INVESTMENT OPPORTUNITIES AND THE VALUE-RELEVANCE

OF EARNINGS, CASH FLOWS AND ACCRUALS

Gopal V. Krishnan Department of Accountancy

City University of Hong Kong, Kowloon, Hong Kong acgk@cityu.edu.hk

and

Krishna R. Kumar School of Business and Public Management

The George Washington University, Washington, DC 20052, USA kkumar@gwu.edu

June 2003 (Work still in progress, please do not quote)

Address correspondence to: Krishna R. Kumar Department of Accountancy School of Business and Public Management The George Washington University Washington DC 20052 Phone: (202) 994-5976 E-mail: kkumar@gwu.edu We gratefully acknowledge helpful comments and suggestions from Steve Christophe, Chris Jones, Sok-Hyon Kang, Fred Lindahl, James Livingstone, Jim Patton, Kumar Visvanathan, and especially Bill Baber. We also thank participants for comments at the accounting research workshop at The George Washington University, and at conferences of the Washington Area Finance Association, European Accounting Association and the American Accounting Association.

Investment Opportunities and the Value-Relevance of Earnings, Cash Flows and Accruals

Abstract

(Under construction) Data Availability: All data are publicly available. Keywords: Capital markets; Investment opportunities; Earnings; Cash flows from operations; Accruals; Value Relevance

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Investment Opportunities and the Value-Relevance of Earnings, Cash Flows and Accruals

I. Introduction

Investment (growth) opportunities (IOS) are, for many firms, a substantial and valuable

component of corporate assets. Frequently, they represent a greater part of a firm’s potential for

competitive advantage, abnormal profits and value creation than assets-in-place.1 Thus,

investors have a strong need for information about investment opportunities, the assessment of

their value and the likelihood of their realization.

Evidence is mixed on the usefulness of financial information for firms with investment

opportunities. Using the ratio of market-to-book-value of equity (MB ratio) as a growth

opportunities proxy, Collins and Kothari (1989) argue that earnings response coefficients (ERCs)

increase with investment opportunities. On the other hand, Ahmed (1994) uses a proxy based on

investment in research and development (R&D) expenditures to demonstrate that ERCs decline

with growth opportunities. In the context of the wireless communications industry, Amir and

Lev (1996) find that non-financial information (market potential and market penetration)

dominates financial information [earnings and cash flows from operations (CFO)] in determining

security prices for high-growth firms. On a stand-alone basis, they find financial information to

be uninformative. Finally, Bodnar and Weintrop (BW) (1997) document higher ERCs for

foreign earnings of US multinationals and make the case, using realized sales growth rates as the

proxy for growth prospects, that ERCs are higher for foreign operations due to higher growth

opportunities.

In this paper, we reexamine relations between investment opportunities and the value-

relevance of earnings while addressing a variety of limitations in prior studies. A distinguishing

feature of our study is a specification that allows ERCs to vary non-linearly with IOS. This

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approach considers the possibility that the inconsistent findings across previous studies are due

in part to the fitting of linear models to samples that represent different segments of the IOS

spectrum. We also use a substantially larger sample, a more comprehensive and better-validated

measure of investment opportunities and a measure of unexpected earnings that considers

transitory earnings. Our results support a characterization wherein ERCs first increase and then

decrease as IOS increases.

As noted earlier, prior research offers opposing predictions for the behavior of ERCs as

IOS increases. CK argue that if current earnings provide useful information to investors about

the extent of abnormal profits in current and future investments, and if such abnormal profits

increase with investment opportunities, then ERCs will increase with IOS. On the other hand,

Ahmed (1994) and Amir and Lev (1996) argue that accounting conservatism, by proscribing the

recognition of growth options on the balance sheet and requiring the immediate expensing of

investments in R&D, advertising and other costs of intangible assets, compromises the

informativeness of earnings as IOS increases and causes ERCs to decline. Yet other arguments,

which we outline below, suggest that return responses to the CFO component of earnings may

either increase or decrease as IOS increase. Our empirical results suggest that factors that cause

ERCs to increase with IOS dominate for relatively low IOS values and then ERC-decreasing

factors dominate for higher IOS values.

Next, we extend the analysis by decomposing earnings into CFO and accruals to

investigate how security returns respond to each of these earnings components as IOS increases.

We are motivated by the fact that theoretical predictions for security return responses for each

component as IOS increases differ from the other. On the one hand, increasing cost differentials

between internal and external financing as IOS increases, attributable to information

3

asymmetries, agency costs and transaction costs, can cause CFO to be an increasingly important

determinant of whether or not investment opportunities are realized. Consequently, security

price responses to CFO – denoted as the cash flow response coefficient (CRC) – can increase

with IOS. CRCs can also increase with IOS if, as suggested by CK, CFO – as a component of

earnings – inform investors about abnormal earnings in current and future investments. On the

other hand, the usefulness of CFO as a performance measure is potentially compromised by the

deduction from CFO of investment outlays for R&D, and other intangible-asset-building

expenditures dedicated to the development and realization of investment opportunities. Which

of these effects dominates at various levels of IOS and how the net effect varies with IOS is an

empirical question, which we address in this paper.

Dechow (1994) argues that timing and matching problems in CFO increase with the

volatility of working capital and cause working capital accruals to play an increasing role in the

usefulness of earnings. She suggests that the informativeness of working capital accruals

increases with the length of the operating cycle and the level of working capital. In the present

context, her analysis suggests that the usefulness to investors of working capital accruals is likely

to decline as IOS increases. This is because as IOS increases, operating assets, and more

specifically working capital, are a decreasing proportion of total assets (including investment

opportunities). Furthermore, the usefulness of non-current accruals is also likely to decline as

IOS increases. This is because, many non-current assets, particularly intangibles, are not

recorded on the balance sheet because of accounting conservatism. Others, such as goodwill and

purchased intangibles, are often amortized at arbitrary rates (Ahmed 1994, Amir and Lev 1996).

Thus, we expect security return responses to accruals – denoted as the accrual response

coefficient (ARC) – to decline as IOS increases.

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As a further test of our predictions, we examine whether CRC and ARC sensitivity to

IOS varies with the nature of assets acquired – tangibles such as inventory, receivables, land,

building and equipment versus intangibles such as intellectual property, knowledge and brand

power – in order to realize investment opportunities. Following Smith and Watts (1992), we

expect that information asymmetries and agency costs are lower for tangible assets. We also

expect that the usefulness of both working capital and non-current accruals is greater when

tangible assets are acquired. Thus, we expect that as IOS increases, the informativeness of CFO

increases and that of accruals decreases at a faster rate for firms realizing investment

opportunities through intangible rather than tangible investments. Using the ratio of R&D-plus-

advertising expense to capital-expenditures as a proxy for the relative intensity of investment in

intangible assets, we document that CRC sensitivity to IOS increases at a faster rate and ARC

sensitivity to IOS declines at a faster rate when investment opportunities are realized to

intangible investments rather than tangible investments.

This study contributes in several ways to research on how investment opportunities

influence security price responses to earnings and its components. First, it reconciles the

conflicting results in prior research by using a more general model specification and addressing

limitations, in order to provide a more complete understanding of how earnings informativeness

varies with investment opportunities. Second, it extends current research by considering how the

value-relevance of two important earnings components – CFO and accruals – varies with IOS.2

This evidence is potentially of value to investors and analysts in determining which earnings

component – CFO or accruals – to emphasize when they evaluate high-IOS versus low-IOS

firms. Third, the paper contributes to research that looks beyond earnings in studying the

valuation of high-growth firms. Whereas, Amir and Lev (1996) demonstrate the importance of

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non-financial information, the present study shows how the usefulness of important elements of

the financial information set varies as IOS increases. A standard-setting implication of our

findings is that investors in large-IOS firms may benefit from more detailed disclosures on

operating cash flows. At the same time, new standards for large-IOS firms, designed to generate

more value-relevant accruals, may be called for. Finally, the paper considers a number of

economic explanations for why the value-relevance of CFO increases and that of accruals

decreases as IOS increases and provide evidence on them.3

The next section of the paper discusses prior studies and how this paper addresses several

of their limitations. It also presents potential explanations for how the informativeness of

earnings, CFO, and accruals varies as IOS increases. Section three presents the research design

and empirical methods. Sample selection, data sources and variable measurement are addressed

in section four. Results are reported in section five. Conclusions are in section six.

II. Prior research, theory and motivation

Prior Research Studies investigating how security return responses to earnings vary with investment

opportunities typically adopt model specifications of the following form. Inferences are based

on coefficient a2.

itn

itnitititit iablescontrolaUEIOSaUEaaCAR ε+×+×++= ∑ var_210 (1)

where

CARit = the cumulative abnormal returns during the disclosure period in which performance-related information for firm i for period t is disclosed;

UEit = the unexpected earnings relative to expectations for firm i at the beginning of the disclosure period;

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IOSit = investment opportunities for firm i in period t; control_variablesit = variables to control for potentially correlated omitted effects; a0, a1, a2, …, an are parameter estimates; and γ is the error term.

CK use earnings changes and the market-to-book-value-of-equity ratio to proxy for

unexpected earnings and growth opportunities respectively. They limit their sample to NYSE-

listed firms with a minimum of three years of data. Their research design suffers from three

limitations. First, the random walk specification for earnings expectations assumes that all

earnings surprises are permanent. Ali and Zarowin (1992) show that estimated ERCs are biased

if a random walk specification is used when earnings have transitory components and

recommend inclusion of earnings levels as an additional proxy for unexpected earnings in such

instances. If earnings persistence is correlated with growth opportunities, then the use of a

random walk specification can result in a biased coefficient a2 in expression (1). This is because

measurement error in unexpected earnings, and the resulting bias in ERC, varies with IOS. A

second limitation of CK is the use of the MB-ratio as a proxy for growth opportunities. Ahmed

(1994) notes that the increase in ERC with IOS documented by CK may be driven by

associations between MB ratios and expected returns. Third, CK’s the selection criteria

potentially bias the sample in favor of large, successful firms. Their exclusion of NASDAQ-

listed firms may imply that nascent, high-growth companies are underrepresented in the sample.

Similar limitations characterize BW. First, they too use the random walk specification

for unexpected earnings. Second, they limit their sample to firms with multinational operations

for which a minimum of five years of stock returns are available. By excluding firms that have

domestic operations only or have existed for less than five years, these criteria also likely cause

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the sample to under-represent small firms with high-growth potential. Finally, they use historical

sales growth rates to proxy for future growth expectations. Evidence in Baber, Janakiraman and

Kang (1996) suggests that relative to several other measures identified by them, past revenue

growth rate is a poor predictor of future growth rates.

The Amir and Lev (1996) study, being limited to the wireless communications industry,

focuses on a small, high-growth segment of the investment opportunities spectrum. Thus, their

findings about the lack of usefulness of financial information may not be generalizable even to

other high-growth industries and may be attributable to their small sample size. They do,

however, address the problem of potential measurement error in earnings changes as proxies for

unexpected earnings and CFO by including levels as additional explanatory variables in their

specifications.

Recognizing the possibility that the observed positive association between ERCs and the

MB-ratio may be attributable to relations between the MB-ratio and expected returns, Ahmed

(1994) uses a proxy comprised of non-market measures – R&D expenditures and the

replacement cost of plant, property and equipment. However, he provides no evidence to

validate this measure as a proxy for future growth opportunities. Furthermore, Ahmed uses the

seasonal random walk model to proxy for unexpected earnings. Thus, his results for the ERC-

IOS relations can also biased because of measurement error in unexpected earnings. Finally, he

limits his sample to manufacturing firms. If manufacturing firms enjoy substantially different

levels of growth opportunities relative to service and other non-manufacturing industries, then

Ahmed’s findings may be biased, especially if the nature of ERC-IOS relations varies with the

level of IOS.

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How this study addresses limitations in prior studies

Three features of this study are designed to address the limitations identified above.

First, we require firms to have only two consecutive years’ data on Compustat and CRSP files in

order to be included in our sample. We include all firms with sufficient data except firms in

regulated industries – utilities (SIC 49) and financial institutions (SIC 60). We exclude these

industries in order to be consistent with Baber et al. (1996), whose measure we use to estimate

investment opportunities. Thus, our sample selection process is less restrictive than prior studies

and hence is more likely to include small, young firms.

Second, our investment opportunities measure, originally developed by Baber,

Janakiraman and Kang (1996), combines four commonly-used growth opportunities metrics –

the ratio of the market to book value of assets, firm growth rate, R&D intensity and investment

intensity. Two key features of this measure are noteworthy. First, the four underlying metrics

are similar to those used in previous studies investigating ERC-IOS relations. Specifically, the

market to book value of assets is similar to the market to book value of equity ratio used in CK.

The R&D and investment intensity measures are similar to the R&D stock measure used by

Ahmed (1994) and the asset growth measure is similar to the revenue growth measure used in

BW. Second, these growth measures are not selected arbitrarily, but rather, through a step-wise

regression process that identifies them from a set of sixteen IOS metrics, based on their ability to

predict the intensity of investment activity over the following five years. This validation process

gives us greater confidence in our growth proxy than in those used in prior studies. We discuss

this measure in greater detail in the following section.

Finally, we use both earnings changes and levels as proxies for unexpected earnings.

Several prior studies recommend the inclusion of earnings levels as an explanatory variable in

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addition to earnings changes to mitigate bias in the estimated ERC when earnings are transitory

(Ali and Zarowin, 1992; Brown et al., 1987; Easton and Harris, 1991). In subsequent analysis,

we use changes and levels of CFO and accruals to proxy for unexpected CFO and accruals when

we decompose unexpected earnings into these two components to examine the behavior of CRCs

and ARCs as IOS increase. As noted earlier, measurement error in the unexpected earnings

proxies can be correlated with IOS if persistence and IOS are correlated. If so, the use of

changes and levels can reduce measurement error and mitigate bias in relations between response

coefficients and IOS due to such error.

Predictions about how ERCs vary with IOS

A number of factors potentially affect how ERCs vary with IOS. First, CK argue that

current period abnormal earnings can help investors assess the potential for abnormal earnings

from future investments and hence the value of future investment opportunities. If the potential

for above normal earnings is greater from investment opportunities than from assets already in

place, then we expect ERCs to increase as investment opportunities increase relative to assets in

place. Above normal earnings potential can be lower for assets-in-place if competition erodes

their opportunities for such earnings more rapidly.

Second, firms need cash in order to exploit investment opportunities. Such cash needs

can be met from internal resources or through external financing. Under perfect and complete

markets, such financing choices do not affect investment decisions and therefore are not relevant

to firm value (Modigliani and Miller, 1958). However, market frictions such as information

asymmetries, agency costs and transaction costs cause the cost of external capital to be higher

than that from internal sources (Jensen and Meckling, 1976; Myers and Majluf 1984; Oliner and

Rudebusch 1992). In the presence of such cost differentials, the availability of internal

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resources to finance investment opportunities increases firm value. This happens for at least two

reasons. First, investment projects that have positive net present values (NPVs) when financed

externally have even higher NPVs when lower-cost internal resources are used instead. Second,

some of the investment opportunities that are not viable (that is, have negative NPVs) if financed

externally become viable positive NPV projects when financed with lower cost internal funds.

We expect differences between the costs of internal and external financing to increase as

investment opportunities increase. This is because, holding internal financing constant, the level

of external financing increases as investment opportunities increase. External financing of

investment opportunities is typically not collateralized; therefore, the marginal cost of such

financing increases with the level of financing (Kaplan and Zingales 1997, p. 174; Hubbard

1998, p. 197). On the other hand, the opportunity cost of internal financing, being determined by

external credit and equity market rates, is unrelated to investment opportunities. If cost

differentials between internal and external financing increase with investment opportunities,

then, the marginal benefits – that is, the contribution to firm value – of an additional dollar of

internal financing increases with investment opportunities.

CFO is an important internal cash resource. Therefore, it follows from the above

discussion that unexpected changes in CFO can cause investors to reassess the extent that lower

cost internal resources are available to realize marginal investment opportunities or to substitute

for higher cost external capital, and hence, to revise stock prices. Such revisions will correlate

positively with unexpected CFO. Moreover, holding unexpected CFO constant, such

reassessments should be greater for large-IOS firms than for small-IOS firms. In other words,

stock price responses to unexpected CFO are predicted to increase with IOS.

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However, as noted in the introduction, deductions from CFO of investment outlays for

R&D, advertising and other expenditures that potentially create intangible assets adds

measurement error to reported CFO. Lack of detailed disclosures about such expenditures makes

it difficult for investors to add back the deductions to CFO. As such outlays are likely to

increase with investment opportunities, we expect them to increasingly compromise the

informativeness of CFO as IOS increases.

Together, these two arguments suggest that, to the extent that earnings consist of CFO,

financing cost differentials cause ERCs to increase with IOS and mismeasurement of CFO

causes ERCs to decrease as IOS increases.

Accruals also potentially affect how ERCs vary with IOS. While there is strong

empirical evidence indicating that accruals are highly value-relevant for the average firm (Bowen

et al. 1987, Bernard and Stober, 1989), there is reason to believe that this property varies with

IOS. Dechow (1994) observes that accruals enhance the value-relevance of earnings by

addressing matching and timing problems in operating cash flows. This is achieved by accruing

non-cash assets and liabilities on the balance sheet from the date of the cash flows to the date on

which revenues or expenses are recognized. However, accruals appear to be most effective in

addressing matching and timing problems for investments in tangibles such as working capital,

and plant, property and equipment. This is because measurements involved in recording accruals

for such assets appear to be subject to less error than for intangibles.

Firms create investment opportunities through investment in specialized physical,

knowledge, brand and human capital (Smith and Watts 1992). A significant portion of such

investment – such as on research and development, advertising, market development, and

employee education and training – is intangible in nature. Even tangible assets used in such

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contexts, for example, laboratory equipment, are likely to be highly specialized with few

alternative uses. Problems in identifying the amounts and timing of the returns on such

investments imply that the accrual and allocation across time-periods of the costs of such

investment can be quite arbitrary and subject to error and manipulation. Accounting standard-

setters have responded conservatively to these problems by requiring the immediate expensing of

such investments, particularly when they are intangible. Even in the instances when costs are

capitalized, as with tangible assets or purchased intangibles, the depreciation or amortization of

costs can be arbitrary. Both tangible assets and intangibles such as patents, copyrights or

trademarks may be impaired by competition or obsolescence long before the end of their

estimated useful lives. Alternatively, brand equity and market penetration achieved through such

investments can last for many years after the assets have been fully depreciated or amortized on

the balance sheet. Thus, estimates used in measuring accruals can be subject to considerable

error for firms investing to create investment opportunities. Therefore, the value-relevance of

accruals, and consequently ERCs, are likely to decline as investment opportunities increase

relative to assets-in-place.

We have identified several factors that potentially determine how ERCs vary with IOS.

Some are expected to cause ERCs to increase and others to cause them to decrease as IOS

increases. Prior studies have focused on one or a few of these factors and predicted monotonic

trends in ERC as IOS increases. However, when several factors are present, such trends are

likely only if one set of factors, either ERC-increasing or ERC-decreasing, dominate the other

over the entire range of IOS. If the dominant effect(s) vary with IOS, non-monotonic patterns

are likely to arise. Alternatively, if factors are uniformly offsetting across all IOS, no trend will

13

be detected. The inconsistent results of prior studies suggest that multiple factors influence how

ERCs vary with IOS.

Although we are unable to make unambiguous predictions about how ERCs vary with

IOS, it is clear from our discussions that the trend in security return responses to CFO as IOS

increases differs from that of return responses to accruals as IOS increases. The two factors that

influence how CRCs vary with IOS – the role of CFO as an internal resource and the

measurement error in CFO due to the inclusion of R&D and other expenditures – appear to have

opposing effects, and the net trend in CRCs as IOS increases remains an empirical question. On

the other hand, our discussion unambiguously predicts a decline in ARCs as IOS increases.

III. Research design, empirical proxies and model specifications

The primary ERC specification

Our primary empirical specification to investigate how ERCs vary with IOS follows

expression (1) in section II. Year and firm subscripts are suppressed.

CAR = b0 + b1 ∆E + b2 E + b3 IOS + b4 ∆E×IOS + b5 E×IOS + b6 ∆E×IOS2

+ b7 E×IOS2 + ∑C

bC control variableC + ∑Y

bY DY + ∑I

bI DI + ∈b (2)

where

CAR = the market-model-based cumulative residual stock return relative to the value-weighted NYSE, AMEX, NASD market index, aggregated over the one-year period beginning with the fourth month of the current fiscal year t; market model parameters are estimated over 250 trading days preceding the annual return window;

∆E (Earnings change) = change in reported income before extraordinary items (Compustat annual data item #18) from the previous fiscal year t-1 to the current year t, scaled by the beginning of period market value of equity (Compustat annual data item #24×#25); E (Earnings level) = reported income before extraordinary items for the current year t, scaled by the beginning of period market value of equity;

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IOS = a measure of investment opportunities estimated as in Baber, Janakiraman and Kang (1996) from a factor analysis of the variables listed in Exhibit 1;

control variablesC = BETA, PERSIST and INTRATE and their interactions with ∆E and E; where BETA is a measure of systematic risk, estimated as the slope coefficient from a market-model regression of daily stock returns on the value-weighted NYSE, AMEX, NASD market index return over the one-year window beginning the fourth month of the fiscal year; PERSIST is a measure of earnings persistence, specified an indicator variable that takes the value of one when the earnings-to-price (Et-1/Pt-1) ratio at the beginning of fiscal year t is in the extreme four sample deciles and zero otherwise; INTRATE is the average 30-year treasury bond yield for year t; DY = six year dummy variables for 1990 through 1995; DI = twelve industry dummy variables representing 2-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 50 and 73;

b0, b1, b2, …, bI are parameter estimates; and ∈b is the error term.

Unexpected earnings proxies: Expression (2) includes current period earnings changes

∆E and earnings levels E as empirical proxies for unexpected earnings UE in expression (1).

Prior research, with the exception of Amir and Lev (1996), has typically assumed a random-walk

annual earnings process and used earnings changes only as the unexpected earnings process.

Following, Ali and Zarowin (1992), Brown et al. (1987) and others, we include earnings levels

as an additional unexpected earnings proxy to address measurement error in earnings changes

when earnings innovations are partly transitory. ERCs are estimated as the sum of the

coefficients on the two proxies.

Quadratic terms: We also include quadratic interaction terms ∆E×IOS2 and E×IOS2 to

allow the specification to reflect potential non-linear relations between ERCs and IOS. The

discussion in the previous section indicates that ERCs may increase or decrease with IOS

15

depending on which of several factors dominate. The inclusion of a quadratic term allows for

the possibility that the dominance of one or other factor may vary with the level of IOS.

IOS measure: Earlier, we noted several limitations of investment opportunities measures

used in prior studies. These included concerns that they may proxy for correlated omitted

variables (as in the case of the MB ratio), that they are weakly related to future realized growth

(as in the case of historical revenue growth rates) or that there is a lack of evidence to validate

them as predictors of future growth [as in the case of Ahmed’s (1994) R&D stock measure]. We

address such concerns by using an investment opportunities measure developed by Baber et al.

(1996). This measure is obtained by identifying through step-wise regression, a parsimonious set

of four growth opportunities proxies from an initial list of sixteen commonly-used variables.

These four measures are them aggregated into one using factor analysis and then validated by

demonstrating strong associations with future realized growth measures.4 This measure has

several advantages. First, the process of aggregation through factor analysis should help reduce

error in measuring the underlying investment opportunities construct relative to any individual

proxy. Second, the measure uses a parsimonious set of variables and a short time-series of data,

thus, minimizing loss of observations due to missing data Third, growth proxies included in the

measure include growth measures commonly used in previous research. Finally, the validation

of the measure by estimating correlations with realized future investment intensity and growth

rates gives us confidence about the construct validity of the measure. The four measures that

make up IOS are recent investment intensity (INVINT), recent asset growth rate (MVAGR), the

ratio of market-to-book values of assets (MKTBKASS), and R&D intensity (R&D). Definitions

of these variables are in Exhibit 1.

[Insert Exhibit 1 about here]

16

Factor loadings used to combine the four components into a single score can vary over

time and Baber et al.’s loadings may not apply to our data. Hence, we re-estimate loadings. The

estimation process yields an IOS mean (standard deviation) across all observations equal to zero

(one) by construction.5

Control variables: In addition to growth opportunities, prior research identifies risk,

earnings persistence and interest rate as determinants of earnings response coefficients

(Kormendi and Lipe 1987, Easton and Zmijewski 1989, Collins and Kothari 1989). If these

variables are correlated with growth opportunities, any documented associations between ERCs

and IOS may be spurious. We address such concerns by including BETA, PERSIST and

INTRATE as proxies for risk, earnings persistence and interest rates. Note that the interactions of

these variables with unexpected earnings measures )E and E must enter the specification in order

to control for the possibility that observed ERC variations with IOS are driven by these other

correlated determinants of ERCs. We follow CK in using systematic risk (Beta) as the measure

of risk and long-term government bond-yields to proxy for risk-free interest rates. We adopt Ou

and Penman’s (1989) binary measure of persistence based on price-earnings ratios because,

unlike alternative earnings time-series based measures, it uses only current data and does not

require a long time-series of earnings.

We include year and industry dummies in the specifications as controls for the mean

effects of any remaining correlated omitted variables and to address residual cross-correlations in

security returns that can bias test statistics (Bernard 1987).

Specification to investigate return responses to CFO and accruals

We examine the separate security return responses to CFO and accruals as IOS increases,

by disaggregating earnings changes and levels in expression (2) into CFO and accrual changes

17

and levels in expression (3). Note that the control variables in expression (3) include interactions

of BETA, PERSIST and INTRATE with changes and levels of CFO and accruals instead of

changes and levels of earnings as in expression (2).

CAR = c0 + c1 ∆CFO + c2 CFO + c3 ∆ACC + c4 ACC + c5 IOS + c6 ∆CFO×IOS

+ c7 CFO×IOS + c8 ∆ACC×IOS + c9 ACC×IOS + c10 ∆CFO×IOS2

+ c11 CFO×IOS2 + c12 ∆ACC×IOS2 + c13 ACC×IOS2

+ ∑'C

cC’ control variableC’ + ∑Y

cY DY + ∑I

cI DI + ∈c (3)

where

∆CFO (CFO change) = change in cash flows from operations (Compustat annual data item # 308) from the previous fiscal year t-1 to the current year t, scaled by the beginning of period market value of equity; CFO (CFO level) = cash flows from operations for the current year t, scaled by the beginning of period market value of equity; ∆ACC (Accruals change) = change in accruals from the previous fiscal year t-1 to the current year t, scaled by the beginning of period market value of equity; accruals are defined as net income before extraordinary items minus cash flows from operations; ACC (Accruals level) = accruals for the current year t, scaled by the beginning of period market value of equity; control variablesC’ = BETA, PERSIST and INTRATE and their interactions with ∆CFO, CFO, ∆ACC and ACC;

c0, c1, c2, …, cI are parameter estimates; ∈c is the error term; and

other variables are as defined in expression (2).

In addition to being a natural extension of the use of earnings changes and levels as

proxies for unexpected earnings, our use of CFO and accrual changes and levels as proxies for

18

unexpected CFO and accruals is motivated by two observations from prior studies. First,

Dechow (1994) documents large negative first-order serial correlations for both CFO and

accruals, thus indicating substantial mean reversion, and hence, the presence of transitory

elements in these earnings components. Second, Pfeiffer and Elgers (1999) demonstrate that the

inclusion of current and lagged levels – a procedure that is equivalent to the inclusion of changes

and levels – reduces bias in the estimated response coefficients.

IV. Sample selection and descriptive statistics For our sample firms, we require stock return data on the CRSP daily-returns file for at

least one complete year beginning the fourth month of a fiscal year in 1990 through 1996, and

for 250 trading days preceding each such year. We also require financial data to be available on

Standard and Poor’s Compustat. We begin with fiscal 1990 so that CFO is reported under

SFAS 95 for all firms in our sample.6 Following Baber et al. (1996) and Gaver and Gaver

(1993), we exclude firms in regulated industries, in particular, utilities (SIC 49) and financial

institutions (SIC 60). We also exclude firm-years with extreme values for IOS components,

INVINT, MVAGR, MKTBKASS and R&D, defined as values greater than 100, 5, 30, and 1,

respectively. The objective is to exclude observations for which IOS is likely to have large

measurement error. Following Easton and Harris (1991) and Cheng et al. (1996), we exclude

observations with values greater than 1.50 or less than -1.50 for earnings and CFO changes and

levels scaled by beginning of period market value of equity. After deleting outliers identified

using Belsley, Kuh and Welsch (1980) diagnostics, 18,108 observations remain in our sample.

Data are adjusted for stock dividends and splits.

[Insert Tables 1 and 2 about here]

19

Descriptive statistics are in Panel A of Table 1. Pair-wise correlations are in Panel B.

Comparisons of sample statistics with Dechow (1994) indicate somewhat lower earnings and

CFO levels (lower means and medians) for our sample observations.7 The 2-digit SIC Industry

distribution for the sample is in Table 2.

V. Results

The primary ERC specification

Table 3 presents results for two restricted versions of expression (2) [Models 1 and 2] and

the full version [Model 3]. Model 1 includes earnings changes only (∆E), as a main effect and in

interactions with IOS and the control variables BETA, PERSIST and INTRATE. In addition to

these variables, Model 2 includes earnings levels (E) and its interactions with IOS and the three

control variables. Neither model includes ∆E interactions with IOS2. We present these

specifications for comparison with CK and other prior studies, and to demonstrate the

implications for observed ERC-IOS relations of using earnings changes as unexpected earnings

proxies and specifying such relations as linear. All three models include year and industry

dummy variables as in expression (2).

In Model 1, the coefficient of ∆E indicates the ERC for firms with zero values for IOS.

Since zero is the mean value of IOS by construction, these are firms with average investment

opportunities (see section III). Coefficient α1 indicates that ERC is positive and significant (p ≤

0.05) for the average firm, consistent with a large volume of prior evidence. Coefficient α4 on

the ∆E×IOS interaction suggests that ERCs increase with IOS, consistent with CK and BW.

However, this result – that ERCs increase with IOS – is not replicated when earnings

levels E and interactions are included in the specification in Model 2. In this specification, the

20

ERC of the average firm is indicated by the sum β1+β2 of the coefficients of the main effects ∆E

and E. The coefficient sum is 1.007, positive and significant (p ≤ 0.05), and about 18% larger

than the estimate from Model 1. The coefficient sum β4+β5 of the interaction terms ∆E×IOS and

E×IOS is negative (-0.025) but not statistically significant. Thus, the effect of including earnings

level E in the specification is to increase estimated ERCs by greater amounts for low-IOS firms

than for high-IOS firms. This suggests that the ERC estimates from Model 1 are biased

downwards to a greater extent for low-IOS firms than for high-IOS firms. This would be the

case if earnings have a larger transitory component for low-IOS firms; that is, if earnings

persistence and IOS are positively correlated. Observed correlations between earnings

persistence and IOS for our sample are indeed positive and significant (0.074, p ≤ 0.01), thus

validating our concern that the use of random-walk earnings expectation models biases estimated

relations between ERCs and IOS.

Model 2 imposes linearity, and hence, monotonicity on the estimated ERC-IOS relation.

However, our discussion in section III identifies some factors that potentially cause increases in

ERCs with IOS and others that cause declines. Unless one or the other set of factors dominate

over the entire range of IOS values, ERC-IOS relations are likely to be non-monotonic. In such

circumstances, a non-linear model specification is preferable, because a linear specification picks

up only the average trend across all observations and not the change in trend as the independent

variable (IOS) changes. Moreover, when the underlying functional relationship is non-linear, the

estimated slope from a linear model will vary across samples, to the extent that samples represent

different parts of the population.

Model 3, which follows expression (2), includes two interaction terms )EΗIOS2 and

EΗIOS2, which allow the ERC-IOS relation, as represented by the sum of the coefficients of

23

return responses to accruals decline as IOS increases, consistent with the premise that accrual

informativeness is increasingly impaired by conservatism and other accounting deficiencies as

IOS increases.

In Model 5, Ν1 + Ν2 indicates the CRC for the average (zero IOS) firm and has a value of

1.084, positive and significant (p ≤ 0.05), and comparable to the corresponding estimate in

Model 4. Similarly, Ν3 + Ν4, which indicates the ARC for the average firm, is 0.802 and is

comparable to the estimate from Model 4. Coefficients sums Ν6 + Ν7 and Ν8 + Ν9, representing

the rates of change of CRC and ARC with IOS, are positive and negative with values of 0.290

and –0.161 respectively. CRC increases with IOS while ARC decreases, as in Model 4.

Coefficients sums Ν10 + Ν11 and Ν12 + Ν13 indicate the rate of change of the CRC-IOS and the

ARC-IOS relations with IOS. The former is negative (-0.055) and significant (p ≤ 0.05), but the

latter is not significantly different from zero. Thus, the rate of increase in the sensitivity of

return reponses to CFO slows down as IOS increases but the rate of decline in return responses

to accruals does not change materially. The partial derivative of the expression (Ν1 + Ν2 +

Ν6IOS + Ν7 IOS + Ν10 IOS2 + Ν11IOS2) with respect to IOS indicates the rate at which the CRC-

IOS relation changes with IOS. Setting it equal to zero, we find that, ceteris paribus, CRCs

reach a maximum for IOS equal to about 5.3 and then decline. This value of IOS is in the top

one percentile of the IOS distribution for our sample. Thus, CRC-IOS relations increase with

IOS except for extremely high-IOS firms.

CFO and accrual informativeness and the nature of assets acquired

Table 5 reports results for Model 6. This model modifies expression (3) in two ways.

First, it includes a binary variable D as a main effect and in interaction with the unexpected CFO

measures, the unexpected accrual measures, the IOS measure and their interactions. D is coded

24

one for firms that have high levels of expenditures on intangibles relative to tangibles – defined

as the ratio of R&D and advertising expenditures to capital expenditures – and zero otherwise.

We specify D by partitioning observations with non-zero R&D-plus-advertising-expenses-to-

capital-expenditures ratio into three groups. D equals one for observations in the highest group

and equals zero for observations in the lowest group. Observations in the middle group are

excluded for this estimation. Finally, observations with zero R&D-plus-advertising-expenses-to-

capital-expenditures ratio are assigned D equal to zero. Thus, observations with D coded as one

have more intensive investments in intangibles than observations with D coded zero.

The full specification of the model is in Table 5. We do not include interactions with

IOS2 in this specification in order to mitigate multicollinearity. The coefficient sum 616 + 617 (3rd

column and 2nd row from the bottom of the table) is positive and significant (0.243, p ≤ 0.10),

indicating that CRCs increase at a faster rate with IOS for when investment is more intangible

intensive. The coefficient sum 618 + 619 (3rd column and last row) is negative and significant (-

0.264, p ≤ 0.05), indicating that ARCs decline at a faster rate with IOS for when investment is

more intangible intensive. Thus, the informativeness of CFO increases and that of accruals

declines at a faster rate for firms with more intangible intensive investments as IOS increases. If

information asymmetries and measurement error in accruals are greater for firms investing in

intangibles, then these findings lend additional support for our explanations for CRC-IOS and

ARC-IOS relations.

Conclusions

(Under construction)

25

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Easton, P. D., and T. S. Harris. 1991. Earnings as an explanatory variable for returns. Journal of Accounting Research 29: 19-36. Easton, P. D., and M. E. Zmijewski. 1989. Cross-sectional variation in the stock market response to accounting earnings announcements. Journal of Accounting and Economics 11: 117-141. Gaver, J. J. and K. M. Gaver. 1993. Additional evidence on the association between the investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Accounting and Economics 16: 125-160. Hubbard, R. G. 1998. Capital-market imperfections and investment. Journal of Economic Literature: 193-225. Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3: 305-360. Johnson, M. F. and D-W. Lee. 1994. Financing constraints and the role of cash flow from operations in the prediction of future profitability. Journal of Accounting, Auditing and Finance 9: 619-652. Kaplan, S. N. and L. Zingales. 1997. Do investment-cash flow sensitivities provide useful measures of financing constraints? The Quarterly Journal of Economics 112: 169-215. Kormendi, R. C. and R. Lipe. 1987. Earnings innovations, earnings persistence, and stock prices. Journal of Business 60: 323-345. Modigliani, F. and M. Miller. 1958. The cost of capital, corporation finance and the theory of investment. American Economic Review 48: 261-297. Myers, S. C., and N. S. Majluf. 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13: 187-221. Oliner, S. D., and G. D. Rudebusch. 1992. Sources of the financing heirarchy for business investment. The Review of Economics and Statistics 74: 643-654. Oppong, A. 1980. Information content of annual earnings announcements revisited. Journal of Accounting Research 18: 574-584. Ou, J., and S. Penman. 1989. Accounting measurement, price-earnings ratio, and the information content of security prices. Journal of Accounting Research 27: 111-144. Pfeiffer, R. J., and P. T. Elgers. 1999. Controlling for lagged stock price responses in pricing regression: An application to the pricing of cash flows and accruals. Journal of Accounting Research 37: 237-249.

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Smith, C., and R. Watts. 1992. The investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Financial Economics 32: 263-292.

29

TABLE 1 Descriptive Statistics and Correlations

(n=18,108) Panel A: Descriptive Statistics

Variable Mean Standard Deviation

Minimum 25% Median 75% Maximum

CAR -0.061 0.582 -1.000 -0.423 -0.146 0.158 4.563Ret 0.170 0.550 -0.945 -0.157 0.081 0.366 4.846∆E 0.012 0.180 -1.410 -0.026 0.008 0.039 1.496E 0.018 0.170 -1.478 -0.000 0.053 0.087 1.392∆CFO 0.011 0.192 -1.466 -0.041 0.008 0.061 1.490CFO 0.108 0.194 -1.393 0.023 0.091 0.173 1.494∆ACC 0.000 0.241 -2.214 -0.060 -0.004 0.051 2.450ACC -0.089 0.216 -2.351 -0.138 -0.048 -0.000 1.938IOS 0.004 0.997 -1.939 -0.579 -0.266 0.246 14.664

Panel B: Correlationsa

Variable CAR Ret ∆E E ∆CFO CFO ∆ACC ACC IOS CAR 1.000 0.609 0.149 0.077 0.100 0.145 0.031 -0.070 -0.139Ret 0.622 1.000 0.255 0.239 0.097 0.162 0.113 0.042 -0.064∆E 0.148 0.388 1.000 0.489 0.164 0.148 0.616 0.251 -0.033E 0.161 0.407 0.521 1.000 0.116 0.299 0.273 0.516 -0.056∆CFO 0.113 0.136 0.246 0.151 1.000 0.512 -0.675 -0.369 -0.007CFO 0.224 0.244 0.192 0.417 0.472 1.000 -0.298 -0.663 -0.242∆ACC 0.003 0.120 0.408 0.206 -0.639 -0.283 1.000 0.482 -0.018ACC -0.117 0.023 0.165 0.230 -0.369 -0.665 0.494 1.000 0.174IOS -0.180 -0.079 -0.056 -0.126 0.004 -0.334 -0.033 0.294 1.000

aPearson (Spearman) correlations are reported above (below) the diagonal. Neither correlation between the pairs, )CFO and IOS, and ∆ACC and CAR, is statistically significant at conventional levels. Both correlations are significant at the 0.05 level or better for all other pairs. Ret is the raw return for the one-year window beginning the fourth month of the fiscal year. )E

and )CFO are changes in earnings before extraordinary items (#18) and changes in cash flows from operations (#308) respectively for firm i in year t. E and CFO are the levels of earnings and cash flows from operations respectively for firm i in year t. )ACC (ACC) is computed as )E

minus )CFO (E minus CFO). Changes and levels of earnings, operating cash flows, and accruals are deflated by the beginning of period market value of equity (#24×#25). Compustat data item numbers are in parentheses. IOS is a measure of the size of a firm’s investment opportunity set and is estimated, as a common factor extracted from the four variables listed in exhibit 1, as in Baber et al. (1996).

30

TABLE 2 Industry distribution for sample firms

2-digit SIC codes Industry Number of firms 01-08 Agricultural and Forestry 19 10-14 Mining, including Oil and Gas Exploration 170 15-17 Construction 51 20-21 Food and Tobacco Products 100 22-23 Textile and Apparel 75 24-25 Lumber, Wood and Furniture Products 56 26-27 Paper, Printing, and Publishing 127 28 Chemicals and Allied Products 294 29 Petroleum Refining 32 30-31 Rubber, Plastic and Leather Products 68 32 Stone, Clay, Glass and Concrete Products 26 33 Primary Metal Industries 79 34 Fabricated Metal Industries 80 35 Industrial and Commercial Machinery including Computing Equipment 278 36 Electrical Machinery and Equipment 304 37 Transportation Equipment 88 38 Instruments 290 39 Miscellaneous Manufacturing 45 40-47 Transportation Services 99 48 Communication Services 94 50 Durable Goods – Wholesale 119 51 Non-Durable Goods – Wholesale 61 52-59 Retail – Various 290 61-62 Brokerage and Credit Services 77 63 Insurance 37 64-67 Other Financial Services 108 70 Hotels and Lodging Services 12 72 Personal Services 13 73 Business Services 281 75 Automobile Repair and Services 8 76 Miscellaneous Repair Services 4 78-79 Motion Pictures and Other Entertainment Services 63 80-87 Health, Legal, Educational, and Consultancy Services 165 99 Others 3 Total 3616

32

The specifications also include six year dummy variables DY for 1990 through 1995, and twelve industry dummy variables DI representing 2-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 50 and 73. Firm and year subscripts have been suppressed for all variables.

***, **, * indicate two-tailed significance at the 0.01, 0.05, and 0.10 levels for individual coefficients and coefficient sums.

36

the upper (lower) third are classified as having high (low) R&D+Advertising Expenses-to-Capital Expenditures ratios. Observations in the middle third are excluded. Compustat data item numbers are in parentheses. The specifications include control variables as described in Table 3. They also include six year dummy variables DY for 1990 through 1995, and twelve industry dummy variables DI representing 2-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 50 and 73. Firm and year subscripts have been suppressed for all variables. ***, **, * indicate two-tailed significance at the 0.01, 0.05, and 0.10 levels respectively for all individual coefficients and coefficient sums.

1 We follow Myers (1977) in viewing a firm as a combination of assets-in-place and investment

opportunities (or options).

2 Previous studies typically either do not investigate explanations for the value-relevance of

earnings components or only focus on CFO as an alternate signal for earnings. Examples of such

studies include Rayburn (1986), Bowen, Burgstahler and Daley (1987), Bernard and Stober

(1989) and Cheng et al. (1996).

3Some prior studies such as, Rayburn (1986), Bowen, Burgstahler and Daley (1987), Bernard

and Stober (1989) and Cheng et al. (1996) consider the value-relevance of CFO to investors.

However, with one exception, they do not examine the moderating effect of investment

37

opportunities, and typically explain the value-relevance of CFO in terms of its role as a

information signal that complements earnings and not as a source of internal cash for investing.

4 Specifically, the factor score computed for year t is highly correlated with t+1 through t+5

realized investment intensity, revenue growth and asset growth, where investment intensity and

asset growth rate are as defined in Exhibit 1 and revenue growth rate is defined similarly to asset

growth rate.

5As a check on whether our IOS measure captures differences across firms on each of the

underlying components, we partition the sample at the median IOS and test for differences across

the two subsets on the four components. All four test-statistics are statistically significant in the

predicted directions (p # 0.01).

6SFAS 95 requires adoption effective July 1988. Thus, 1989 is the first fiscal year for which all

firms in our sample report CFO under the new standard. We need CFO changes, therefore we

begin our analysis with fiscal 1990.

7Such evidence raises concerns that our findings are limited to periods of poor financial

performance. Later, in section 5.2.3, we discuss sensitivity tests designed to address such

concerns.