environment of the firms

(Atiase 1985; Welker 1995; Aboody and Lev 2000; Brown et al. 2004).

2.2.2 Growth Opportunities: Book-to-Market Ratio

Firms with higher growth opportunities relative to assets have higher

contracting costs because growth opportunities are often intangible in nature

and information related to them are less verifiable. As a result, managers have

greater discretionary power and are more likely to manage earnings through

accruals, and thus agency costs are higher in high-growth firms. (Smith and

Watts 1992; Kwon and Yin 2006). In addition, high growth firms are more likely

13

to have more volatile returns and higher probability of lawsuits, which suggests

a higher demand of accounting conservatism (Khan and Watts, 2009).

Empirically, we use book-to-market ratio (an inverse measure of growth

opportunity) to measure the growth opportunity of firms.

2.2.3 Free Cash Flow

Managers are keen to expand the firm to the size beyond the optimal size,

often at the costs of shareholders. Excessive free cash flow allows managers to

do so without costly external financing. Overinvestment problem is found to be

most severe in firms with the highest level of free cash flows (Jensen 1986;

Richardson 2006).

In sum, managers in firms with lower transparency, greater growth

opportunities and higher level of free cash flow are more likely to use their

discretionary powers and enjoy private benefit from investments. Therefore, the

second hypothesis is derived as the follows:

H2. The effect of accounting conservatism on increasing hurdle rate is more

pronounced for firms with higher ex ante agency costs, measured by lower

transparency, greater growth opportunity, or higher level of free cash flow.

14

3. MODEL AND EMPIRICAL FRAMEWORK

Following Kang et al. (2010), we offer a partial-equilibrium model in which a

manager maximizes expected utility by choosing investment and consumption.

The following sections briefly discuss the model and lay out the empirical

framework.

3.1 A Simple Model

Suppose firm i uses capital to produce goods in each period t. A typical utility

maximization decision faced by an owner-manager is based on the expected

value of the firm (Eq. (1)), which is the sum of discounted future dividends,

subject to the dividend payout (Eq. (2)) as well as the stock accumulation (Eq.

(3)) constraints:

,0 ,0 , ( )i i i t

t

V Max E u d

, (1)

subject to

titititititi IKICKd ,,,,,, ),(),( , and

(2)

titiiti IKK ,,1, )1( ; (3)

whereio

E is the expectations operator conditional on the manager’s time 0

information set; is the one-period discount factor common to all firms; ( )u is

the manager’s utility which is concave if the manager is risk averse; andit

d is the

dividend paid by firm i in period t. In the constraints, Ki,t is the

15

beginning-of-period capital stock; ζi,t is a shock to the profit function; П(Ki,t , ζi,t )

is the firm’s profit function. Ii,t is investment during time t; C(Ii,t ,Ki,t) is the real

cost of adjusting the capital stock. δi is the firm-specific constant rate of

economic depreciation.

Solving the utility maximization problem yields the Euler condition for Ki,t:

1,1,1,

,

'

1,

'

,, )(1)(1()()()(

)()(1 tiititi

ti

ti

titiI

C

K

C

Kdu

duE

I

C , (4)

where CI

is the marginal adjustment cost of investment; the term '

, 1

'

,

( )

( )

i t

i t

u d

u d is

the marginal rate of substitution of dividends, or the pricing kernel from a

consumption-based asset pricing model; and K is the marginal profit of capital.

The intuition of Eq. (4) is that, when deciding whether to invest today or

tomorrow, managers evaluate the discounted benefit of harvesting in the future

net of costs versus the investment costs incurred today. This is analogous to the

typical capital allocation problem where a project’s net present value is

calculated.

In the heart of this utility maximization problem is the term)(

)(

,

'

1,

'

ti

ti

du

du , the

marginal rate of substitution of dividends. For notation purposes, we define

)(

)(

,

'

1,

'

1,

ti

ti

tidu

du

and re-write Eq. (4) as:

))(1)(1()()()(1 1,1,1,1,,, tiitititititi

I

C

K

C

KE

I

C . (5)

16

Eq. (5) presents the options that managers face: investing today or

tomorrow. It shows the marginal adjustment cost of investing in this period,

versus expected discounted cost of waiting to invest until next period. The

pricing kernel,)(

)(

,

'

1,

'

1,

ti

ti

tidu

du

, describes the essence of the managers’

investment decisions. On the margin, the manager must be indifferent to the

choice between investing in the current period and transferring those resources

to the next period. The perceived hurdle rate that firm managers use for optimal

investment can be defined as:

11

,

,

ti

tir . (6)

That is, if the manager is more cautious, the discount factor he/she uses will

be lower (and hence the hurdle rate will be higher), making investing today

appears to be less appealing.

3.2 Estimation of the Model

3.2.1 The Investment Decision

Eq. (5) can be converted into the following equation by replacing the

expectation operator with an error, ei,t+1:

M

m

ti

m

ti

ti

m

M

m

M

m

m

ti

ti

mi

m

ti

ti

m

ti

titi

ti

eK

I

K

I

K

I

m

m

K

VCY

2

1,

1

,

,

2 2

1

1,

1,

1,

1,

1,

1,1,

1,

.)(1

))(1)(1()(1

(7)

17

where αm, m=2,…., M are coefficients to be estimated, and M is a truncation

parameter that sets the highest power of ti

ti

K

I

,

, in the expansion. We follow prior

studies to set M = 3. Assuming perfect competition and a constant markup

attached to the cost to obtain the sales prices, we derive the marginal profit of

capital (Eq. (8)) and the real adjustment costs of investment (Eq. (9)):

ti

titi

titiK

VCYK

K ,

,,

,, ),(

, (8)

ti

M

m

m

ti

ti

mtiti KK

I

mKIC ,

2 ,

,

,, ))(1

(),(

, (9)

3.2.2 Hurdle Rate

The stochastic discount factor, Γi,t+1 is parameterized to estimate Eq. (7). We

specify Γi,t+1 as a function of accounting conservatism (CONSERVATISMi,t,) and

several firm-level characteristics that are known to be related to the discount

factor (Fama and French 1992; Jegadeesh and Titman 1993; Kang et al. 2010).

Γi,t =l0 + l1 i,t + l2 Sizei,t + l3 btmi,t + l4 Annret i,t + l5 CONSERVATISMi,t . (10)

where i,t measures the systematic risk as indicated in a standard CAPM model,

Sizei,t, btmi,t, Annret i,t, are firm size, book-to-market ratio, and the past 12-month

cumulative stock return, respectively.

It is not ensured that the model specification is correct, since the

parameterization in Eq. (10) does not allow for an explicit error term. Therefore,

J-test statistics of the overidentifying restrictions are used to check the model's

18

validity. Furthermore, the inclusion of the control variables in Eq. (10) is rather

ad hoc since there is no guidance on which variables should be included in the

parameterization of the stochastic discount factor. We follow the Kang et al.

(2010) study in which the authors suggest that these four variables work best in

most of the specifications.

Additionally, Eq. (7) is estimated in first differences to eliminate possible

fixed firm effects4. This treatment allows us to test the effects of accounting

conservatism on managers’ hurdle rates directly after controlling for other firm

fixed effects such as the level of corporate governance. We estimate the

investment Euler equation applying the general method of moments (GMM)

estimation to the following moment conditions:

tititit eezE ,1,1,1 ( , (11)

where zi,t-1 is a vector of instrumental variables known at time t and ⊗ denotes

the Kronecker product. Because of the first-difference estimation, we include as

4 One concern regarding this first-difference research design is that the observed positive

relation between accounting conservatism and the increased hurdle rate is driven by a particular

subset of firms that are under the restructuring process. These firms tend to write down their

assets (and captured by the conservatism measure), and at the same time suspend new

investments (captured by the increased hurdle rate). We alleviate this concern by conducting

tests on a level model rather than a first-difference model. Our results hold using the alternative

model. We further release this concern by requiring our firm-year observations to have positive

values in earnings before extraordinary item (IB).

19

instruments the two-period-lagged variables including all the variables that

appear in our investment Euler equation and several firm-specific variables.

3.2.3 The Relation between Hurdle Rate and Discount Factor

We hypothesize that accounting conservatism affects corporate investment

by changing managers’ hurdle. In the empirical test, however, we focus on the

coefficient of CONSERVATISMi,t of the stochastic discount factor , Γi,t , which is

prescribed by Eq. (10). Note that the discount factor Γi,t is the inverse function of

hurdle rate, hence we expect l5 in Equation (10) to be significantly negative to

conclude that accounting conservatism raises hurdle rates.

4. DATA AND DESCRIPTIVE STATISTICS

This section first discusses our data sources and the construction of the

samples. It next defines the variables used in the empirical analysis and offers

summary statistics.

4.1 Sample Description

Our analysis is based on a sample of firms listed on the NYSE, AMEX and

NASDAQ exchanges during 1982-2009. We obtained accounting information

from the Standard & Poor's Compustat Annual database. Stock price and stock

return information are retrieved from the monthly CRSP (Center for Research in

20

Security Prices) database. We filtered our sample by the following procedures.

We delete any firm-year observations where values of total assets, gross capital

stock, or sales are either zero or negative. Regulated or financial firms are

excluded from our sample (i.e., primary SIC classifications between 4900 and

4999, or between 6000 and 6999). Firm-year observations with missing data are

dropped, and firms without three consecutive years’ data are deleted. Finally, we

trim our sample at the top and bottom 1% of variables. This filtering process

leaves us with a panel of 27,978 -45,443 firm-year observations for different

specifications.

To test hypothesis H2, we also construct subsamples based on firm-level

characteristics by sorting the full sample into three equal-sized subgroups, and

we use only the top and bottom subgroups for comparison purposes. This

sorting processes yield 9,025-9,173 observations for the subsample sorted by

size, 8,650-11,358 observations for the subsample sorted by analyst following,

8,701-9,284 observations for the subsample sorted by BTM, and 8,980-9,093

observations for the subsamples based on sorted free cash flow ratio5.

5 Free cash flow ratio is calculated by following Richardson (2006).

21

4.2 Variables

4.2.1 Accounting Conservatism

Four proxies for accounting conservatism are used in our study to ensure the

robustness of results

Accounting conservatism based on Baus (1997). The first conservatism

measure we use is TIMELINESSi,t estimated following the Basu (1997) model.

Under conservative accounting, bad news is incorporated into earnings earlier

than the good news. Basu (1997) uses the positive (negative) return to proxy for

the arrivals of good (bad) economic news, and expects to find a higher (lower)

correlation between earnings and negative (positive) returns. The following

regression is then estimated using rolling windows of at least seven prior years

for each firm-year:

Xi,t / Pi,t-1 =α0+α1Reti,t+α2DRi,t+α3DRi,t *Reti,t+ei,t , (12)

where Xi,t is earnings per share, scaled by beginning-of-the-year stock price

(Pi,t-1), Reti,t is annual stock return, and DRi,t is the bad-news dummy that equals

one if annual return is negative, and zero otherwise. The asymmetric timeliness

measure, TIMELINESSi,t, is calculated as the ratio of (α1+α3)/ α1 (Basu 1997; Pope

and Walker 1999; Givoly and Hayn 2000; Francis et al. 2004). TIMELINESSi,t is

expected to be greater than one if conservative accounting is in place.

22

Accounting conservatism based on Khan and Watts (2009). To control

for the time- and firm- specific characteristics of firms’ reporting conservatism,

Khan and Watts (2009) model the timeliness of good and bad news as linear

functions of firm-level characteristics. The bad news (good news) coefficient,

CSCORE (GSCORE) is parameterized as the follows:

CSCOREi,t =λ1+λ2Sizei,t+λ3MBi,t +λ4Levi,t (13),

and

GSCOREi,t = μ1+ μ2Sizei,t+ μ3MBi,t + μ4Levi,t . (14)

Drawing on the Basu (1997) model illustrated as Eq. (12) and replacing Eq. (13)

as α3 and Eq. (14) as α1 in Eq. (12) yield the regression:

Xi,t / Pi,t-1 =0 + 1DRi,t + Ri,t (μ1 + μ2Sizei,t + μ3MBi,t + μ4Levi,t) + Ri,t * DRi,t

(λ1+λ2Sizei,t+ λ3MBi,t +λ4Levi,t )+ (δ1Sizei,t + δ2MBi,t + δ3Levi,t + δ4DRi,t * Sizei,t

+ δ5DRi,t * MBi,t + δ6DRi,t * Levi,t ) + ei,t . (15)

In Eq. (15), variables Xi,t, DRi,t, Ri,t are defined as in Eq. (12). Sizei,t is the

natural logarithm of market value of equity, MBi,t is the market-to-book ratio, and

Levi,t is the leverage. We calculate the firm-year level proxy for accounting

conservatism, CSCOREi,t, by estimating Eq. (15). The advantage of using CSCOREi,t

is that this measure controls for the time- and firm- specific characteristics of

firms’ reporting conservatism.

23

Accounting conservatism based on Givoly and Hayn (2000) and Beatty

et al. (2008). Our third and fourth accounting conservatism measures,

ACCRUALi,t and SKEWNESSi,t are estimated based on the time-series properties of

earnings and accruals. Specifically, we estimate the accumulated non-operating

accruals (ACCRUALi,t) that captures the extent to which earnings incorporate

negative non-operating accruals. It is calculated as the ratio of non-operating

accruals6 to total assets cumulated over the previous three years and multiplied

by -1. We estimate the level of skewness of earnings distribution resulted from

the asymmetric timeliness of good news and bad news incorporated in earnings

due to accounting conservatism: SKEWNESSi,t is calculated by the difference

between the skewness in cash flows from operations and the that in earnings

over the preceding three years window.

4.2.2 Variables used in the investment Euler equation

We estimate Equations (7) and (10) simultaneously. The key variable for the

empirical analysis is corporate investment, Ii,t, which is defined as the difference

6 Nonoperating accruals are defined as net income (NI) plus depreciation (DP) minus cash flow from operations (OANCF) minus the change in accounts receivable (RECT) minus the change in inventories (INVT) minus the change in prepaid expenses (XPP) plus the change in accounts payable (AP) plus the change in tax payable (TXP). Data on cash flow from operations (OANCF)since 1987 is available. If OANCF is missing, we estimate cash flow from operations as funds from operations (FOPT) minus the change in current assets (ACT) minus the change in short term debt (DLC) plus the change in current liabilities (LCT) plus the change in cash (CHE). Accumulated nonoperating accruals are multiplied by -1 so that the value of the conservatism proxy increases with a firm’s level of conservatism.

24

between capital expenditure and the sales of property, plant, and equipment in

year t for firm i.7 Capital stock, Ki,t, is measured by beginning-of-year total assets,

and is used to deflate other firm-level characteristic variables in order to reduce

heteroskedasticity. Firm i's output Yi,t in year t is measured by its sales, and

variable cost (VCi,t) is the sum of the costs of goods sold and the selling, general,

and administrative expenses. Both Yi,t and VCi,t are deflated by beginning-of-year

total assets. To parameterize the discount factor (i.e., Eq. (10)), market model

beta, i,t, is calculated by fitting the CAPM on a rolling basis with monthly stock

returns over the previous five years for every firm-year observations. The

market return (rm) and the risk-free rate (rf) are calculated by the CRSP

value-weighted market index return and by the one-month Treasury bill rate,

respectively. Total stock return volatility (Annvoli,t) is the annualized standard

deviation of monthly stock returns over the past five years. Firm size (Sizei,t) is

the logarithm of the year-end market value of the firm's common equity,

Book-to-market equity (btmi,t) is the book value of the firm's equity divided by its

market value of common equity, cumulative stock return (Annreti,t) is calculated

over the previous twelve months for every firm-year observations, and leverage

(Levi,t) is the ratio of the book value of the firm's debts to its total assets. In the

7 We also use alternative measures of investment defined as the sum of the capital expenditure net

of the sale of PPE and Research and Development cost. We get the similar results.

25

GMM estimation, we use rf,K

Y , K

I ,K

VC ,, Size, Annret, Annvol, and Lev,all lagged

by two periods, as instruments. A constant is also included as an instrumental

variable. The GMM estimation is implemented in first difference.

4.3 Descriptive Statistics

Table1 exhibits the descriptive statistics of the variables used in our main

tests. All the variables in our investment Euler equation have the similar sample

distribution to that presented in Kang et al. (2010). The mean of our main

interested variables, TIMELINESSi,t, CSCOREi,t, ACCRUALi,t, and SKEWNESSi,t, are

also comparable to those presented in studies by Hui et al. (2011), Khan and

Watts (2009), Beatty et al. (2008) and Givoly and Hayn (2000), respectively. The

Pearson and Spearman correlation matrix with each pair of variables are

presented in Table 2. Variable pairs that are correlated within 5% significance

are starred.

[Insert Table 1 and Table 2 about here]

5. EMPIRICAL RESULTS FROM MULTIPLE REGRESSIONS

5.1 The Relation between Accounting Conservatism and Hurdle Rates

26

We test hypothesis H1 by simultaneous estimations of models Eq. (7) and Eq.

(10), and the results are presented in Table 3. Columns (1)-(4) present different

conservatism measures and their effects on the discount factor (i.e., the inverse

function of hurdle rate) that governs a manager’s investment decisions. Column

(1) reports that the parameter estimate of TIMELINESSi,t is -0.243, and is

statistically significant at the 1% level. Column (2)-(4) present estimates for

CSCOREi,t, ACCRUALi,t, and SKEWNESSi,t are also negative and significant at 5%

level or better. Taken together, Columns (1)-(4) suggest that managers who

work for firms with higher level of accounting conservatism use a lower discount

factor (i.e., a higher hurdle rate) when making investment decisions. These

evidences are consistent with monitoring role of accounting conservatism on

curbing managers’ overinvestment incentives. Signs of parameters on all other

control variables are consistent with those documented in prior studies (Whited

1998; Whited and Wu 2006; Kang et al. 2010).

J-test of overidentifying restrictions is commonly included in GMM

estimations to detect the possible misspecification of models. The null

hypothesis is that these specifications do not have overidentifying restrictions

and the test statistics is chi-squared distributed with degrees of freedom equal to

the number of overidentifying restrictions. J-test reported in Column (1) does

27

not reject the exclusion restrictions, indicating a proper specification of the

model and orthogonal property of the instruments but J-test statistics reported

in columns (2)-(4) produce rejections of the exclusion restrictions. Therefore,

although accounting conservatism measures CSCOREi,t, ACCRUALi,t, and

SKEWNESSi,t used to estimate Eq. (10) produce coefficients consistent with the

theoretical derivation, we do not proceed to use these variables in the rest of our

empirical tests. That is, in the following tests we use TIMELINESSi,t as our key

variable to proxy for accounting conservatism.

[Insert Table 3 about here]

5.2 The Moderating Effect of Accounting Conservatism on Hurdle Rate

Conditional ex Ante Agency Costs

Hypothesis H2 is tested by simultaneous estimations of models Eq. (7) and

Eq. (10) in different subsamples with various degrees of ex ante agency costs,

and Table 4 presents these regression results. Columns (1)-(2) present the

parameter estimates based on the subsamples formed according to size. The

coefficient of TIMELINESSi,t estimated in the small-firm subsample is negative

and significant at the 10% level, while this statistic is insignificant when

estimated in the large-firm subsample. This result suggests that accounting

28

conservatism increases managers’ hurdle rates in a greater level in small firms

than in large firms. Similarly, Columns (3) – (8) show that accounting

conservatism has a stronger effect in raising managers’ hurdle rates if the firm is

more opaque (i.e., has a smaller number of analysts following), has greater

growth opportunities, and higher level of free cash flows. Overall, these results

indicate a stronger moderating effect of accounting conservatism in firms with

higher ex ante agency costs. Note that, in seven out of eight cases, J-tests do not

reject the overidentifying restrictions, suggesting the good fit of these models.

[Insert Table 4 about here]

5.3 Additional Tests

To facilitate our understanding of how accounting conservatism affects

managers’ investment decisions and the consequences related to such decisions

and robustness of our tests, we conduct several additional tests as the follows.

5.3.1 Accounting Conservatism, Future Financing Costs, and Hurdle Rate

One concern on the observed relation between accounting conservatism and

hurdle rates is that accounting conservatism may affect cost of financing and

thus change the hurdle rate used by managers: hurdle rate is the average cost of

29

capital plus managers’ discretionary adjustments (Driver and Temple 2010).

That is, if employing a more conservative accounting system could reduce future

cost of financing, managers will delay investment to future, which decreases the

stochastic discount factor and increases the hurdle rate. This suggests that

higher level of accounting conservatism per se does not lead to the lower hurdle

rate because of better monitoring of managers.

If cheaper future external financing is a plausible alternative explanation, we

should observe the moderating effect of accounting conservatism on hurdle rates

pronounced in the financially constrained firms but not in the financially

unconstrained firms. We hence divide our samples into two subsamples, firms

that are financially constrained and unconstrained, using three different proxies

following Denis and Sibilkov 2010 and replicate our analyses concerning the

effect of accounting conservatism on hurdle rate based on these two groups of

firms separately. Results reported in Table 5 demonstrate the moderating effect

of accounting conservatism on hurdle rates in both financially constrained and

unconstrained subsamples. This addresses the concern that accounting

conservatism, by lowering future financing costs, is the key driving factor of a

lower hurdle rate used by the manager.

[Insert Table 5 about here]

30

5.3.2 Accounting Conservatism, Hurdle Rate and Underinvestment

Prior studies express the concern that conservative accounting could induce

managers’ dysfunctional incentives to underinvest (Leuz 2001; Watts 2003). Our

study appears to imply the same conclusion. If accounting conservatism

increases hurdle rates in a disproportional way, managers will give up investing

and enjoy the “quiet life” (Stein et al. 2003). A priori, our theoretical framework

cannot reject the possibility that the increase of hurdle rate resulted from

heightened accounting conservatism leads to underinvestment. However, we are

keen to find out whether such an increase in hurdle rates would hurt

shareholder value.

We test whether the increased level of accounting conservatism has a

negative impact on firm value when investment is under consideration. We test

the following regression model:

, , , ,0 1 , 1 2 , 1 3 4

, , , ,5 76 , 1 8 9 10 , 1

, ,11 , 1*

*i t i t i t i ti t i t

i t i t i t i ti t i t

i t i ti tLev Cash YFE IFE

Ret TIMELINESS TIMELINESS I I Div

Cash Cash Earn NetFin Int Lev

, (16)

where Reti,t is the market-adjusted stock return over a fiscal year and the

benchmark portfolios used are twenty five Fama-French value-weighted

portfolios. ΔIi,t, the change of the capital minus the sale of property, plant, and

equipment. TIMELINESSi,t-1 is the firm-specific coefficient estimates from the

Basu (1997) model in year t-1; ΔCashi,t, is the change in cash, ΔIncomei,t is the

31

change in earnings before interest and extraordinary items, ΔRDi,t is the change

in R&D expenses, ΔDividendi,t is the change in dividends, ΔInteresti,t, is the

change in interest expenses, Cashi,t-1, is the lagged cash holdings, Levi,t , is the

leverage ratio, and Netfini,t is the net financing. The explanatory variables except

leverage are standardized by lagged market equity. Regressions are estimated

using OLS. Statistical significance is computed using heteroskedasticity and

autocorrelation robust standard errors that are clustered at the firm level.

If accounting conservatism, by increasing the hurdle rate, induces

dysfunctional underinvestment incentives for managers, we should observe β2 to

be negative and significant. In addition, if the underinvestment incentives

induced by accounting is detrimental, it will affect firms regardless of the ex ante

agency costs of the firms. On the contrary, if accounting conservatism plays

important monitoring roles, the firm value should increase, and the increments

should be larger for firms with higher perceived agency costs.

Table 6 reports the results of the regression (Eq. (16)). The coefficient of the

interaction term TIMELINESSi,t *∆Ii,t is significantly positive at 5%, indicating on

average the stock market places greater value on capital investments made by

firms with more conservative accounting than it does for capital investments of

the firms with less conservative accounting.

32

[Insert Table 6 about here]

Table 7 presents results from estimations of Eq. (16) based on subsamples of

firms that have various degrees of ex ante agency costs. Columns (1)-(2) present

results when Sizei,t is used to partition the sample. The estimated coefficient on

TIMELINESSi,t *∆Ii,t, is significantly positive for the small size group, whereas it is

insignificant for the large size group. This evidence is consistent with the

prediction that when firms have higher degrees of agency problems, accounting

conservatism adds more value. Columns (3)-(8) convey the same message when

the sample is partitioned according to analyst following, growth opportunity and

free cash flow holdings.

[Insert Table 7 about here]

5.3.3 Controlling for other accounting quality measures and corporate

governance measures

We further control for other accounting quality measures and firm-level

corporate governance measures in Equation (10) to see whether our results still

hold. In particular, we include the performance-adjusted abnormal accrual

suggested by Kothari et al. (2005), the accrual quality measure of Dechow and

Dichev (2002), the financial statement readability measure suggested by Li

(2008), the GSCORE developed by Gompers et al. (2003), the CEO-president

33

duality dummy and board independence ratio suggested by Harford et al. (2008).

The untabulated results show that our conservatism measures are still positively

correlated to managers’ hurdle rates and other accounting matrix are negatively

correlated with managers’ hurdle rates. This is not surprising given that

managers’ hurdle rate is the sum of firms’ cost of capital and managers’

subjective adjustment and Francis et al. (2004) already finds that the accounting

quality measures except for conservatism measures are negatively correlated

with firms’ cost of capital. In addition, we find the corporate governance

variables are also positively correlated with managers’ hurdle rates, indicating

that the better the corporate governance of the firm, the higher hurdle rates

managers use to screen the projects.

6. CONCLUSIONS

Misalignment of interest as well as asymmetric information between

managers and shareholders are the main reasons for investment inefficiency.

Accounting conservatism is documented as an importing monitoring and

contracting mechanism, and we investigate how this mechanism is related to

corporate investment decisions. We argue that accounting conservatism could

increase managerial cautiousness during project. By recognize bad news timely,

accounting conservatism accelerates terminations of unsuccessful projects,

34

increase personal costs of managers and thus deter managers from investing in

projects merely to enjoy private benefits.

Using a structural model and GMM estimation, we find that conservative

accounting increases hurdle rates and such increases are more pronounced for

firms that exhibit higher degree of agency problem. Our additional tests rule out

the alternative explanation that conservatism changes hurdle rate by reducing

the cost of future financing rather than by increasing the efficiency of contracting

with the managers. We also find conservatism adds value to firms when

investment is under consideration. Our results are robust when we further

control for other accounting quality and corporate governance measures. Our

work sheds lights on the literature attempting to identify the relation between

accounting conservatism and managers’ investment decisions.

35

Reference

Aboody, D., and B. Lev. 2000. Information asymmetry, r&d, and insider gains. The Journal

of Finance 55 (6): 2747-2766.

Ahmed, A. S., and S. Duellman. 2007. Accounting conservatism and board of director

characteristics: An empirical analysis. Journal of Accounting and Economics 43

(2-3): 411-437.

Atiase, R. K. 1985. Predisclosure information, firm capitalization, and security price

behavior around earnings announcements. Journal of Accounting Research 23 (1):

21-36.

Ball, R. 2001. Infrastructure requirements for an economically efficient system of public

financial reporting and disclosure. In Brookings-wharton papers on financial

services, 2001: Brookings Institution Press, 127-169.

Ball, R., and L. Shivakumar. 2005. Earnings quality in uk private firms: Comparative loss

recognition timeliness. Journal of Accounting and Economics 39 (1): 83-128.

Basu, S. 1997. The conservatism principle and the asymmetric timeliness of earnings.

Journal of Accounting and Economics 24 (1): 3-37.

Beatty, A., W. Scott Liao, and J. Weber. 2010. The effect of private information and

monitoring on the role of accounting quality in investment decisions.

Contemporary Accounting Research 27 (1): 17-47.

Beatty, A., J. Weber, and J. J. Yu. 2008. Conservatism and debt. Journal of Accounting and

Economics 45 (2-3): 154-174.

Biddle, G. C., and G. Hilary. 2006. Accounting quality and firm-level capital investment.

The Accounting Review 81 (5): 963-982.

Biddle, G. C., G. Hilary, and R. S. Verdi. 2009. How does financial reporting quality relate

to investment efficiency? Journal of Accounting and Economics 48 (2-3): 112-131.

Billett, M. T., J. A. Garfinkel, and Y. Jiang. 2011. The influence of governance on

investment: Evidence from a hazard model. Journal of Financial Economics 102

(3): 643-670.

Brown, S., S. A. Hillegeist, and K. Lo. 2004. Conference calls and information asymmetry.

Journal of Accounting and Economics 37 (3): 343-366.

Bushman, R. M., J. D. PIotroski, and A. J. Smith. 2004. What determines corporate

transparency? Journal of Accounting Research 42 (2): 207-252.

Bushman, R. M., J. D. Piotroski, and A. J. Smith. 2011. Capital allocation and timely

accounting recognition of economic losses: Blackwell Publishing Ltd, 1-33.

Census Bureau. 2011. 2011 capital spending report:U.S. Capital spending

patterns—2000-2009.

36

Dechow, P. M. and I. D. Dichev. 2002. The quality of accruals and earnings: the role of

accrual estimation errors. The Accounting Review 77 (Supplement): 35-59.

Denis, D. J., and V. Sibilkov. 2010. Financial Constraints, Investment, and the Value of

Cash Holdings. Review of Financial Studies 23 (1):247-269.

Driver, C., and P. Temple. 2010. Why do hurdle rates differ from the cost of capital?

Cambridge Journal of Economics 34 (3): 501-523.

Fama, E. F., and K. R. French. 1992. The cross-section of expected stock returns. The

Journal of Finance 47 (2): 427-465.

Francis, J., R. LaFond, P. M. Olsson, and K. Schipper. 2004. Costs of equity and earnings

attributes. The Accounting Review 79 (4): 967-1010.

Francis, J. R., and X. Martin. 2010. Acquisition profitability and timely loss recognition.

Journal of Accounting and Economics 49 (1-2): 161-178.

García Lara, J., B. García Osma, and F. Penalva. 2009. Accounting conservatism and

corporate governance. Review of Accounting Studies 14 (1): 161-201.

Givoly, D., and C. Hayn. 2000. The changing time-series properties of earnings, cash

flows and accruals: Has financial reporting become more conservative? Journal

of Accounting and Economics 29 (3): 287-320.

Gompers, P. A., J. Ishii, and A. Metrick. 2003. Corporate governance and equity prices.

Quarterly Journal of Economics 118:107.55.

Harford, J., S. Mansi., and W. Maxwell. 2008. Corporate governance and firm cash holding

in the US. Journal of Financial Economics 87: 535-555.

Hirshleifer, D. 1993. Managerial reputation and corporate investment decisions.

Financial Management 22 (2): 145-160.

Hui, K. W., S. Klasa, and E. Yeung. 2011. Corporate suppliers and customers and

accounting conservatism. Journal of Accounting and Economics 53

(1)-(2):115-135.

Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers:

Implications for stock market efficiency. The Journal of Finance 48 (1): 65-91.

Jensen, M. C. 1986. Agency costs of free cash flow, corporate finance, and takeovers. The

American Economic Review 76 (2): 323.

Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency

costs and ownership structure. Journal of Financial Economics 3 (4): 305-360.

Kang, Q., Q. Liu, and R. Qi. 2010. The sarbanes-oxley act and corporate investment: A

structural assessment. Journal of Financial Economics 96 (2): 291-305.

Khan, M., and R. L. Watts. 2009. Estimation and empirical properties of a firm-year

measure of accounting conservatism. Journal of Accounting and Economics 48

(2-3): 132-150.

37

Kwon, S. S., and Q. J. Yin. 2006. Executive compensation, investment opportunities, and

earnings management: High-tech firms versus low-tech firms. Journal of

Accounting, Auditing and Finance 21 (2): 119-148.

Lafond, R., and S. Roychowdhury. 2008. Managerial ownership and accounting

conservatism: Blackwell Publishing Inc, 101-135.

LaFond, R., and R. L. Watts. 2008. The information role of conservatism. The Accounting

Review 83 (2): 447-478.

Leuz, C. 2001. Comments on infrastructure requirements for an economically efficient

system of public financial reporting and disclosure. In Brookings-wharton papers

on financial services, 2001: Brookings Institution Press, 170-178.

Li,F. 2008. Annual report readability, current earnings and earnings persistence. Journal

of Accounting and Economics 45: 221-247.

Lin, C., Y. Ma, and Y. Xuan. 2011. Ownership structure and financial constraints:

Evidence from a structural estimation. Journal of Financial Economics 102 (2):

416-431.

Ma, T. 2010. Accounting conservatism and corporate investment. Working Paper,

Washington University in St. Louis.

Pope, P. F., and M. Walker. 1999. International differences in the timeliness,

conservatism, and classification of earnings. Journal of Accounting Research 37:

53-87.

Richardson, S. 2006. Over-investment of free cash flow. Review of Accounting Studies 11

(2): 159-189.

Roychowdhury, S. 2010. Discussion of: "Acquisition profitability and timely loss

recognition" by j. Francis and x. Martin. Journal of Accounting and Economics 49

(1-2): 179-183.

Shleifer, A., and R. W. Vishny. 1989. Management entrenchment: The case of

manager-specific investments. Journal of Financial Economics 25 (1): 123-139.

Smith, C. W., and R. L. Watts. 1992. The investment opportunity set and corporate

financing, dividend, and compensation policies. Journal of Financial Economics

32 (3): 263-292.

Stein, J. C., M. H. G.M. Constantinides, and R. M. Stulz. 2003. Agency, information and

corporate investment. In Handbook of the economics of finance: Elsevier,

111-165.

Stulz, R. 1990. Managerial discretion and optimal financing policies. Journal of Financial

Economics 26 (1): 3-27.

Watts, R. L. 2003. Conservatism in accounting part i: Explanations and implications. In

Accounting Horizons: American Accounting Association, 207-221.

38

Weisbach, M. S. 1995. Ceo turnover and the firm's investment decisions. Journal of

Financial Economics 37 (2): 159-188.

Welker, M. 1995. Disclosure policy, information asymmetry, and liquidity in equity

markets*. Contemporary Accounting Research 11 (2): 801-827.

Whited, T. M. 1998. Why do investment euler equations fail? Journal of Business &

Economic Statistics 16 (4): 479-488.

Whited, T. M., and G. Wu. 2006. Financial constraints risk. Review of Financial Studies 19

(2): 531-559.

39

Appendix I Variable Definition

Variable Conservatism Variables

TIMELINESSi,t The coefficient estimate of the negative return when regressed on earnings following the Basu (1997) model, estimating from at least an eight-year rolling window of each firm-year.

CSCOREi,t Firm-specific conservatism measure estimated following Khan and Watts (2009). ACCRUALi,t Accumulated non-operating accruals estimated following Givoly and Hayn (2000).

SKEWNESSi,t the difference between the skewness in cash flows and that in earnings distributions, estimated following Beatty et al. (2008).

Variables in Investment Euler Equation

Ii,t Investment, defined as capital expenditure minus the sale of property, plant, and equipment (CAPX – SPPE).

Ki,t Capital stock, = total assets of a fiscal year (AT).

VCi,t Variable costs incurred in a given year, including cost of goods sold (COGS) and selling, general, and administrative costs, i.e., = COGS + SGA.

Yi,t Output, defined as total sales (Sale)

i,t A firm’s beta computed from fitting a CAPM on a rolling basis with monthly returns over the previous five years.

Sizei,t Defined as the logarithm of a firm’s market capitalization, which is the product of the number of shares outstanding at year-end and the stock price at year-end (PRCC_F*CSHO).

btmi,t Book-to-market Ratio, calculated as book equity (CEQ) over market equity (PRCC_F*CSHO).

Annreti,t Annual return. Calculated as the past 12-month cumulative stock return.

Annvoli,t Annual stock volatility. Defined as the annualized standard deviation in monthly stock returns over the previous five years.

rf The risk-free rate, measured by the one-month Treasury bill rate. Levi,t Leverage. The ratio of book value of long term debt to total assets. Other variables Analyst The number of analyst following. FCFi,t Free cash flow of the firm defined as Richardson 2006. Variables in Market Valuation Test

ΔIi,t Change in investment, defined as capital expenditure minus the sale of property, plant, and equipment (CAPX – SPPE) scaled by lagged market value of equity (PRCC_F*CSHO).

ΔCashi,t, Change in cash (CHE) scaled by lagged market value of equity (PRCC_F*CSHO).

ΔIncomei,t, Change in earnings before interest and extraordinary items (IB) scaled by lagged market value of equity (PRCC_F*CSHO).

ΔDividendi,t, Change in dividend. (DVC) scaled by lagged market value of equity (PRCC_F*CSHO).

ΔInteresti,t Change in Interest Expenses (EXP) scaled by lagged market value of equity (PRCC_F*CSHO).

Netfini,t, The sum of issuance of equity and debt during the fiscal year (DLTIS and SSTK) scaled by lagged market value of equity (PRCC_F*CSHO).

40

Table 1. Descriptive Statistics This table presents the descriptive statistics of the variables used in the empirical analysis on an annual basis over 1982–2009. The sample is constructed from the Compustat and CRSP databases. Investment (I), total variable costs (VC) and output (Y) are scaled by the beginning-of-the-year total assets. The conservatism measures used are TIMELINESS, CSCORE, ACCRUAL and SKEWNESS. TIMELINESS is the coefficient estimates following the Basu (1997) model, estimating from at least an eight-year rolling window of each firm-year.; CSCORE is estimated by following Khan and Watts (2009); ACCRUAL is the accumulated nonoperating accruals estimated by following Givoly and Hayn (2000); and SKEWNESS is the difference between the skewness in cash flows and the skewness in earnings, estimated by following Beatty et al. (2008). All other variables are defined in Appendix I. Variable Obs Mean Median SD Q1 Q3 Min Max

Ii,t/Ki,t 47,406 0.063 0.045 0.062 0.023 0.082 -0.050 0.464

VCi,t/Ki,t 47,406 1.237 1.101 0.745 0.723 1.577 0.0830 4.785

Yi,t/Ki,t 47,406 1.367 1.235 0.776 0.832 1.735 0.0290 4.940

i,t 47,406 1.055 1.009 0.604 0.654 1.385 -0.464 3.713

Sizei,t 47,406 5.414 5.325 2.169 3.727 6.993 0.764 10.78

btmi,t 47,406 0.676 0.557 0.525 0.339 0.878 -1.631 3.648

Annreti,t 47,406 0.151 0.135 0.466 -0.119 0.392 -1.367 2.230

Annvoli,t 47,406 1.611 1.448 0.698 1.100 1.953 0.621 5.691

rf 47,406 0.045 0.048 0.019 0.032 0.054 0.001 0.095

Analysti,t 47,406 1.788 2 2.521 0 3 0 48

FCFi,t 47,406 -0.041 -0.004 0.215 -0.098 0.072 -0.485 0.491

TIMELINESSi,t 33,102 1.072 0.587 5.110 -0.621 2.465 -17.12 23.48

CSCOREi,t 43,959 0.130 0.127 0.116 0.063 0.193 -1.048 1.386

ACCRUALi,t 26,949 0.026 0.015 0.104 -0.010 0.049 -0.503 0.637

SKEWNESSi,t 46,739 0.389 0.368 1.419 -0.606 1.410 -3.109 3.503

41

Table 2. Correlation Matrix

Ii,t/Ki,t VCi,t/Ki,t Yi,t/Ki,t i,t Sizei,t btmi,t Annreti,t Annvoli,t rf Analysti,t FCFi,t TIMELINESSi,t CSCOREi,t ACCRUALi,t SKEWNESSi,t

Ii,t/Ki,t 1 0.058* 0.121* 0.011 0.231* -0.181 0.049* -0.165* 0.147* 0.039* 0.174* -0.019* -0.232* -0.105* -0.004

VCi,t/Ki,t 0.014* 1 0.982* -0.059* -0.291* 0.071* 0.065* 0.027* 0.122* -0.025 -0.038* 0.038* 0.101* 0.087* 0.011

Yi,t/Ki,t 0.068* 0.983* 1 -0.058* -0.179* 0.010 0.102* -0.018* 0.125* 0.088* -0.004 0.040* 0.041* 0.102* 0.003 i,t -0.016* -0.048* -0.065* 1 0.097* -0.093* -0.005 0.365* 0.097* -0.034* -0.124* 0.008 -0.111* 0.054* 0.025* Sizei,t 0.144* -0.187* -0.118* 0.069* 1 -0.472* 0.116* -0.436* -0.157* 0.059* 0.058* -0.054* -0.616* -0.077* -0.021* btmi,t -0.133* 0.013* -0.024* -0.102* -0.401* 1 -0.344* 0.051* 0.126* 0.011 0.022* -0.013* 0.430* -0.118* -0.029* Annreti,t 0.049* 0.078* 0.115* 0.025* 0.127* -0.318* 1 0.052* -0.000 0.021* 0.032* -0.006 -0.147* -0.079* -0.056* Annvoli,t -0.091* 0.033* -0.027* 0.399* -0.441* -0.001 0.114* 1 -0.135* -0.012* -0.144* 0.048* 0.363* 0.017* -0.002 rf 0.112* 0.083* 0.086* 0.024* -0.200* 0.071* -0.035* -0.105* 1 0.005 -0.018* -0.002 -0.034* 0.005 0.042*

Analysti,t 0.031* -0.033 -0.082* -0.045* 0.072* 0.012 0.025* -0.021* 0.007 1 -0.178* 0.016* 0.125* 0.032* 0.025*

FCFi,t 0.187* -0.040* -0.002 -0.101* 0.063* 0.026* 0.039* -0.175* -0.023* -0.132* 1 -0.112 -0.100* -0.102 -0.013*

TIMELINESSi,t -0.012* 0.012* 0.012* 0.022* -0.008* -0.015* -0.012* 0.017* 0.029 0.012* -0.007 1 0.033* 0.043* -0.018*

CSCOREi,t -0.148* 0.063* 0.017* -0.078* -0.564* 0.374* -0.147* 0.262* -0.029* 0.233* -0.006* 0.012* 1 0.033* 0.028*

ACCRUALi,t 0.073* 0.100* 0.109* 0.047* -0.020* -0.079* 0.029* 0.021* 0.032* 0.029* -0.112* 0.016* 0.032* 1 0.048*

SKEWNESSi,t -0.007 0.014* 0.008 0.021* -0.016* 0.024* -0.053* -0.003 0.036* 0.032* -0.006 -0.007 0.026* 0.056* 1

Note: This table presents the Spearman/ Pearson correlation of each variable used in the empirical analysis on an annual basis over 1982–2009. All variables

are defined in Table 1.

42

Table 3. GMM Estimates of Investment Euler Equation This table reports the results from GMM estimations. We estimate our investment Euler equation on the sample consisting of firms during 1982–2009. The number of firms varies as various conservatism measures require different data availability.

, 1 , 1 , 1 , 1 1

,

2 2, 1 , 1 , 1

Mi,t m-1

m i,t+1

m-2 i,t

,

1* ( ) (1 )(1 ( ) )

I=1+ α ( ) +e ., and

K

(

M Mi t i t i t i tm m

i t m i m

m mi t i t i t

i t

Y VC I Im

K m K K

l

0 1 , 2 , 3 , 4 , 5 , )i t i t i t i t i tl l Size l btm l Annret l Conservatism

I/K is used to measure investment, and α2 and α3 are adjustment cost parameters. We use four different measures to proxy for Conservatism, namely, TIMELINESS, CSCORE, ACCRUAL, and SKEWNESS. Specifically, TIMELINESS is the coefficient estimates of negative returns following the Basu (1997) model, estimating from at least an eight-year rolling window of each firm-year; CSCORE is the firm-specific conservatism measure estimated following Khan and Watts (2009; ACCRUAL is the accumulated non-operating accruals estimated following Givoly and Hayn (2000), and SKEWNESS is the difference between the skewness in cash flows and the skewness in earnings, estimated following Beatty et al. (2008). All other variables are defined in Appendix I. We conduct the GMM estimation on the model in the first differences with twice-lagged instrumental variables. For all estimations, we include the following instruments: constant; rf, Y/K, VC/K, I/K, , size, btm, annvol, annret, leverage, each lagged by two periods; The λ2 values, degrees of freedom, and p-values from the J-test (overidentifying restriction test) are reported in the bottom section of the table.

(1) TIMELINESSi,t

(2) CSCOREi,t

(3) ACCRUALi,t

(4) SKEWNESSi,t

μi,t -0.002 (-0.13)

0.012** (2.07)

-0.005 (-0.56)

0.023** (2.03)

α2 0.490*** (2.73)

0.309*** (4.88)

0.257** (2.08)

0.662*** (4.81)

α3 -0.169*** (-2.85)

-0.098*** (-4.88)

-0.088** (-2.28)

-0.205*** (-4.65)

i,t 0.0239 (1.03)

-0.175*** (-19.75)

-0.038*** (-5.68)

-0.278*** (-13.87)

Sizei,t -0.079*** (-9.54)

-0.085*** (-28.04)

-0.071*** (-11.34)

-0.086*** (-13.72)

btmi,t -0.668*** (-12.24)

-0.584*** (-24.15)

-0.564*** (-17.35)

-0.348*** (-10.70)

Annreti,t 0.036 (0.11)

0.415*** (4.98)

-1.257*** (-10.41)

0.850*** (4.64)

Conservatismi,t -0.243*** (-3.16)

-0.442*** (-4.55)

-0.679** (-2.40)

-0.792*** (-5.35)

λ2 of J Test 2.430 4.441 15.097 65.227 Degree of freedom 2 2 2 2 p-value of J Test 0.2967 0.1081 0.0005 0 N 27,978 42,749 25,128 45,443

Note: *** , ** and * denote significance at the 1, 5, and 10% levels, respectively. Coefficient estimates are reported with t-statistics in parentheses.

43

Table 4. Value of Conservatism When Ex Ante Agency Costs Vary: GMM Estimates of Investment Euler Equation for Subsamples

This table reports the results from GMM estimations from different subsets of our sample. We estimate our investment Euler equation on the sample consisting of firms during 1982–2009. The number of firms varies as various conservatism measures require different data availability.

, 1 , 1 , 1 , 1 1

,

2 2, 1 , 1 , 1

Mi,t m-1

m i,t+1

m-2 i,t

,

1* ( ) (1 )(1 ( ) )

I=1+ α ( ) +e ., and

K

(

M Mi t i t i t i tm m

i t m i m

m mi t i t i t

i t

Y VC I Im

K m K K

l

0 1 , 2 , 3 , 4 , 5 , )i t i t i t i t i tl l Size l btm l Annret l Conservatism

I/K is used to measure investment, and α2 and α3 are adjustment cost parameters. We use four different measures to proxy for Conservatism, namely, TIMELINESS, CSCORE, ACCRUAL, and SKEWNESS. Specifically, TIMELINESS is the coefficient estimates of negative returns following the Basu (1997) model, estimating from at least an eight-year rolling window of each firm-year; CSCORE is the firm-specific conservatism measure estimated following Khan and Watts (2009; ACCRUAL is the accumulated non-operating accruals estimated following Givoly and Hayn (2000), and SKEWNESS is the difference between the skewness in cash flows and the skewness in earnings, estimated following Beatty et al. (2008). All other variables are defined in Appendix I. We conduct the GMM estimation on the model in the first differences with twice-lagged instrumental variables. For all estimations, we include the following instruments: constant; rf, Y/K, VC/K, I/K, , size, btm, annvol, annret, leverage, each lagged by two periods; The λ2 values, degrees of freedom, and p-values from the J-test (overidentifying restriction test) are reported in the bottom section of the table.

(1) Size-Small

(2) Size-Large

(3) Analyst-Low

(4) Analyst-High

(5)

BTM-Small

(6)

BTM-Large

(7) FCF-Small

(8) FCF-Large

μi,t -0.018 (-0.63)

-0.072** (-2.11)

-0.171*** (-2.57)

-0.003 (-0.18)

0.181** (2.12)

-0.045** (-2.56)

-0.014 (-0.44)

-0.069 (-0.69)

α2 -0.071 (-0.22)

-1.199*** (-2.74)

-1.800*** (-3.50)

0.011 (0.16)

2.425** (2.54)

-1.125*** (-3.67)

0.476** (1.97)

0.209 (0.24)

α3 0.015 (0.19)

0.433*** (2.85)

0.645*** (3.50)

-0.005 (-0.52)

-0.725** (-2.41)

0.335*** (3.17)

-0.014** (-2.01)

-0.099 (-0.30)

i,t -0.096*** (-5.11)

-0.133 (-1.52)

-0.171*** (-5.42)

-0.003 (-0.61)

0.012 (0.34)

-0.119*** (-3.24)

0.009 (0.21)

0.304* (1.73)

Sizei,t N/A N/A 0.008 (0.40)

0.083*** (-44.69)

-0.141*** (-4.96)

-0.031*** (-3.87)

-0.082*** (-7.08)

-0.051 (-1.28)

btmi,t -0.419*** (-7.23)

0.291 (0.82)

-0.052 (-0.62)

0.418*** (-18.80)

N/A N/A -0.767*** (-7.02)

0.178 (0.75)

Annreti,t -0.521*** (-10.91)

-1.626*** (-4.79)

-0.799* (-1.80)

0.568*** (9.47)

0.245 (0.30)

-2.521*** (-4.26)

0.083 (0.15)

-0.148** (-2.48)

TIMELINESSi,t -0.228* (-1.85)

0.276 (1.28)

-0.215** (-2.11)

0.00 (0.12)

-0.268** (-2.51)

-0.009 (-0.18)

-0.218 (-1.56)

-0.761** (-2.48)

λ2 values of J Test 5.191 0.127 9.362 1.603 1.829 2.554 1.823 2.231

Degree of freedom 2 2 2 2 2 2 2 2

p-value of J Test 0.075 0.938 0.009 0.449 0.401 0.279 0.402 0.326

N 9,025 9,173 8,650 11,358 8,701 9,284 9,093 8,980

Note: *** , ** and * denote significance at the 1, 5, and 10% levels, respectively. Coefficient estimates are reported with t-statistics in parentheses.

44

Table 5. Additional Test: GMM Estimates of Investment Euler Equation for Financially Constrained and Unconstrained Firms

This table reports the results from GMM estimations from different subsamples partitioned by various financial constrained criteria. We estimate our investment Euler equation on the sample consisting of firms during 1982–2009. The number of firms varies as various conservatism measures require different data availability.

, 1 , 1 , 1 , 1 1

,

2 2, 1 , 1 , 1

Mi,t m-1

m i,t+1

m-2 i,t

,

1* ( ) (1 )(1 ( ) )

I=1+ α ( ) +e ., and

K

(

M Mi t i t i t i tm m

i t m i m

m mi t i t i t

i t

Y VC I Im

K m K K

l

0 1 , 2 , 3 , 4 , 5 , )i t i t i t i t i tl l Size l btm l Annret l Conservatism

I/K is used to measure investment, and α2 and α3 are adjustment cost parameters. We use TIMELINESS as our measure of conservatism. TIMELINESS is the coefficient estimates of negative returns following the Basu (1997) model, estimating from at least an eight-year rolling window of each firm-year. All other variables are defined in Appendix I. We conduct the GMM estimation on the model in the first differences with twice-lagged instrumental variables. For all estimations, we include the following instruments: constant; rf, Y/K, VC/K, I/K, , size, btm, annvol, annret, leverage, each lagged by two periods; The λ2 values, degrees of freedom, and p-values from the J-test (overidentifying restriction test) are reported in the bottom section of the table. We use three different measures of financial constraint following Denis and Sibilkov 2010. For each year, we assign those firms in the bottom (top) three deciles of the annual dividend payout ratio distribution to the financially constrained (unconstrained) group. Firms are classified as financially unconstrained if they have had their short-term debt rated by S&P’s and their debt is not in default. Firms are classified as constrained if they have debt outstanding that year, but have never had their short-term debt rated before (or the rating is unavailable). Firms are classified as financially unconstrained if they have had their long term debt rated by Standard & Poor’s (S&P Long-term Senior Debt Rating is available on Compustat) and their debt is not in default (rating of “D” or “SD”). Firms are classified as constrained if they have debt outstanding that year, but have never had their public debt rated before (or the long-term debt rating is unavailable).

(1)

Divdend- Constrained

(2) Divdend- Unconstrained

(3) Paperrating- Constrained

(4) Paperrating- Unconstrained

(5) Bondrating- Constrained

(6) Bondrating- Unconstrained

μi,t -0.069 (-0.88)

-0.048 (-0.82)

-0.257 (-1.61)

0.011 (0.19)

-0.045 (-1.33)

-0.063** (-2.33)

α2 -0.457 (-0.59)

0.060 (0.12)

-0.105 (-0.13)

0.535 (1.04)

0.192 (0.71)

-0.447*** (-1.49)

α3 0.141 (0.58)

-0.063 (-0.32)

0.059 (0.22)

-0.189 (-1.13)

-0.062 (-0.73)

0.136 (1.30)

i,t -0.057 (-0.84)

0.182 (1.49)

0.188 (1.03)

-0.161** (-2.05)

0.002 (0.07)

0.092 (1.35)

Sizei,t -0.057*** (-4.24)

-0.054*** (-3.24)

-0.1.7* (-1.85)

-0.068*** (-3.01)

-0.089*** (-5.86)

-0.037*** (-2.98)

btmi,t -0.247** (-2.33)

-0.456*** (-2.65)

-0.216*** (-2.11)

-0.416** (-2.49)

-0.542*** (-6.12)

-0.179** (-2.29)

Annreti,t 0.146 (0.21)

0.353 (0.41)

-0.989 (-0.85)

0.725 (0.73)

0.286 (0.41)

-1.171** (-2.40)

TIMELINESSi,t -0.544*** (-3.18)

-0.594** (-2.28)

-0.604** (-1.98)

-0.666*** (-3.54)

-0.251* (-1.91)

-0.323** (-2.11)

λ2 values of J Test 3.531 3.306 3.748 3.115 0.608 4.366 Degree of freedom 2 2 2 2 2 2 p-value of J Test 0.171 0.191 0.153 0.211 0.738 0.112 N 7,489 9,268 4,047 16,975 8,464 16,948

Note: *** , ** and * denote significance at the 1, 5, and 10% levels, respectively. Coefficient estimates are reported with t-statistics in parentheses.

45

Table 6. Additional Test: The Value of Accounting Conservatism in Capital Investment

This table reports the valuation test using the following model.

, 0 1 , 2 , , 3 , 4 ,

5 , 6 , 1 7 , 8 , 9 , 10 , 1

11 , 1 , ,

*

*

i t i t i t i t i t i t

i t i t i t i t i t i t

i t i t i t

ret TIMELINESS TIMELINESS I I Div

Cash Cash Earn NetFin Int Lev

Lev Cash YFE IFE

The dependent variable reti,t is stock return over fiscal year minus the return on a benchmark portfolio. The benchmark portfolios are 25 Fama-French value-weighted portfolios. β2 is the coefficient we are interested in. All other variables are defined as in Appendix I. The explanatory variables except leverage are standardized by lagged market equity. Regressions are estimated using OLS. Statistical significance is computed using heteroskedasticity and autocorrelation robust standard errors that are clustered at the firm level.

Coefficient t-stat

TIMELINESSi,t-1 -0.015 (-0.46)

ΔIi,t *TIMELINESSi,t-1 0.025** (2.41)

ΔIi,t 0.817*** (3.87)

ΔCashi,t 2.088*** (7.12)

ΔIncomei,t 2.553*** (11.46)

ΔRDi,t 3.228*** (5.28)

ΔDividendi,t 0.771*** (4.18)

ΔInteresti,t -0.912** (-1.99)

Cashi,t-1 0.939*** (8.25)

Levi,t 0.059 (0.37)

Netfinancei,t -0.057* (-1.72)

Cashi,t-1*ΔCashi,t -0.188*** (-2.69)

Levi,t*ΔCashi,t 0.325 (0.78)

N 40,312

Adjusted-Rsquare 0.088

Note: *** , ** and * denote significance at the 1, 5, and 10% levels, respectively. Coefficient estimates are reported with t-statistics in parentheses.

46

Table 7. Additional Test: The Value of Accounting Conservatism in Capital Investment When Ex Ante Agency Cost Vary

This table reports the valuation test using the following model.

, 0 1 , 2 , , 3 , 4 ,

5 , 6 , 1 7 , 8 , 9 , 10 , 1

11 , 1 , ,

*

*

i t i t i t i t i t i t

i t i t i t i t i t i t

i t i t i t

ret TIMELINESS TIMELINESS I I Div

Cash Cash Earn NetFin Int Lev

Lev Cash YFE IFE

The dependent variable reti,t is stock return over fiscal year minus the return on a benchmark portfolio. The benchmark portfolios are 25 Fama-French value-weighted portfolios. β2 is the coefficient we are interested in. All other variables are defined as in Appendix I. All explanatory variables except leverage are standardized by lagged market equity. Regressions are estimated using OLS. Statistical significance is computed using heteroskedasticity and autocorrelation robust standard errors that are clustered at the firm level.

(1) Size-Small

(2) Size-Large

(5) Analyst-Low

(6) Analyst-High

(6) BTM-Small

(5) BTM-Large

(7) FCF-Small

(8) FCF-Large

TIMELINESSi,t -0.022 (-0.91)

-0.001 (-0.21)

0.000 (0.04)

-0.000 (-0.46)

-0.000 (-0.46)

0.001 (0.26)

-0.000 (-0.93)

-0.001 (-1.23)

ΔIi,t *TIMELINESSi,t 0.041*** (2.51)

-0.002 (-0.06)

0.114** (2.32)

0.011 (0.41)

0.033* (1.81)

0.013 (1.15)

0.001 (0.05)

0.044** (2.26)

ΔIi,t 2.254*** (7.33)

0.998*** (2.64)

2.926*** (5.69)

0.956*** (2.92)

6.562*** (5.52)

0.469*** (3.35)

2.012*** (6.46)

1.526*** (3.70)

ΔCashi,t 2.441*** (6.26)

3.471*** (9.29)

2.578*** (5.87)

3.093*** (7.78)

7.123*** (8.07)

0.829*** (5.22)

2.732*** (7.88)

2.018** (6.12)

ΔIncomei,t 2.016*** (12.75)

3.372*** (13.01)

3.244*** (8.87)

3.245*** (15.35)

6.328*** (10.15)

0.895*** (11.44)

2.458*** (11.00)

2.413*** (11.89)

ΔRDi,t 3.667*** (9.26)

5.645** (2.35)

4.616 (1.50)

1.986 (1.23)

16.436*** (4.14)

-0.055 (-0.07)

4.855** (3.40)

7.672*** (3.14)

ΔDividendi,t 9.226*** (3.13)

6.607** (2.55)

10.151*** (3.50)

6.528** (2.26)

-2.629*** (-0.39)

3.555** (2.42)

9.177*** (2.78)

9.955*** (4.75)

ΔInteresti,t -0.751 (-0.76)

-4.264** (-2.47)

-1.875 (-0.83)

-2.322 (-1.40)

-1.078 (-0.28)

-0.497 (-0.85)

1.137 (0.83)

-4.957*** (-3.42)

Cashi,t-1 0.529*** (4.23)

1.309*** (5.96)

0.554*** (2.81)

1.262*** (5.76)

4.179*** (8.37)

0.254*** (3.59)

1.051*** (6.84)

0.659*** (3.85)

Levi,t -0.011 (-0.09)

-0.107 (-1.01)

0.364* (1.70)

-0.151 (-1.48)

0.116 (0.57)

-0.056 (-0.69)

-0.113 (-0.85)

-0.259* (-1.86)

Netfinancei,t -0.049* (-1.81)

-0.055* (-1.69)

-0.054 (-1.45)

-0.069* (-1.78)

-0.033 (-1.28)

-0.057* (-1.82)

-0.046* (-1.77)

-0.052 (-1.62)

Cashi,t-1*ΔCashi,t -0.019*** (-3.00)

0.175*** (0.83)

-0.024** (-2.01)

0.034 (0.83)

-0.010 (-1.09)

-0.003 (-0.76)

-0.007 (-0.86)

-0.259* (-1.86)

Levi,t*ΔCashi,t 0.074 -0.672 0.568 -0.248 -0.313 0.283 0.249 -0.020**

(0.20) (-0.74) (0.93) (-0.40) (-0.20) (0.94) (0.91) (-2.44)

N 13,421 13,443 13,462 13,499 13,448 13,437 11,013 11,022 Adjusted-Rsquare 0.057 0.094 0.049 0.086 0.101 0.044 0.070 0.087

Note: *** , ** and * denote significance at the 1, 5, and 10% levels, respectively. Coefficient estimates are reported with t-statistics in parentheses.