Does Analyst Forecasting Behavior Explain Anomalous ......to information in cash and accrual earnings components. Comparatively little prior research evaluates the efficiency of financial

Does Analyst Forecasting Behavior Explain Anomalous Stock Market Reactions to Information in Cash and Accrual Earnings Components?

Dana Holliea, Phil Shaneb, Qiuhong Zhaoc

a Louisiana State University b University of Virginia and University of Auckland

cUniversity of Missouri at Columbia

Abstract

This study examines whether analysts’ forecasting behavior can explain market inefficiencies with respect to cash and accrual earnings components. Specifically, we focus on whether analysts’ earnings forecasts impound persistence characteristics of the retained and distributed free cash flow components of cash, and the sales growth and efficiency change components of accruals. In a sample of relatively large firms in recent years, our evidence points toward generalized analyst underreaction across all earnings components, market underreaction to the distributed-to-debtholder component of cash, and market overreaction to the efficiency change component of accruals. Our findings are consistent with misunderstanding of the implications of leverage changes for earnings persistence. Our findings are also consistent with accounting distortions that fool investors but not financial analysts. While we find no evidence that analyst behavior explains market overreaction to accruals, we estimate that analyst behavior can explain 35% of market underreaction to the distributed-to-debtholder component of cash.

JEL classification: G14, M41

Keywords: Analyst Forecast, Accruals, Cash Flows, Market Efficiency

We thank the Leeds School of Business for the Hart Fellowship support in 2007. We thank I/B/E/S for providing the analysts forecast data for this study. We would also like to thank Katherine Gunny, Steve Rock and workshop participants at Colorado State University, the University of Colorado at Boulder, the 2008 Annual AAA meeting and our discussant Sudipta Basu for their helpful comments. Qiuhong Zhao thanks the Leeds School of Business for the Hart Summer Fellowship support. * Email addresses: [email protected] (D. Hollie), [email protected] or [email protected] (P. Shane), and [email protected] (Q. Zhao).

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I. Introduction

As information intermediaries financial analysts play an important role in conveying

information to the stock market.1

Building on prior research (e.g., Richardson et al., 2001; Richardson et al., 2005; and

Dechow et al., 2008), we examine analyst efficiency with respect to earnings disaggregated into

cash and accrual components, with the accruals portion further disaggregated into asset

efficiency change and sales growth components, and the cash portion further disaggregated into

distributed and retained components. Given that we evaluate analysts’ forecasts, our sample

necessarily differs from the samples used in prior studies of investor, but not analyst, response to

For that reason, whether analysts efficiently process

accounting information remains an interesting research question. This paper investigates whether

financial analysts’ forecasting behavior is a potential explanation for anomalous market response

to information in cash and accrual earnings components. Comparatively little prior research

evaluates the efficiency of financial analysts' earnings forecasts with respect to information in the

cash flow and accrual components of earnings, and even less research evaluates the degree to

which the behavior of analysts' earnings forecasts might explain accrual and cash flow

anomalies. The purpose of this paper is to evaluate analyst forecasting behavior as a potential

explanation for apparent stock market inefficiencies with respect to disaggregated cash and

accrual components of accounting earnings. Our two primary research questions are: Do analysts

efficiently process information in components of earnings? Does analyst forecasting behavior

explain market inefficiencies with respect to information in components of cash and accrual

earnings? Moreover, this study is the first to examine analysts’ ability to differentiate between

the persistence of the retained and distributed components of cash earnings.

1 See Schipper (1991), Brown (1993) and Ramnath et al. (2008) for literature reviews describing the role of financial analysts in capital markets.

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information contained in earnings components. In particular, relative to the prior returns-oriented

research, our sample is constrained to more recent years and larger firms included in the I/B/E/S

database. Nonetheless, we are able to replicate the key prior research findings that the market

appears to overreact to the total accruals component of earnings and, in particular, to the portion

of accruals due to changes in asset efficiency.2

Contrary to Dechow et al., in our more recent sample of larger firms, we find that retained

cash earnings are as persistent as distributed cash earnings, and we find that the market appears

to: (i) react efficiently to the persistence of retained and distributed-to-stockholder cash earnings;

and (ii) underreact to the persistence of distributed-to-debtholder cash earnings. On the other

hand, Dechow et al. (2008) find that the market appears to react efficiently to the distributed

portion of cash earnings and overreact to the persistence of the retained portion of cash

earnings.

3 Clearly, differences in sample characteristics may play a role in differences between

the evidence in our study and Dechow et al. (2008). Although beyond the scope of our study,

future research might explore the influence of firm-size and analyst following on the relative

persistence of retained versus distributed cash earnings, distributed-to-stockholder versus

distributed-to-debtholder cash earnings, and market efficiency with respect to all three

components.4

2 Our evidence that efficiency changes drive the market overreaction to accrual earnings is in contrast to Hribar and Yehuda (2008), who provide evidence suggesting that accruals mispricing arises from misunderstanding of returns to the firm’s growth opportunities, particularly during the firm’s growth stage. Subsequent research might explore whether the sales growth versus efficiency change explanation for accruals mispricing depends on the stage of the firm in its life cycle.

3 However, as discussed below, our results are consistent with Dimitrov and Jain (2008) who find that the market underreacts to the association between increases in leverage and deteriorating operating performance. 4 Each earnings component can take on positive or negative values. For example, the distributed-to-debtholder component of cash earnings is positive in the case of net payments to debtholders (i.e., debt-reducing cash flows) or negative in the case of net receipts from debtholders (i.e., debt-increasing cash flows). Similarly, the change in asset efficiency and sales growth components of accrual earnings can take on positive or negative values, and both the retained and distributed-to-equity components of cash earnings can take on positive or negative values.

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Additionally, we find that analysts appear to underreact to both cash and accrual components

of earnings. We also find similar analyst underreaction across all three cash components of

earnings (retained cash and cash distributed to both debtholders and stockholders). Furthermore,

we find that analyst underreaction to the sales growth (rather than the efficiency change)

component of accruals drives analysts’ overall underreaction to the persistence of accrual

earnings. Thus, we find no evidence that analyst forecasting behavior explains apparent market

overreaction to accrual earnings.5

Our evidence of analyst underreaction to accrual earnings is inconsistent with Bradshaw et al.

(2001), who find that analysts overreact to the persistence of working capital accruals in a

manner consistent with the market overreaction observed in Sloan (1996). However, our results

are consistent with Yu (2007) who provides evidence that Bradshaw et al.’s (2001) omission of

the cash earnings variable explains the differences in results between the two studies. Since cash

and accrual earnings are negatively correlated, a positive relation between cash earnings and

subsequent forecast errors may explain the Bradshaw et al. (2001) finding that accruals are

negatively related to future forecast errors. In fact, when we omit the cash variable, consistent

with Bradshaw et al. (2001), we find a negative correlation between accruals and future forecast

errors. However, when we include the cash variable, consistent with Yu (2007), we find that

analysts’ forecasts underreact to the persistence of accrual earnings.

Our evidence of analyst underreaction to the net distribution-

to-debtholders component of cash earnings is consistent with our evidence of similar market

underreaction. However, our empirical findings provide evidence of a point estimate that only

35% of the market underreaction can be attributed to analyst underreaction.

5 This interpretation is consistent with Hughes et al. (forthcoming) who find that trading strategies based on predictable analyst forecast errors are not profitable.

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Our empirical results, suggesting that analysts generally underreact to earnings persistence

across all components of accruals and cash flows, are consistent with the theory of analyst

underreaction developed in Raedy et al. (2006). Raedy et al. (2006) develop a mathematical

model predicting generalized analyst underreaction, given an asymmetric loss function that

punishes analysts more severely for reversing the direction of their earnings forecasts in light of

new information. This view suggests that analysts are sophisticated users of financial accounting

information and, as such, understand the persistence properties of various earnings components.

However, economic incentives lead analysts to underreact, and the underreaction is apparent

across all earnings components.6 On the other hand, the market includes unsophisticated

investors, who apparently overreact to the persistence of some accrual earnings components and

underreact to some cash earnings components, and market frictions prevent sophisticated

arbitragers from fully exploiting these inefficiencies.7

The rest of the paper is organized as follows. The next section discusses prior research, and

section III describes our research design. Section IV describes our sample, section V presents our

results, and section VI summarizes and concludes the paper.

II. Prior Research

6 Also, see Francis and Philbrick (1993) who find evidence suggesting that economic incentives lead analysts to bias their earnings forecasts upward in the face of bad news, and see Trueman (1990) who develops a mathematical model suggesting that analysts rationally underreact to public information to disguise relatively low ability in developing timely private information. 7 Lev and Nissim (2006) and Mashruwala et al. (2006) indicate that the accrual anomaly persists and will probably endure. The explanation for why the accrual anomaly is not arbitraged away by sophisticated institutional investors is that institutions avoid extreme-accrual firms because of their attributes, such as small size, low profitability, and high risk and that sophisticated individual investors are unable to profit from trading on accruals information due to the high information and transaction costs. Also see Hirshleifer and Teoh (2003) who argue that non-diversifiable risk associated with trading against inefficient unsophisticated investor behavior limits sophisticated investor arbitrage trading that might otherwise eliminate the mispricing.

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Beginning with Sloan (1996), researchers have investigated stock market efficiency with

respect to information in accrual and cash flow components of earnings. While Sloan examined

the market’s response to operating cash flows and working capital accruals minus depreciation,

the research has evolved to consider the market response to total accruals, accruals disaggregated

into various components, and cash flows disaggregated into retained and distributed components.

Richardson et al. (2005) find that the market overreacts to the persistence of both current

(non-cash working capital) and non-current operating accruals and that trading strategies based

on total operating accruals generate more profits than strategies based on current operating

accruals. Richardson et al. (2001, 2006) disaggregate total operating accruals into sales growth

and asset efficiency (i.e., turnover) components. Richardson et al. (2001) find that the market

overreacts to the persistence of both the sales growth and asset efficiency change components of

total accruals. The authors interpret this result to support both the perspective of Fairfield et al.

(2003), Cooper et al. (2005) and others that the accrual anomaly emerges from diminishing

returns on firms' growth opportunities and the perspective of Sloan (1996), Xie (2001) and others

that the accrual anomaly emerges from accounting distortions. We extend this research by

examining whether analysts’ forecasting behavior can explain market inefficiency with respect to

total operating accruals, as well as the sales growth and asset efficiency change components of

total operating accruals.

Dechow et al. (2008) disaggregate the cash component of earnings and find that the market

reacts efficiently to the persistence of the portion of cash earnings that the firm distributes to

investors; whereas, the market overreacts to the persistence of the portion of cash earnings that

the firm retains. This contradicts prior research evidence that the market underreacts to total cash

earnings (e.g., Sloan 1996; Desai et al., 2004; Ahmed et al. 2006). In a sample of larger firms

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followed by analysts during a more recent time period, we re-examine the market's response to

cash components of earnings, and extend the literature by examining whether analysts’

forecasting behavior can explain the market’s response to the cash components of earnings as

defined in Dechow et al. (2008). Contrary to Dechow et al. (2008), but consistent with Dimitrov

and Jain (2008) and Penman et al. (2007), we find that the market underreacts to the persistence

of the distributed-to-debtholder component of cash earnings. As described by Dimitrov and Jain

(2008), if managers have private information about deteriorating operating performance, they

may increase debt to provide funds to replace anticipated shortfalls. Dimitrov and Jain (2008)

provide evidence that the market appears to underreact to the negative signal about future

performance associated with current increases in debt (which, in our study, corresponds to a

negative distributed-to-debtholder component of cash earnings). Our evidence that the market

underreacts to the persistence of the distributed-to-debtholder component of current cash

earnings is consistent with the evidence and discussion in Dimitrov and Jain (2008). Similarly,

Penman et al. (2007) find a negative relation between the leverage component of the book-to-

price ratio and subsequent stock returns.8

8 Bradshaw, Richardson, and Sloan (2006) identify two competing hypotheses about the relationship between future stock returns and changes in debt: Ritter’s (2003) misvaluation hypothesis and Eberhart and Siddique’s (2002) wealth transfer hypothesis. Consistent with Bradshaw et al. (2006), our evidence of a positive relation between the distributed-to-debtholder component of cash earnings and both subsequent analyst forecast errors and subsequent stock return performance supports the misvaluation hypothesis. That is, our evidences suggests that analysts’ forecasts and stock prices are too low (high) following distributions to (from) debtholders, which means decreases (increases) in debt lead to positive (negative) future returns and forecast errors (consistent with Bradshaw et al.).

In addition to reexamining the persistence and market

reaction to information in cash earnings components, our study investigates the efficiency of

analysts’ earnings forecasts with respect to information in cash earnings components, and we

also investigate analyst forecasting behavior as an explanation for market inefficiencies with

respect to disaggregated cash earnings.

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III. Research design

Our investigation of analyst and investor response to cash and accrual earnings

components, respectively, builds on earnings disaggregations developed by Richardson et al.

(2001, 2006) and Dechow et al. (2008). Consistent with Dechow et al. (2008), we disaggregate

earnings into free cash flow (cash) and accrual components as follows (firm subscripts

suppressed throughout all models):

RNOAt = FCFt + ΔNOAt (1)

where RNOAt = operating income after depreciation, FCFt = free cash flow, and ΔNOAt = total

operating accruals, all deflated by NOAt-1 (non-cash net operating assets).9

FCFt = ΔCASHt + DIST_Dt + DIST_EQt (2)

Dechow et al. (2008)

disaggregate FCFt (cash earnings) into retained and distributed components as follows:

where ΔCASHt = the change in cash and short-term investments (i.e., free cash flow retained),

DIST_Dt = free cash flow to debtholders (excluding interest), and DIST_EQt = free cash flow to

stockholders (net income minus the change in stockholders’ equity), all deflated by NOAt-1.10

Consistent with Richardson et al. (2001, 2006), we define and disaggregate total

operating accruals as follows:

ACCt = SGt – ΔEFFt – (SGt * ΔEFFt) (3)

where: ACCt represents total operating accruals, defined as the percentage change in the firm’s

net operating assets; SGt = ΔSalest/Salest-1 and represents sales growth; ΔEFFt = ΔATt/ATt and

represents the change in asset efficiency measured as (Salest/NOAt – Salest-1/NOAt-1) /

(Salest/NOAt); and NOAt = total non-cash current and non-current operating assets less total

9 Theoretically, RNOAt in (1) should be NOIt (i.e., net operating income after tax). Following Richardson et al. (2005, 2006), we simply represent RNOAt as income after depreciation (Compustat item # 178). 10 Theoretically, distributions to preferred stockholders should be included in DIST_D. Following Dechow et al., we include distributions to/from preferred stockholders in DIST_EQ.

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current and non-current operating liabilities (with cash treated as a financial asset). Richardson et

al. (2006) provide an algebraic proof showing that the three terms on the RHS of (3) sum to total

accruals (ACCt).11 The authors argue that this decomposition allows tests with the potential to

distinguish between two competing explanations for the accrual anomaly: market failure to fully

impound mean reversion in sales growth versus market failure to fully impound information in

accruals about temporary accounting distortions. If diminishing marginal returns on investment

drive the lower persistence of accruals, this should be reflected in the growth component (SGt).

In contrast, if accounting distortions or changes in real operating efficiency drive the lower

persistence of accruals, then this should be reflected in the efficiency component (EFFt).12

Summarizing (1), (2), and (3) above, our fully disaggregated model of net income

becomes:

RNOAt = (ΔCASHt + DIST_Dt + DIST_EQt)

+ [SGt – ΔEFFt – (SGt * ΔEFFt)] (4)

where the first bracketed term on the RHS of (4) disaggregates free cash flow (FCFt), and the

second bracketed term disaggregates accruals (ACCt). Following Dechow et al. (2008), we

examine the persistence of the various earnings components by replacing RNOAt in model (4)

with RNOAt+1 in the summary components and detailed components regression models (5) and

(6) below.

RNOAt+1 = θ0 + θ1 FCFt + θ2 ACCt + ηt+1 (5) 11 Appendix A provides detailed definitions of each variable used in the study, along with Compustat item numbers. 12 Results in Hribar and Yehuda (2008) suggest that market overreaction to the persistence of firms’ growth opportunities, rather than overreaction to the persistence of accounting accruals, drives the accruals anomaly, particularly for growth firms early in their life cycle. If this is the case, then we expect that, controlling for market reaction to cash earnings components, the sales growth component should drive any market overreaction to accruals. Our results do not support this prediction and, instead, suggest that the market overreaction to accruals is driven by overreaction to the persistence of the changes in asset efficiency component, rather than the sales growth component, of total accruals.

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RNOAt+1 = λ0 + λ1∆CASHt + λ2DIST_EQt + λ3DIST_Dt

+ λ4SGt + λ5ΔEFFt + λ6(SGt * ΔEFFt) + ξt+1 (6)

Positive θ1 and θ2 in model (5) indicate that the summary earnings components, FCFt and ACCt,

respectively, are positively related to future earnings (i.e., they persist). Positive λ1, λ2, λ3, and λ4

and negative λ5 in model (6) indicate that the detailed earnings components, ∆CASHt, DIST_EQt,

DIST_Dt, SGt, and ΔEFFt, respectively, are positively related to future earnings (i.e., they

persist).

Next, we evaluate market efficiency with respect to the various components of cash and

accrual earnings.

RETt+1 = α0 + α1FCFt + α2ACCt + ut+1 (7)

RETt+1 = γ0 + γ1(ΔCASHt ) + γ2(DIST_EQt) + γ3(DIST_Dt)

+ γ4(SGt) + γ5(ΔEFFt) + γ6(SGt*ΔEFFt) + et+1 (8)

where RETt+1 = firm i’s raw returns, accumulated from the fifth month following the end of

fiscal year t through the fourth month following the end of fiscal year t+1, minus the similarly

accumulated mean return of all firms in the same size-decile as firm i (with size deciles formed

as of the end of fiscal year t).13

13 For sensitivity analysis, we accumulate returns starting from the beginning of the fourth month following the end of fiscal. The results are qualitatively the same.

All empirical models exclude firm-year observations with

negative values of NOAt-1 and, following Richardson et al. (2005), all financial statement ratios

are winsorized at +/-1 to mitigate the influence of outliers. If, as described in Richardson et al.

(2001), stock prices overreact to the persistence of sales growth and efficiency components of

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total accruals, then we expect α2 < 0 in (7) above, and we expect γ4 < 0 and γ5 > 0 in (8) above.14

If, as described in Dechow et al. (2008), stock prices overreact to the persistence of the retained

component of cash earnings, then we expect γ1 < 0 in (8) above.15

To evaluate any inefficiency in analysts’ earnings forecasts following the release of

financial statement information containing cash and accrual earnings components, we estimate

summary model (9) and components model (10) below.

FEt+1 = β0 + β1FCFt + β2ACCt + εt+1 (9)

FEt+1 = δ0 + δ1(ΔCASHt) + δ2(DIST_EQt) + δ3(DIST_Dt)

+ δ4(SGt) + δ5(ΔEFFt) + δ6(SGt*ΔEFFt) + ωt+1 (10)

where FEt+1 = (At+1 – Ft+1) / Pt ; At+1 = actual earnings for year t+1 per I/B/E/S; Ft+1 is the most

recent individual analyst forecast of year t+1 earnings, released prior to the beginning of the

return accumulation period; and Pt is the adjusted price taken from the same I/B/E/S monthly

report containing Ft+1.16

14 Controlling for sales growth, increases in asset efficiency (ΔEFFt) correspond to decreases in NOAt and decreases in accruals [see model (3) above]. Thus, if investors overreact to the persistence of accruals components of earnings, then increases in ΔEFFt (and decreases in accruals) should correspond to lower stock prices, higher subsequent returns (as correction occurs), and γ5 > 0 in model (8).

If analysts issue efficient forecasts following firms’ release of their

financial statements containing cash and accrual components of earnings, then the information in

those financial statements should not predict analysts’ forecast errors (FEt+1), and the coefficients

on the information variables in (9) and (10) should equal zero. The intercept term (β0 or δ0)

15 Rather than using the framework developed by Mishkin (1983), we use a conventional OLS model to test the rational expectations hypotheses. Kraft et al. (2007) show that OLS and the Mishkin test generate identical inferences in accounting settings when samples are large. As described in Kraft et al. (2007), to obtain estimates of the difference between actual and implied market estimates of the persistence of earnings components, the residual terms from models (5) and (6) are added as independent variables in models (7) and (8). Then the difference between actual and implied market persistence estimates are derived by dividing each component variable coefficient by the residual variable coefficient in models (7) and (8). 16 When more than one individual analyst forecast occurs on the day we use the median of those forecasts.

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captures analysts’ optimism/pessimism. For example, a significantly negative coefficient for the

intercept term would indicate systematically optimistic analysts’ forecasts.

To assess whether analyst forecasting behavior potentially explains market inefficiencies

with respect to information in cash and accrual components of earnings, we estimate summary

model (11) and components model (12).

RETt+1 = ρ0 + ρ1FCFt + ρ2ACCt + ρ3FEt+1 + ρ4FREVt+2+ et+1 (11)

RETt+1 = φ0 + φ1(ΔCASHt) + φ2(DIST_EQt) + φ3(DIST_Dt)

+ φ4(SGt) + φ5(ΔEFFt) + φ6(SGt)*(ΔEFFt) + φ7FEt+1 + φ8FREVt+2 + μt+1 (12)

where FREVt+2 = (t+1Ft+2 – tFt+2) / Pt; t+1Ft+2 is the first individual analyst forecast of t+2 earnings

issued after the end of the return accumulation period; and tFt+2 is the most recent forecast of t+2

earnings issued prior to the beginning of the return accumulation period.

Following Richardson et al. (2001, 2005), we use models (7) and (8) above to assess market

efficiency with respect to earnings components in our sample and during our time period. If

analyst forecasting behavior explains market inefficiency with respect to the information in cash

and accrual components of earnings, then we expect the coefficients on the cash and accrual

earnings information variables in models (9) and (10) to have the same signs and similar

significance levels as the coefficients on the same information variables in models (7) and (8),

and we expect the coefficients on the same information variables in models (11) and (12) to

equal zero. As described in Shane and Brous (2001), if analyst forecasting behavior explains

market inefficiency with respect to information about future earnings, then adding the forecast

error and forecast revision variables to the returns regression should make the coefficients on the

information variables go to zero.

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IV. Sample

Since we rely on I/B/E/S to measure the forecast error (FEt+1) and forecast revision

(FREVt+2) variables in models (9) through (12), our sample represents larger firms and a more

recent time period relative to the sample and time periods in Dechow et al. (2008) and

Richardson et al. (2001, 2005). Our time period spans the years 1988-2006; whereas, the

Richardson et al. (2001) time period spans 1988-1998, the Richardson et al. (2005) time period

spans 1962-2001, and the Dechow et al. (2008) time period spans 1950-2003.

To estimate the variables in our models, we obtain: financial statement data from Compustat;

returns data from CRSP; and earnings forecasts, actual earnings, and stock prices from I/B/E/S.

Table 1 describes our sample selection procedure. Our initial sample finds 186,928 firm-year

observations on Compustat’s Annual Industrial, Research, and Full Coverage files between the

years, 1988 and 2006. Following Richardson et al. (2005) and Richardson et al. (2006), we

exclude firms in the financial services industry (32,923 firm-years with SIC codes in the range

6000-6999). We omit 56,001 observations without Compustat data needed to compute our cash

flow and accrual earnings component variables or with negative net operating assets. We lose

29,649 observations missing the CRSP data needed to compute our returns variable. We lose

another 51,661 firm-year observations without I/B/E/S data needed to compute our forecast error

variable (FE), and we lose 7,929 observations without I/B/E/S data needed to compute our

forecast revision variable (FREV), resulting in a final sample of 8,755 firm-years spanning the

1988-2006 time-frame.

{Insert Table 1 about here}

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V. Results

Descriptive statistics

Table 2 presents descriptive statistics, including univariate correlations between variables

used in the study. The sample firms exhibit relatively high sales growth (15.8% on average),

slightly optimistic analyst forecasts (FE < 0), and horizon-dependent optimism (FREV < 0).

Most firm-years are associated with distributions of cash to stockholders (median DIST_EQ =

0.109), increases in debt (median DIST_D = -0.002), and positive free cash flow (median FCF =

0.1). The latter result is inconsistent with Dechow et al. (2006) whose sample has a negative

median free cash flow (median FCF = -0.014). Negative free cash flow is not necessarily an

indication of a financially distressed firm. Many young firms put a lot of their cash into

investments, which diminishes their free cash flow. However, a firm should have a good reason

for having negative free cash flows and the firm should be earning a sufficiently high rate of

return on its investments. While free cash flow does not receive as much scrutiny as earnings, it

is considered by some experts to be a better indicator of a firm’s financial health. Dechow et al.

(2008) find that accruals are less persistent than cash flows, on average, only in their subsample

of negative FCF firm-years. While our sample is characterized by firms with relatively more

persistent accrual earnings, as described in table 3 below, we still find that cash earnings (FCF)

are more persistent than accrual earnings (ACC) in our sample.


Panel B of Table 2 provides pair-wise correlations among the variables used in this study. On

a univariate basis, it appears that changes in asset efficiency, rather than sales growth, explain the

overall negative correlation between accruals and the subsequent year’s returns. Analysts also

appear to overestimate the persistence of the changes in asset efficiency earnings component, as

15

we observe a positive correlation between changes in asset efficiency and subsequent forecast

errors. Thus, on a univariate basis we see some indication that analyst forecasting behavior could

explain market overreaction to the persistence of the change in asset turnover component of

accrual earnings. The univariate correlations also provide some evidence of market and analyst

underreaction to the persistence of the distributed portion of free cash flow.

Table 3 describes returns to trading strategies with positions in top and bottom deciles of

each detailed earnings component: FCFt, ACCt, SGt, ΔEFFt, ΔCASHt, DIST_EQt and DIST_Dt.

Table 3 also describes differences in analyst forecast errors in the top versus bottom deciles of

the same variables. Panel A shows a 10.4% hedge portfolio return based on a trading strategy

taking a long (short) position in stocks in the highest (lowest) decile of cash earnings; however

this hedge return is not statistically significant, as the two-tailed p-value associated with the

difference in mean returns in the highest versus lowest decile is 0.14. Panel A shows that

analysts’ forecast errors are significantly larger in the highest decile relative to the lowest decile

of cash earnings. Thus, on a univariate basis, it appears that analysts’ forecasts underreact to the

persistence of cash earnings.


Panel B shows a 13.2% hedge portfolio return based on a trading strategy taking a long

(short) position in stocks in the lowest (highest) decile of net operating accruals. Panels C and D

suggest that this 13.2% hedge portfolio return is driven by market overreaction to the efficiency

change component (ΔEFF) of net operating accruals. We find a statistically significant 12.4%

hedge portfolio return based on a trading strategy taking a long (short) position in stocks in the

highest (lowest) decile of ΔEFF. Recall that the highest decile contains stocks where asset

turnover increased due to either an increase in sales (holding net operating assets constant) or a

16

decrease in accruals (holding sales growth constant). Since, we see only a 0.2% return to a

trading strategy based on sales growth (panel C), we attribute the 12.4% return to the trading

strategy based on changes in asset efficiency to a denominator effect (i.e., changes in accruals,

holding sales growth constant). On the other hand, financial analysts’ earnings forecasts do not

differ significantly between the top and bottom deciles of total accruals (ACC) or accrual

components (SG, ΔEFF).

In our sample of relatively large profitable and positive free cash flow firms, contrary to

Dechow et al. (2008), we find no evidence of returns to a trading strategy based on the retained

cash component of free cash flow (panel E). We do, however, find some evidence (panel G) of

returns to a trading strategy taking long (short) positions in stocks with high (low) free cash flow

to debtholders (7.6%, p-value = 0.018). This suggests that the market efficiently impounds

(underreacts to) information about the persistence of the retained (distributed-to-debtholder)

component of cash earnings. On the other hand, our univariate tests suggest that financial

analysts’ earnings forecasts underreact to the persistence of the distributed-to-stockholder

component of cash earnings (panel F).

Returns analysis

Before examining market efficiency with respect to earnings components, we report the

persistence characteristics of earnings components for our sample firms and time period. Panel A

of Table 4 shows that the coefficients relating cash and accrual earnings to next year RNOA are

0.618 and 0.420, respectively, and both coefficients are highly significant. Similarly, as shown in

table 4 panel B, each detailed cash and accrual earnings component is strongly related to next

year’s RNOA, and all components have positive persistence.

17


Table 5 describes the behavior of stock returns following publication of information in

accrual and cash components of earnings. In panel A, like Richardson et al. (2001, 2005), we

find a significantly negative relation between total accruals and following year returns,

suggesting market overreaction to the persistence of accrual earnings. Like Richardson et al.

(2001, 2005), we do not find a significant relation between cash earnings and subsequent returns.

Compared to Richardson et al. (2001, 2005), the coefficient on the total accruals variable is

smaller in our sample. We attribute this difference to mitigation of the negative relation between

accruals and subsequent returns during the 1999-2006 time period.17


Panel B of Table 5 replicates the Richardson et al. (2001) analysis of the relation of cash

flow and disaggregated current fiscal year accrual earnings components with following year

returns. In both our replication and the Richardson et al. (2001) study, the cash flow component

of earnings is not significantly related to future returns, and the change in asset efficiency

component of accruals is positively related to future returns. In our sample, the coefficient on the

sales growth component of accruals although negative is not significantly different from zero;

whereas, Richardson et al. find a significantly negative relation between sales growth and

following year returns. The coefficient on the change in asset efficiency component of accruals is

only about half the size of the coefficient in the Richardson et al. (2001) study.18

17 Our results do not change when we remove our forecast error and forecast revision data constraint. However, when we maintain that constraint but eliminate the years 1999-2006, the estimated coefficient on the accruals variable is similar in magnitude than the coefficient in the Richardson et al. study.

Nonetheless,

using our smaller sample of larger firms in a more recent time period, we are able to replicate the

18 The analysis of the relation of future returns with current accruals disaggregated into sales growth and change in asset efficiency components appears only in the Richardson et al. (2001) working paper and not in any subsequent published papers.

18

Richardson et al. (2001) finding that the market apparently overreacts to the persistence of the

change in the efficiency component of total accruals.19

Panels C and D of Table 5 extend the analysis to include the relation between

disaggregated cash flow and accrual information and next year’s returns. Panel C shows that, in

our sample, we replicate the result in Dechow et al. (2008) that the market overreacts to the

persistence of the accrual component of current year earnings and, consistent with Richardson et

al. (2001), panel D shows the overreaction to ΔEFFt drives the overreaction to ACCt.

Panel D of Table 5 reports the results of estimating our full returns model (8), which

regresses following year abnormal returns (RETt+1) on all three current year cash components of

earnings (ΔCASH, DIST_EQ, and DIST_D) and all three current year accrual components of

earnings (SG, ΔEFF and SG*ΔEFF). Consistent with our results reported above, estimates of

coefficients of the full model (8) suggest that in our sample the market underreacts to the

persistence of the net distributed-to-debtholder cash earnings component (DIST_D); whereas

Dechow et al. (2008) find that the market overreacts to the persistence of the retained cash

earnings component (ΔCASH).

Analyst forecast errors

To assess the efficiency of analysts’ earnings forecasts following the release of financial

statements containing information about the cash and accrual components of the prior year’s

earnings, we estimate models (9) and (10). Table 6 reports estimates of our regressions of

analysts’ forecast errors on prior year cash and accrual components of earnings. Given the results

reported above, if analyst forecasting behavior is consistent with the market response to total and

19 Recall that, holding sales growth constant, an increase in asset turnover (efficiency) corresponds to a decrease in accruals, so a positive coefficient on the efficiency change variable indicates that the market has overreacted to the persistence of this component of accruals.

19

disaggregated cash and accrual earnings, then we expect a negative coefficient on total accruals

in model (9), a positive coefficient on the efficiency change component of accrual earnings in

model (10), and a positive coefficient on the distributed-to-debtholder component of cash

earnings in model (10).


Panel A shows the results of estimating summary model (9), which regresses the error in

forecasts following the release of prior year financial statements on prior year cash (FCF) and

accrual (ACC) summary earnings components. The significantly positive coefficients on both

variables suggest that analysts’ earnings forecasts underreact to the persistence of both cash and

accrual earnings; whereas, we found in Table 5 that the market appears to overreact to the

persistence of accrual earnings. Thus, it does not appear that analyst forecasting behavior can

explain the apparent market overreaction to the persistence of accruals. Furthermore, panel B

indicates that the underreaction to accrual earnings extends to both the sales growth and asset

efficiency change components of total accruals, although the coefficient on the efficiency change

component is only marginally significant. Nonetheless, analyst behavior does not appear to be

responsible for investor overreaction to the persistence of the efficiency change component of

accrual earnings.

Panel C reports estimates of the coefficients in our full model (10). As shown in panel C,

analyst underreaction to cash earnings extends to all three components: ΔCASH, DIST_EQ, and

DIST_D. The positive coefficient on DIST_D offers a possible explanation for the market’s

underreaction to the distributed-to-debtholders component of cash earnings documented in panel

D of Table 5. Overall, we find that analyst behavior can only partially explain apparent market

inefficiencies in processing information about the persistence of accrual and cash earnings.

20

Analyst forecasting behavior as an explanation for market inefficiency

To summarize the results above, we find some evidence of market overreaction to total

accruals (panel A of Table 5), market overreaction to the asset efficiency change component of

total accruals (panel D of Table 5), and market underreaction to the distributed-to-debtholders

component of cash earnings (panel D of Table 5); and we find evidence of general analyst

underreaction to accrual and cash components of earnings, with the possible exception of the

efficiency change component of accruals (panel C of Table 6).

Table 7 adds forecast error and forecast revision variables to the returns summary and

component models estimated in Table 5. Like the returns variable, the forecast error (FE) and

forecast revision (FREV) variables are computed with reference to forecasts issued following

firms’ publication of financial statements containing the earnings component information

variables: ACC, SG, ΔEFF, FCF, ΔCASH, DIST_EQ, and DIST_D. If analyst forecasting

behavior explains the market overreaction to ACCt and ΔEFFt and the market underreaction to

DIST_Dt, then we expect the coefficients on those variables to approach zero when we add FEt+1

and FREVt+1 to the returns model.20


As described in panel A of Table 7, without FEt+1 and FREVt+2 in model (7), the coefficient

relating ACCt to RETt+1 equals -0.062 and is significantly less than zero (p-value < 0.05). Since

estimating models (9) and (10) in Table 6 produces no evidence that analysts overreact to any

earnings components, it is not surprising that adding FEt+1 and FREVt+1 to model (7) does not

cause the coefficient on ACCt to move much towards zero. In fact, panel A of Table 7 shows that

20 See Shane and Brous (2001) for a detailed explanation of this approach to gauging the degree to which analyst forecasting behavior explains market inefficiencies.

21

the coefficient on ACCt in model (11) is -0.060, still significantly negative (p-value < 0.05).

Similarly, the coefficient relating ΔEFFt to RETt+1 equals 0.11 without FEt+1 and FREVt+1 in

model (8) (indicating market overreaction to the persistence of changes in asset efficiency, p-

value < 0.01), and adding FEt+1 and FREVt+2 to model (8) does not cause the coefficient on

ΔEFFt to move much towards zero. In fact, the coefficient on ΔEFFt in model (12) is 0.104, still

significant at less than the 0.01 level.

On the other hand, since the market underreacts to the persistence of DIST_Dt (panel D of

Table 6), and analysts similarly underreact (panel C of Table 6), we expect the coefficient on

DIST_Dt to move towards zero when we add FEt+1 and FREVt+2 to model (8). In fact, the

coefficient relating DIST_Dt to RETt+1 is 0.057 without FEt+1 and FREVt+2 in the model (p-value

< 0.05), and it decreases by 35% to 0.037 and is no longer statistically significant when we add

FEt+1 and FREVt+1 to the model. Thus, we estimate that analyst forecasting behavior potentially

explains 35% of the market’s underreaction to the distributed-to-debtholder component of cash

earnings.

VI. Conclusion

Richardson et al. (2001, 2005) provide evidence suggesting that stock prices overreact to the

persistence of total operating accruals, and Richardson et al. (2001) provide evidence that the

apparent stock price overreaction to the persistence of total operating accruals extends to two

important accrual earnings components: sales growth and change in asset efficiency. Dechow et

al. (2008) provide evidence that stock prices overreact to the persistence of cash earnings, and

Dechow et al. find that overreaction to the persistence of the retained cash component of

earnings drives the market’s overreaction to total cash earnings. Since both Dechow et al. (2008)

22

and Richardson et al. (2001) suggest that the market does not efficiently impound information

about the persistence of earnings components, an important question is whether financial

analysts fail to distinguish the persistence of various earnings components and, thereby, publish

earnings forecasts that create market inefficiency.

We find little evidence to support the view that financial analysts are responsible for market

inefficiency with respect to the persistence characteristics of the various earnings components

evaluated by Dechow et al. (2008) and Richardson et al. (2001). Instead, consistent with

economic theories of analyst underreaction (e.g., Raedy et al. 2006, Trueman 1990), we find a

generalized analyst underreaction to earnings components.

Our sample of relatively large firms followed by analysts in a recent time period produces

results that differ somewhat from the evidence of market inefficiencies in Dechow et al. (2008)

and Richardson et al. (2001). In particular, we do not find the overreaction to sales growth

documented by Richardson et al. (2001), and we find that the market underreacts to the

persistence associated with cash earnings distributed to debtholders, whereas, Dechow et al.

(2008) find that the market overreacts to cash earnings retained for reinvestment. Our evidence

is consistent with market underreaction to the persistence of earnings components corresponding

to cash flows that increase or decrease leverage. Our evidence is also consistent with accounting

distortions causing temporary accounting changes in asset efficiency (turnover) that fool

investors but not financial analysts.

23

Appendix A Variable definitions

(Firm i subscripts suppressed; Item #s refer to Compustat) RETt+1 = the annual buy and hold size-adjusted return. The size-adjusted return is calculated by subtracting the value-weighted average return for all firms in the same size-matched decile, where size is measured as market capitalization at the beginning of the return accumulation period. The return accumulation period begins four months after the end of fiscal year t. FEt+1 = the signed forecast error, calculated as (At+1 – Ft+1) / Pt. At+1 is actual earnings for fiscal year t+1, per I/B/E/S. Ft+1 is the most recent I/B/E/S individual forecast prior to the beginning of the return accumulation period. When more than one forecast occurs on that day, Ft+1 is the median of all forecasts occurring on that day. Pt is the adjusted price taken from the same I/B/E/S report month containing Ft+1. FREVt+2 = (t+1Ft+2 – tFt+2) / Pt; where t+1Ft+2 = the first individual analyst forecast of t+2 earnings issued after the end of the return accumulation period; and tFt+2 = the most recent forecast of t+2 earnings issued prior to the beginning of the return accumulation period ACCt = (NOAt – NOAt-1) / NOAt-1 = the percentage change in Non-cash Net Operating Assets, defined as total operating accruals for fiscal year t. NOAt = Total operating assets – Total operating liabilities for fiscal year t, where cash and investments are defined as financial assets [Item #6 – Item #1 – Item #32 – (Item #181 – Item #34 – Item #9)]. SGt = sales growth, calculated as (Salest / Salest-1) – 1, where Sales is Item #12. ∆EFFt = the deflated change in asset efficiency, calculated as (ATt – ATt-1) / ATt, where ATt = Salest / NOAt. RNOAt = return on net operating assets for fiscal year t, calculated as operating income after depreciation (Item #178) deflated by NOAt-1. FCFt = free cash flow deflated by NOAt-1, calculated as RNOAt – ACCt. DIST_EQt = net equity distributions, calculated as (∆Item #6 – ∆Item #181 - ∆Item #32 – Item #178) deflated by NOAt-1.

DIST_Dt = net debt distributions, calculated as (∆Item #9 + ∆Item #34) deflated by NOAt-1.

24

DISTt = net distributions to capital providers, calculated as DIST_EQt + DIST_Dt.

∆CASHt = change in cash balance, calculated as ∆Item #1 deflated by NOAt-1.

25

References

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Fairfield, P., Whisenant, S., Yohn, T., 2003. Accrued earnings and growth: implications for future profitability and market mispricing. The Accounting Review 78, 353-371. Francis, J., Philbrick, D., 1993. Analysts’ decisions as products of multi-task environment. Journal of Accounting Research 31: 216-230. Hirshleifer, D., Teoh, S., 2003. Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics 36, 337-386. Hribar, P., Yehuda, N., 2008. Reconciling growth and persistence as explanations for accrual mispricing. Working paper, University of Iowa and Cornell University. Hughes J., Liu, J., Su, W., 2008. On the relation between predictable market returns and predictable analyst forecast errors. Review of Accounting Studies 13, 266-291. Kraft, A. G., Leone, A. J. Wasley, C. E., 2007. Regression-Based Tests of the Market Pricing of Accounting Numbers: The Mishkin Test and Ordinary Least Squares. Journal of Accounting Research, 45, 1081-1114. La Porta, R., 1996. Expectations and the cross-section of stock returns. Journal of Finance 51, 1715-1742. Lev, B., Nissim, D., 2006. The persistence of the accruals anomaly. Contemporary Accounting Research 23, 1-34. Mashruwala, C., Rajgopal, S., Shevlin, T., 2006. Why is the accrual anomaly not arbitraged away? The role of idiosyncratic risk and transactions costs. Journal of Accounting & Economics 13, 341-363. Penman, S., Richardson, S., Tuna, I. 2007. The book-to-price effect in stock returns: Accounting for leverage. Journal of Accounting Research 45, 427-467. Raedy, J., Shane, P., Yang, Y., 2006. Horizon-dependent underreaction in financial analysts’ earnings forecasts. Contemporary Accounting Research 23, 291-322. Ramnath, S., Rock, S., Shane, P., 2008. Financial analyst forecasting literature: Taxonomy with suggestions for further research. International Journal of Forecasting 24, 34-75. Richardson, S., Sloan, R., Soliman, M., Tuna, I., 2001. Information in accruals about the quality of earnings. Working paper, University of Michigan. Richardson, S., Sloan, R., Soliman, M., Tuna, I., 2005. Accrual reliability, earnings persistence and stock prices. Journal of Accounting & Economics 39, 437-485. Richardson, S., Sloan, R., Soliman, M., Tuna, I., 2006. The implications of accounting distortions and growth for accruals and profitability. The Accounting Review 81, 713-743.

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28

Table 1 Sample Selection

All Compustat firm-years from 1988 to 2006 186,928 Exclude financial firms (SIC codes 6000-6999) -32,923 Exclude firm-years missing data needed to compute cash flow and accrual earnings component variables or where NOA < 0 -56,001

Exclude firm-years without matching CRSP data needed to compute RET -29,649 Exclude firm-years without matching I/B/E/S data needed to compute FE -51,661 Subtotal 16,684 Exclude firm-years without data needed to compute FREV -7,929 Number of firm-years in final sample 8,755

*See appendix A for variable definitions.

29

Table 2 Descriptive Statistics

Panel A: Univariate Statistics (variables winsorized at +/- 1 except RET) Mean Std.Dev. 25% Median 75% SG 0.158 0.250 0.025 0.109 0.238 ∆AT -0.026 0.278 -0.109 0.008 0.102 ∆CASH 0.043 0.267 -0.017 0.003 0.057 DIST_EQ 0.102 0.308 0.040 0.109 0.194 DIST_D -0.065 0.240 -0.102 -0.002 0.027 DIST 0.037 0.394 -0.042 0.090 0.197 FCF 0.080 0.405 -0.033 0.100 0.225 ACC 0.175 0.400 -0.011 0.088 0.265 RNOAt 0.256 0.385 0.109 0.188 0.323 FE -0.009 0.059 -0.012 -0.001 0.003 A/P 0.046 0.087 0.034 0.055 0.074 F/P 0.056 0.079 0.042 0.060 0.080 FREV -0.011 0.051 -0.023 -0.004 0.004 RET 0.039 0.566 -0.239 -0.033 0.197

*See appendix A for variable definitions.

30

Table 2 Descriptive Statistics (cont’d)

Panel B: Correlation Matrix—Pearson above diagonal and Spearman below diagonal (p-values below correlations) RETt+1 FEt+1 At/Pt Ft/Pt FREVt+2 SGt ∆EFFt ∆CASHt DIST_EQt DIST_Dt DISTt FCFt ACCt RNOAt RETt+1 1 0.334 0.044 0.041 0.268 -0.029 0.051 0.003 0.062 0.041 0.082 0.076 -0.067 0.007 <.0001 <.0001 <.0001 <.0001 0.0057 <.0001 0.7371 <.0001 <.0001 <.0001 <.0001 <.0001 0.4864 FEt+1 0.165 1 0.065 -0.032 0.002 -0.013 0.080 0.058 0.038 0.065 0.079 0.100 -0.082 0.015 <.0001 <.0001 0.0021 <.0001 0.2186 <.0001 <.0001 0.0003 <.0001 <.0001 <.0001 <.0001 0.1542 At/Pt -0.060 0.390 1 0.744 0.017 0.027 0.028 0.015 0.227 0.022 0.186 0.189 0.019 0.271 <.0001 <.0001 <.0001 0.0982 0.0102 0.0074 <.0001 <.0001 0.0346 <.0001 <.0001 0.0636 <.0001 Ft/Pt -0.056 -0.131 0.763 1 0.029 -0.150 -0.031 -0.081 0.186 -0.011 0.137 0.091 -0.057 0.042 <.0001 <.0001 <.0001 0.0060 <.0001 0.0036 <.0001 <.0001 0.2867 <.0001 <.0001 <.0001 <.0001 FREVt+2 0.137 0.383 0.021 -0.021 1 -0.024 0.028 0.030 0.023 0.041 0.048 0.056 -0.037 0.012 <.0001 <.0001 0.0399 0.0480 0.0236 0.0076 0.0044 0.0313 0.0001 <.0001 <.0001 0.0004 0.2494 SGt -0.008 0.001 0.060 -0.041 -0.004 1 0.188 0.210 -0.274 -0.276 -0.382 -0.233 0.491 0.264 0.4150 0.8565 <.0001 <.0001 0.6730 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

∆EFFt 0.046 0.022 -0.007 -0.029 0.019 0.198 1 0.158 0.180 0.377 0.370 0.465 -0.713 -0.251 <.0001 0.0337 0.4990 0.0055 0.0714 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

∆CASHt -0.001 0.028 0.043 -0.001 0.009 0.159 0.241 1 -0.300 -0.106 -0.299 0.367 0.021 0.407 0.8871 0.0077 <.0001 0.8908 0.3669 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

DIST_EQt 0.021 0.039 0.107 0.118 0.017 -0.164 0.085 -0.148 1 0.021 0.793 0.575 -0.332 0.259 0.0493 0.0002 <.0001 <.0001 0.1067 <.0001 <.0001 <.0001 0.0408 <.0001 <.0001 <.0001 <.0001

DIST_Dt 0.043 0.018 0.007 -0.008 0.009 -0.221 0.370 -0.005 -0.029 1 0.625 0.539 -0.508 0.037 <.0001 0.0846 0.4759 0.4224 0.3626 <.0001 <.0001 0.5761 0.0060 <.0001 <.0001 <.0001 0.0004

DISTt 0.042 0.042 0.088 0.087 0.019 -0.284 0.334 -0.141 0.710 0.561 1 0.777 -0.569 0.225 <.0001 <.0001 <.0001 <.0001 0.0698 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 FCFt 0.040 0.059 0.114 0.084 0.025 -0.205 0.468 0.311 0.542 0.531 0.799 1 -0.541 0.488 0.0001 <.0001 <.0001 <.0001 0.0182 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 ACCt -0.047 -0.022 0.052 0.005 -0.020 0.507 -0.640 -0.099 -0.183 -0.512 -0.505 -0.566 1 0.469 <.0001 0.0341 <.0001 0.6388 0.0539 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 RNOAt -0.006 0.039 0.174 0.093 0.005 0.362 -0.176 0.220 0.381 -0.001 0.264 0.363 0.411 1 0.5410 0.0002 <.0001 <.0001 0.6314 <.0001 <.0001 <.0001 <.0001 0.8538 <.0001 <.0001 <.0001

31

32

Table 3 Mean Returns and Forecast Errors

Sorted on Earnings Component Deciles * Panel A: Deciles formed based on the cash flow variable (FCFt)

Decile FCFt FEt+1 RETt+1 1 -0.715 -0.016 0.005 2 -0.173 -0.024 0.009 3 -0.041 -0.014 0.044 4 0.026 -0.015 0.026 5 0.076 -0.010 0.023 6 0.121 -0.006 0.048 7 0.167 -0.008 0.055 8 0.228 -0.006 0.021 9 0.340 0.097 0.051

10 0.774 -0.005 0.109 Differences between 10th and 1st deciles **

0.011 (0.020)

0.104 (0.140)

Panel B: Deciles formed based on the total accruals variable (ACCt) Decile ACCt FEt+1 RETt+1

1 -0.326 -0.010 0.128 2 -0.072 -0.008 0.054 3 -0.010 -0.008 0.062 4 0.028 -0.012 0.035 5 0.070 -0.011 0.028 6 0.118 -0.009 0.034 7 0.180 -0.012 0.029 8 0.270 -0.013 0.019 9 0.440 -0.015 0.009 10 1.058 -0.013 -0.004

Differences between 10th and 1st deciles **

-0.003 (0.725)

-0.132 (0.055)

* See appendix A for variable definitions. ** Two-tailed p-values are in the parentheses.

33

Table 3 (continued)

Panel C: Deciles formed based on the sales growth component of total accruals (SGt)

Decile SGt FEt+1 RETt+1 1 -0.185 -0.015 0.034 2 -0.020 -0.014 0.075 3 0.025 -0.008 0.022 4 0.059 -0.008 0.055 5 0.092 -0.009 0.016 6 0.130 -0.009 0.030 7 0.176 -0.013 0.010 8 0.242 -0.011 0.060 9 0.358 -0.011 0.056

10 0.701 -0.012 0.032 Differences between 1st and

10th deciles -0.003 (0.393)

0.002 (0.978)

Panel D: Deciles formed based on the efficiency change component of total accruals (ΔEFFt) Decile ΔEFFt FEt+1 RETt+1

1 -0.621 -0.014 -0.018 2 -0.225 -0.018 -0.005 3 -0.115 -0.112 0.044 4 -0.054 -0.010 0.028 5 -0.012 -0.010 0.035 6 0.024 -0.010 0.036 7 0.059 -0.007 0.038 8 0.104 -0.007 0.064 9 0.174 -0.013 0.065 10 0.398 -0.010 0.106

Differences between 10th and 1st deciles

0.004 (0.443)

0.124 (0.049)

34

Table 3 (continued)

Panel E: Deciles formed based on the change in cash balance component of free cash flows (ΔCASHt)

Decile ΔCASHt FEt+1 RETt+1 1 -0.365 -0.016 0.109 2 -0.065 -0.013 0.038 3 -0.019 -0.011 0.034 4 -0.004 -0.012 -0.007 5 0.001 -0.009 -0.012 6 0.009 -0.010 0.027 7 0.024 -0.009 0.019 8 0.061 -0.010 0.033 9 0.164 -0.011 0.056 10 0.629 -0.008 0.097

Differences between 10th and 1st deciles 0.008

(0.035) -0.012 (0.823)

Panel F: Deciles formed based on the net distributions to stockholder component of free cash flows (DIST_EQt)

Decile DIST_EQt FEt+1 RETt+1 1 -0.529 -0.018 0.025 2 -0.054 -0.016 0.014 3 0.037 -0.020 0.047 4 0.072 -0.014 0.037 5 0.097 -0.011 0.023 6 0.009 -0.005 0.048 7 0.152 -0.007 0.044 8 0.197 -0.006 0.041 9 0.287 -0.008 0.052 10 0.643 -0.005 0.059

Differences between 10th and 1st deciles

0.013 (0.028)

0.034 (0.729)

Panel G: Deciles formed based on the net distributions to debtholder component of free cash flows (DIST_Dt)

DIST_Dt FEt+1 RETt+1 1 -0.610 -0.013 -0.036 2 -0.208 -0.019 0.023 3 -0.107 -0.016 -0.006 4 -0.052 -0.008 0.034 5 -0.018 -0.009 0.043 6 -0.001 -0.009 0.101 7 -0.044 -0.010 0.055 8 0.028 -0.007 0.078 9 0.067 -0.008 0.058

10 0.241 -0.011 0.040 Differences between 10th and 1st deciles

0.002 (0.772)

0.076 (0.018)

35

Table 4 Regressions of Future Earning Performance on Accrual Components and Cash Flow

Components of Current Earnings Performance (8,755 observations spanning the years 1988-2006)

Panel A: Regression analyzing the persistence of the free cash flow and total operating accruals Model (5): RNOAt+1 = θ0 + θ1 FCFt + θ2 ACCt + ηt+1 Intercept FCF ACC Adj. R2

Coefficient 0.115 0.618 0.420 0.336 (t-statistic) (11.83)a (14.94)a (12.71)a

Panel B: Regression analyzing the persistence of accrual components and cash flow components Model (6): RNOAt+1 = λ0 +λ 1∆CASHt + λ2DIST_EQt +λ 3DIST_Dt + λ4SGt +λ θ5ΔEFFt + λ6(SGt * ΔEFFt) + ξt+1 Intercept ∆CASH DIST_EQ DIST_D SG ΔEFF SGt * ΔEFF Adj. R2 Coefficient 0.086 0.647 0.658 0.513 0.532 -0.336 -0.281 0.380 (t-statistic) (7.11)a (14.06)a (13.25)a (9.25) a (11.40)a (-7.23)a (-3.92)a

a indicates two tailed P value associated with t-stats is less than 0.01; b indicates two tailed P value associated with t-stats is less than 0.05; c indicates two tailed P value associated with t-stats is less than 0.10. The Fama-MacBeth procedure is employed to run cross-sectional regressions (associated t-statistics in parentheses).

36

Table 5 The Behavior of Stock Returns Following Publication of Detailed Cash and Accrual

Earnings Information (8,755 observations spanning the years 1988-2006)

Panel A: OLS Regression for Total Operating Accruals Model (7): RETt+1 = α0 + α 1 FCFt + α 2 ACCt + ut+1 Intercept FCF ACC Adj. R2

Coefficient 0.047 0.018 -0.062 0.009 (t-statistic) (1.97)c (0.69) (-2.42)b

Panel B: OLS Regression for Growth and Efficiency Decomposition of Accruals RETt+1 = β0 + β1 FCFt + β2 SGt + β3ΔEFFt + β4[(SGt)*(ΔEFFt)] + ωt+1 Our Results (8,755 firm-years spanning 1988-2006) Intercept FCF SG ΔEFF SG * ΔEFF Adj. R2

Coefficient 0.044 0.005 -0.015 0.121 0.023 0.015 (t-statistic) (1.87)c (0.19) (-0.30) (3.80)a (0.37)

Panel C: OLS Regression of subsequent years returns on accrual and cash flow components RETt+1 = κ0 + κ1 ∆CASHt + κ2 DIST_EQt + κ3 DIST_Dt + κ4 ACCt + ςt+1

Intercept ΔCash DIST_EQ DIST_D ACC Adj. R2 Coefficient 0.049 0.008 0.003 0.064 -0.051 0.012 (t-statistic) (1.85)c (0.24) (0.08) (2.33)b (-2.03)c

Panel D: OLS Regression of subsequent years returns on accrual components and cash flow components Model (8): RETt+1 = γ0 + γ1∆CASHt + γ2 DIST_EQt + γ3DIST_Dt + γ4 SGt + γ5ΔEFFt + γ6(SGt * ΔEFFt) + et+1 Intercept ∆CASH DIST_EQ DIST_D SG ΔEFF SGt * ΔEFF Adj. R2 Coefficient 0.046 -0.014 0.0002 0.057 0.0006 0.110 -0.007 0.018 (t-statistic) (1.77)c (-0.41) (0.01) (2.01)c (0.01) (3.41)a (-0.12)


37

Table 6 The Behavior of Analysts’ Earnings Forecasts

Following Publication of Detailed Cash and Accrual Earnings Information (8,755 firm-years spanning 1988-2006)

Panel A: Summary model regressing subsequent forecast errors on cash and accrual earnings Model (9): FEt+1 =β0 + β1FCFt + β2ACCt + εt+1

Intercept FCF ACC Adj. R2 Coefficient -0.013 0.0130 0.005 0.006 (t-statistic) (-5.18)a (3.91)a (1.91)c

Panel B: Components model regressing subsequent forecast errors on cash and accrual earnings components FEt+1 = δ0 + δ1FCFt + δ2SGt + δ3ΔEFFt + δ4[(SGt)*(ΔEFFt)] + ωt+1

Intercept FCF ∆SG ΔEFF SG * ΔEFF Adj. R2 Coefficient -0.014 0.014 0.009 -0.008 0.006 0.010 (t-statistic) (-5.57)a (3.97)a (2.07)c (-1.59) (0.76)

Panel C: OLS Regression for Growth, Efficiency, ∆CASH, DIST_EQ, and DIST_D Model (10) : FEt+1 = δ0 + δ1∆CASHt + δ2 DIST_EQt + δ3DIST_Dt + δ4

*SGt + δ5ΔEFFt + δ6 [(SGt)*(ΔEFFt)] + ωt+1 Intercept ∆CASH DIST_EQ DIST_D SG ΔEFF SG *ΔEFF Adj. R2 Coefficient -0.014 0.012 0.017 0.010 0.007 -0.006 0.010 0.012 (t-statistic) (-5.03)a (4.02)a (2.84)b (2.95)a (1.74)c (-1.25) (1.39)


38

Table 7 Analyst Forecasting Behavior as an Explanation for Market Inefficiency with Respect to

Information in Cash and Accrual Components of earnings (8,755 firm-year observations from 1988 to 2006)

Panel A: OLS Regression for Accruals Model (7): RETt+1 = α0 + α 1FCFt + α 2ACCt + ut+1 Model (11) : RETt+1 = ρ0 + ρ1FCFt + ρ2ACCt + ρ3FEt+1 + ρ4FREVt+2+ et+1

Intercept FCF ACC FE FREV Adj. R2

Model (7) Coefficient 0.047 0.018 -0.062 0.009 (t-statistic) (1.97)c (0.69) (-2.42)b

Model (11) Coefficient 0.079 -0.004 -0.060 1.709 1.225 0.083 (t-statistic) (3.28)a (-0.16) (-2.34)b (6.00)a (6.14)a

Panel B: OLS Regression for Growth, Efficiency, ∆CASH, DIST_EQ, and DIST_D Model (8): RETt+1 = γ0 + γ1∆CASHt + γ2 DIST_EQt + γ3DIST_Dt + γ4 SGt + γ5ΔEFFt + γ6(SGt * ΔEFFt) + et+1 Model (12): RETt+1 =φ0 +φ1∆CASHt + φ2 DIST_EQt + φ3DIST_Dt+ φ4 SGt +φ5ΔEFFt + φ6(SGt)*(ΔEFFt) + φ7FEt+1 + φ8FREVt+2 + μt+1 Intercept ∆CASH DIST_EQ DIST_D SG ΔEFF SGt * EFF FE FREV Adj. R2

Model (8) Coefficient 0.046 -0.014 0.0002 0.057 0.0006 0.110 -0.007 0.018 (t-statistic) (1.77)c (-0.41) (0.01) (2.01)c (0.01) (3.41)a (-0.12)

Model (12) Coefficient 0.081 -0.035 -0.027 0.037 -0.005 0.104 -0.015 1.727 1.213 0.092 (t-stat.) (3.06)a (-0.92) (-0.71) (1.24) (-0.11) (3.43)a (-0.28) (6.02)a (6.10)a


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Does Analyst Forecasting Behavior Explain Anomalous ......to information in cash and accrual earnings components. Comparatively little prior research evaluates the efficiency of financial