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Estimating Abnormal Changes in Cash with

Earnings Persistence and Mispricing Implications *

Jeff Zeyun Chen†

Philip B. Shane††

First Draft: 27 July 2010

This draft: 9 August 2010

*We appreciate helpful comments of University of Colorado workshop participants.

† Corresponding author. University of Colorado at Boulder, Leeds School of Business. Voice: (303) 492-4480.

Email: zeyun.chen@colorado.edu.

††

University of Colorado at Boulder Leeds School of Business, and the University of Auckland Business School.

Voice: 303-492-0423. Email: phil.shane@colorado.edu or p.shane@auckland.ac.nz.

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Estimating Abnormal Changes in Cash with

Earnings Persistence and Mispricing Implications

1. Introduction

This paper extends research investigating the persistence and market pricing of earnings

components. Sloan [1996] separates earnings into cash and accrual components and finds that

the cash component has greater persistence than the accrual component, investors inefficiently

treat both components as having similar persistence characteristics and, therefore, the stock

market underreacts to the persistence of cash earnings and overreacts to the persistence of

accruals. Xie [2001] further disaggregates accrual earnings into discretionary and non-

discretionary components and finds that discretionary accruals drive the accrual anomaly

discovered by Sloan.1

Dechow, Richardson and Sloan [DRS 2008] more precisely define total accruals as the

income effects of changes in non-cash operating asset and liability accounts and free cash flow

as the difference between total earnings and accruals. Furthermore, DRS disaggregate total free

cash flow into net changes in the firm‟s cash balance (including short-term investments and other

financial assets) and net distributions to providers of capital. DRS find that the change in the

cash balance has similar persistence characteristics as accruals and that the market similarly

overreacts to the persistence of both accruals and changes in cash.

Our paper further analyzes the primary DRS finding. We investigate the characteristics of

changes in cash that create persistence characteristics similar to accruals. In particular, as

explained in more detail below, we disaggregate changes in cash into a normal (precautionary)

1 Richardson et al. [2006] describe another approach to disaggregating total accruals, but their goal is to investigate

whether accounting distortions or diminishing returns to new investment drive the accrual anomaly. Richardson et al.

conclude that accounting distortions are primarily responsible for the accrual anomaly, these distortions exist in both

their asset turnover and sales growth components of total accruals, and they cannot rule out the Fairfield et al. [2003]

diminishing returns to investment explanation for the accrual anomaly.

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component and an abnormal component that can be positive or negative and creates either excess

or insufficient cash on hand. In addition, we extend Xie‟s [2001] decomposition of accruals into

discretionary and non-discretionary components to include long-term accruals (Richardson et al.

[2005]) and we follow the performance-based matching approach recommended by Kothari et al.

[2005]. Following DRS, we apply the Mishkin [1983] approach in our evaluation of market

efficiency.2 We extend DRS by evaluating market efficiency with respect to the persistence of

seven earnings components: total accruals, disaggregated into discretionary and non-

discretionary components; and free cash flow, disaggregated into distributions to debt holders,

distributions to equity holders, and normal, insufficient and excessive changes in cash. Our

disaggregation of changes in cash follows recent developments in finance literature estimating

optimal cash balances (e.g., Opler et al. [1999]; Bates, Kahle and Stulz [BKS 2009]).

DRS disaggregate the free cash flow component of earnings into three categories: the change

in the cash balance, net distributions to debt holders, and net distributions to equity holders.3

DRS find that the change in cash and distribution to debt holders components of free cash flow

have similar persistence as total accruals and lower persistence than distributions to equity

holders, and the market similarly overreacts to both accruals and the portion of earnings (and free

cash flow) due to the change in cash. DRS infer that net cash distributions to equity holders drive

Sloan‟s [1996] finding that cash earnings have greater persistence than accruals. DRS find no

2 Further extending DRS, in sensitivity tests, we evaluate market efficiency using an OLS approach advocated by

Kraft, et al. [2006] and Shane and Brous [2001]. Also, to avoid the look-ahead bias inherent in pooled cross-

sectional regressions, our sensitivity analysis applies both Mishkin tests and OLS in a Fama-MacBeth [1973] year-

by-year framework. Conclusions regarding market inefficiency with respect to the persistence characteristics of all

four free cash flow components are robust; however, as in Kraft et al. [2006], conclusions regarding market

inefficiency with respect to accruals are not robust.

3 Throughout the paper, references to changes in the cash balance include changes in marketable securities and any

other financial assets. Like changes in cash, net distributions to debt and equity holders can be positive or negative;

i.e., the firm‟s payments to debt holders and stockholders can exceed receipts due to new investments in the firm‟s

debt and equity securities or vice versa.

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evidence of market inefficiency with respect to the cash distributions to equity and debt holder

components of free cash flow; however, they find that the market similarly overreacts to the

persistence of accruals and the change in cash earnings components. DRS (p. 558) attribute the

finding of similar market overreaction and lower persistence of accruals and changes in the cash

balance to “…hubris concerning future investment opportunities. If managers and investors are

overoptimistic about the investment opportunities of certain firms, these firms invest more

capital and have less sustainable profitability.”

Our paper further investigates the DRS hubris interpretation of their results. To do this, we

disaggregate the change in cash component of free cash flow into a normal part that moves the

firm towards an estimated optimum level of cash holdings and an abnormal part that moves the

firm away from the estimated optimum. We further disaggregate abnormal changes in the cash

balance into those that create excess cash and those that create insufficient cash. The hubris

hypothesis implies that excessive changes in the cash balance lack persistence and that the

market overreacts to the persistence of this component of free cash flow. We also investigate the

hypothesis that insufficient free cash flow leaves the firm in a vulnerable position with respect to

the ability to quickly take advantage of positive net present value investments when they arise

(Opler et al. [1999]). Thus, we divide the change in cash component of free cash flow into three

parts and, relative to DRS, provide a more detailed evaluation of the persistence and pricing of

the change in cash component of free cash flow. The three parts are: normal changes in cash that

move the firm towards an estimated optimal equilibrium level of cash holdings; changes in cash

creating excessive cash on hand; and changes in cash detracting from the optimal level and

creating insufficient cash on hand.

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To estimate the optimal change in cash on hand, we follow a line of finance literature

extending from Keynes [1934], who introduces the notions of transactions costs and

precautionary motives for holding cash, and Miller and Orr [1966] and Jensen and Meckling

[1976] who, respectively, develop the notions of brokerage and inefficient investment costs

associated with holding cash. Opler et al. [1999], BKS and others have extended the theory,

conceptualizing and testing hypotheses and developing models to estimate an optimal amount of

cash holdings and changes in cash holdings.4 As described by BKS (p. 1988-89), the models

include proxies for four factors explaining the level and change in a company‟s cash balance.

Firms manage their cash balance to: (1) avoid transactions costs associated with liquidating

assets (financial and operating) to meet obligations or make investments (Miller and Orr [1966]);

(2) avoid repatriation of foreign earnings that would be taxed at a higher rate (Folie, et al.

[2007]); (3) avoid missing investment opportunities when financing costs are high and cash

flows are risky or the correlation between operating income and investment opportunities is low

(Han and Qiu [2007], Acharya et al. [2007]); and (4) extract rents from shareholders by

entrenched managers of firms with high agency costs (Jensen and Meckling [1976], Jensen

[1986]). For purposes of the exposition of our paper, we refer to the first three factors as

precautionary and the fourth factor as agency-related.

The agency-related factor refers to costs associated with inefficient investment and

perquisites extracted by managers. We rely primarily on the BKS model to estimate optimal

changes in cash for precautionary reasons. The residual of the model provides an estimate of

4 For example, see Keynes [1934], Chudson [1945], Miller and Orr [1966], Vogel and Maddala [1967], Jensen and

Meckling [1976], Myers [1977], Myers and Majluf [1984], Baskin [1987], John [1993], Beltz and Frank [1996],

Mulligan [1997], Harford [1999], and Han and Qiu [2007], among others.

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agency-related changes in cash during each firm-year, where positive (negative) residuals

indicate excessive (insufficient) changes in the cash balance.5

Our paper is closely related to contemporaneous research by Oler and Picconi [OP 2010].

Several differences between our paper and OP emerge from our objectives to extend DRS and

Xie [2001], which are not goals of OP. First, OP‟s model predicts optimal levels of cash and

estimates the level of excess (insufficient) cash as the amount of a positive (negative) residual

from the model. We predict the optimal change in cash and disaggregate the DRS change in cash

variable into normal, excess and insufficient changes. Second, in the spirit of DRS and Xie

[2001], our models predicting accounting and market performance examine differences in the

persistence of and market efficiency with respect to the various components of current year

earnings. Third, OP hypothesize that future accounting performance decreases with excessive or

insufficient cash holdings. We hypothesize that the persistence of excess and insufficient cash

components differ from each other and from the persistence of other components of free cash

flow; i.e., the normal change in cash, distributions to debt holders and distributions to equity

holders. Furthermore, we hypothesize that excess but not insufficient changes in cash potentially

drive the DRS result indicating that the change in cash component of earnings has less

persistence than other components of free cash flow. Fourth, we develop hypotheses based on

predictions of differences in the persistence of the various earnings components that we examine.

Finally, our paper includes the disaggregation of accruals, and we identify discretionary (or

abnormal) accruals using the technique developed in Kothari et al. [2005]. Our paper then

5 Our paper differs from studies that evaluate earnings management through real activities manipulation and

transaction timing (e.g., Roychowdhury [2006]; Cohen et al. [2008]; Gunny [2010]; Zang [2010]) and studies that

evaluate cash flow management (e.g., Zhang [2006, 2009]; Hollie et al. [2010]). Both types of studies analyze

behaviors that potentially affect the cash balance but, to our knowledge, no prior study evaluates the persistence and

market pricing of changes in the cash balance that move the company towards (normal changes in cash) or away

from (abnormal changes in cash) the optimal cash balance.

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develops and tests predictions about the persistence and pricing of the disaggregated accrual and

free cash flow components of earnings vis a vis one another.

Our paper makes a significant contribution to the accounting literature, as, relative to DRS,

we offer a more complete explanation for why changes in cash have less persistence than other

components of free cash flow and earnings. We find that the hubris explanation offered by DRS

only partially explains the lower persistence of the DRS change in cash variable, as both

insufficient and excess changes in cash are associated with lower future earnings. Our paper also

contributes hypotheses and tests of differences in persistence of the various components of the

free cash flow and accrual portions of earnings, and we break up accruals into normal and

abnormal (discretionary) components building on techniques developed by Xie (2001) and

Kothari et al. [2005].

Our results may be summarized as follows. We replicate the DRS finding that the net change

in cash earnings component has less persistence than the net distributions of free cash flow. We

also replicate the results in Xie [2001] indicating that the abnormal (discretionary) accruals drive

the evidence of lower persistence of accruals. In evaluating market efficiency with respect to the

persistence characteristics of earnings components, we find evidence of market inefficiency with

respect to the persistence of accruals and the mispricing of accruals is primarily driven by

abnormal accruals. We find evidence that the market underreacts to the persistence of free cash

flow. However, we fail to reject the null hypotheses that market rationally prices normal changes

in cash and positive abnormal change in cash. The market‟s underreaction to the following three

components of free cash flow drives the underreaction to the total: (i) underreaction to the

persistence of negative abnormal changes in cash; (ii) underreaction to the persistence of net

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distribution to debt holder cash flows; and (iii) underreaction to the persistence of net distribution

to equity holder cash flows.

The relatively large persistence of the distributions to investors and insufficient changes in

cash components of free cash flow is consistent with our expectations. Insufficient changes in

cash fail to provide for the firm‟s precautionary needs. As a result, the firm potentially misses

profitable investment opportunities, and thus negative abnormal changes in cash correspond to

lower future earnings. The market fails to anticipate this relation in pricing negative abnormal

changes in cash. The market also apparently underreacts to the persistence characteristics

associated with the good (bad) earnings prospects signaled by net distributions to (investments

by) debt and equity holders. Our evidence does not support the DRS hubris hypothesis. We find

no evidence of market inefficiency with respect to the persistence characteristics of excessively

positive changes in cash, which might be used for investment in negative net present value

projects.

The rest of this paper is organized as follows. Regarding differences between the persistence

and market pricing of various earnings components, Section 2 develops our expectations based

on prior literature and our new hypotheses referring to normal and abnormal changes in cash.

Section 3 describes the models used to: (a) estimate normal and abnormal accruals; (b) estimate

normal and abnormal changes in cash; and (c) test our hypotheses. Section 4 describes our

sample and data sources. Section 5 provides descriptive statistics. Section 6 presents and

analyzes the results, and Section 7 concludes.

2. Hypotheses and Prior Literature

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This section develops predictions about the relative persistence of the various earnings

components. First, we evaluate the persistence of total accruals relative to the persistence of free

cash flow, the two broadest components of earnings. As described by Richardson et al. [2006],

accruals reflect three types of estimation error: first, accruals lack reliability due to inherent

uncertainty about the future events subject to accrual accounting (e.g., unexpected warranty

claims detracts from the persistence of the accrued warranty liability and corresponding expense);

second, accruals lack persistence when managers use them to manipulate earnings, for example,

towards bright line thresholds either through upward earnings management to meet the threshold

or downward earnings management towards the threshold to build reserves; and third, accruals

lack persistence if they reflect new investment and there are diminishing returns to investment.

The persistence of free cash flow also suffers from manipulation by managers to achieve

performance goals and diminishing returns to investment; however, free cash flows do not lack

reliability due to any inherent uncertainty about the future affecting the actual amount of cash

received or paid currently. Consistent with results in DRS and elsewhere, we expect that free

cash flow has greater persistence than accruals.6

Following DRS, we begin by decomposing free cash flow into changes in cash, distributions

to debt holders, and distributions to equity holders. We then build on the model developed by

BKS to further decompose the change in cash variable into normal and abnormal components,

where abnormal changes refer to movements away from the optimum cash balance, and normal

changes refers to precautionary movements toward the optimum cash balance. We extend Xie

[2001] by disaggregating total accruals into normal and abnormal categories using techniques

developed by Kothari, et al. [2005]. Normal accruals lack reliability due to the inherent

6 All hypotheses stated in the alternative form.

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uncertainty related to estimates of the effects of future events, but do not lack reliability due to

manipulation or diminishing returns to new investment. Therefore, we expect that, consistent

with Xie‟s results, normal accruals have greater persistence than abnormal accruals.

In accord with BKS, normal changes in cash move the company‟s cash balance towards

an estimated optimal level. Positive (negative) normal changes result from increases (decreases)

in the demand for cash holdings in the current period, as reflected in the changes in firms‟

fundamentals. There is no clear a priori difference in the persistence of positive and negative

normal changes in cash. On the other hand, we hypothesize differences in the persistence of

positive versus negative abnormal changes in cash.

Positive (negative) abnormal cash changes result in excessive (insufficient) cash balances.

Compared to positive normal changes in cash which move the company towards the optimum,

we expect positive abnormal changes to have lower persistence, since they represent movement

towards excessive amounts of cash likely to be wasted on low NPV investments or unwarranted

perquisites. Consequently, a dollar abnormal increase in cash is associated with a smaller

increase in future earnings than a dollar normal increase in cash. Compared to negative normal

changes in cash which move the company towards the optimum, we expect negative abnormal

changes to be associated with even lower future earnings, because they move the company

towards insufficient amounts of cash which could lead to missed opportunities to invest in

positive NPV projects. Therefore, a dollar abnormal decrease in cash is associated with a larger

decrease in future earnings than a dollar normal decrease in cash. Thus, we expect abnormal cash

decreases to have greater persistence than abnormal cash increases. Figure 1 illustrates the

hypothesized relations between future earnings, normal changes in cash, and abnormal changes

in cash. Based on the above discussion, we propose the following hypothesis:

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H1: Abnormal negative cash changes have greater persistence than abnormal positive cash

changes.

DRS find that changes in cash have almost identical persistence as accruals. They argue

that changes in cash likely have low persistence because managers can waste cash on negative

NPV projects, firms can window dress the balance sheet to improve perceived financial health;

cash is also subject to manipulation; and cash increases can result in future expenditures on net

operating assets that have diminishing returns to investment. However, DRS do not separate

changes in cash and accruals into normal and abnormal components. The above arguments more

likely apply to abnormal cash changes and abnormal accruals. Our next two hypotheses focus on

the comparison of persistence of abnormal accruals and persistence of abnormal changes in cash.

Assuming that DRS capture the average persistence level of changes in cash (i.e., normal

changes, and positive and negative abnormal changes), we predict that positive abnormal

changes in cash have less persistence than abnormal accruals because they are the least persistent

component of changes in cash. Negative abnormal changes in cash, however, have greater

persistence than abnormal accruals because they are the most persistent component of changes in

cash. Our formal hypotheses are stated as follows:

H2: Abnormal positive changes in cash have less persistence than abnormal accruals.

H3: Abnormal negative changes in cash have greater persistence than abnormal accruals.

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Finally, as described by DRS, firms with positive free cash flow have three choices: they can

retain the cash, distribute the cash to equity holders, or distribute the cash to debt holders. Of the

three options, DRS expect that retaining the cash has the least persistence because: (i) it may be

wasted on negative net present value projects (Jensen [1986], Harford [1999]; (ii) it may

represent a temporary increase due to earnings management through real activities, such as

delaying R&D, advertising, maintenance, etc. (Roychowdary [2006], Gunny [2010]); (iii) it may

be fraudulently misstated (e.g., Parmalat); or (iv) it may be a precursor to acquisition of assets

with diminishing returns to investment. We expect that the above arguments apply relatively

more to our measure of abnormal increases in cash. Therefore, we hypothesize that:

H4: Abnormal increases in cash have lower persistence than normal changes in cash.

Following the arguments in DRS, we also expect total changes in cash to have lower

persistence than distributions to either debt or equity holders. Furthermore, consistent with the

explanations in DRS, we expect net distributions to equity holders to occur only when the firm is

relatively sure of sufficient future income to internally provide for the firm‟s cash needs and,

therefore, we expect net distributions to equity holders to have greater persistence than net

distributions to debt holders (also see Bartov [1991]). Also, as explained by DRS, these

arguments are symmetric, since a firm with two choices for covering a free cash flow shortfall is

more likely to issue equity than debt when the firm expects future losses.

We also compare market efficiency with respect to the various earnings components.

If the stock market tends to treat all components as having similar persistence characteristics,

then we expect market underreaction to the pricing implications of higher persistence earnings

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components, such as negative abnormal changes in cash and distributions to equity holders, and

overreaction to the pricing implications of lower persistence components, such as abnormal

accruals and positive abnormal changes in cash. More specifically, with reference to our

contribution of the further disaggregation of the change in cash component of free cash flow, we

hypothesize that:

H5: Stock prices fail to fully impound the greater persistence characteristics of negative

versus positive abnormal changes in cash.

3. Research Design

Following DRS, we begin by decomposing companies‟ income statements into accrual and

free cash flow components as follows:7

INCOMEt = ACCRUALSt + FCFt, (1)

where: the subscript refers to the fiscal year, INCOMEt equals income before extraordinary items

(IB), ACCRUALS represents total accruals and equals the change in non-cash (operating) assets

(AT – CHE) minus non-debt (operating) liabilities (LT – DLTT – DLC), and FCF represents free

cash flow defined as INCOMEt minus ACCRUALSt.8

Next, we use the approach described in Kothari et al. [2005] to decompose total accruals

into estimates of normal (i.e., non-discretionary) and abnormal (i.e., discretionary) components

as follows.

7 The firm j subscript is suppressed in all models. Variable descriptions include parenthetical references to the

relevant COMPUSTAT “variable name.” Unless otherwise specified, all variables are scaled by average total assets

[(ATt + ATt-1)/2]=NETASSETSt, where the subscript refers to firm j‟s fiscal year.

8 Throughout the paper cash refers to cash plus short-term investments (CHE), and non-cash assets refers to total

assets less cash (AT – CHE). This definition of cash proxies for all financial assets, and the definition of non-cash

assets proxies for operating assets.

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ACCRUALSt = α0 + α1(1/ASSETSt) + α2(ΔSALESt – ΔARt) + α3PPEt + εt (2)

We cross-sectionally estimate model (2) for each industry (two-digit SIC code) and year

requiring at least 10 observations in each industry-year. New variables in (2) are defined as

follows: ΔSALES represents firm j‟s sales in year t minus sales in year t-1 (SALEt – SALEt-1),

ΔAR represents firm j‟s accounts receivable at the end of year t minus accounts receivable at the

end of year t-1 (RECDt – RECDt-1), and PPE represents firm j‟s gross property, plant and

equipment at the end of year t (PPEGTt).

We subtract our initial estimate of firm j‟s normal (non-discretionary) accruals from

model (2) from firm j‟s total accruals in year t in order to obtain an initial estimate of firm j‟s

abnormal (discretionary) accruals for year t. We then subtract our initial estimate of firm j‟s

abnormal accruals from the similarly derived estimate of abnormal accruals for firm k in year t,

where firm k is the firm in firm j‟s industry with the closest year t return on total assets. This

procedure provides a proxy for firm j‟s abnormal accruals, ABNACCjt, which we then subtract

from firm j‟s total accruals, ACCRUALSjt to get our proxy for firm j‟s normal year t accruals,

NACCjt.

We disaggregate the free cash flow component in two steps. The first step follows DRS

and the second step applies recent developments in the finance literature to further disaggregate

the change in cash component of free cash flow into normal and abnormal components, where

the normal (abnormal) component moves the firm‟s cash balance towards (away from) an

estimate of the optimal level. The disaggregation begins with a reformulated balance sheet

equation and the clean surplus relation.

CASH + OPASSETS = OPLIAB + DEBT + EQUITY (3)

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Equation (3) represents the left-hand-side of the balance sheet equation as cash plus all other

assets, where cash includes the cash balance and all other financial assets and the rest of the

firm‟s assets are labeled OPASSETS (i.e., total operating assets). The right-hand-side of the

balance sheet equation includes operating liabilities (labeled OPLIAB), financial liabilities

(labeled DEBT) and common stockholders‟ equity (labeled EQUITY).9 Subtracting operating

liabilities from operating assets yields net operating assets (labeled NETOPASSETS), and

representing the balance sheet equation as changes in each component creates a reformulated

balance sheet equation in (4).

∆CASH + ∆NETOPASSETS = ∆DEBT + ∆EQUITY (4)

Equation (4) represents the clean surplus relation; i.e., the change in common stockholders‟

equity (labeled ∆EQUITY) is completely explained by the difference between two components,

INCOME and net distributions to common stockholders (labeled as INCOME – DIST_EQ).

∆EQUITY = INCOME – DIST_EQ (5)

Substituting the right-hand-side of (5) for ∆EQUITY in (4), renaming the change in net operating

assets ACCRUALS, renaming the change in debt DIST_D (i.e., net distributions to debt holders,

and rearranging terms provides expressions for free cash flow on both sides of equation (6).10

INCOME - ACCRUALS = ∆CASH + DIST_D + DIST_EQ (6)

Free cash flow created in the firm‟s operations equals the firm‟s income less accruals, and free

cash flow available for distribution to providers of capital equals the change in cash plus net

distributions to debt holders plus net distributions to common shareholders. DRS represent the

9 Preferred stockholders equity is included with DEBT.

10 Theoretically, interest expense should be removed from INCOME and considered part of DIST_D. Also, some

portion of cash should be considered an operating asset and the other portion a financing asset. However, for

practical purposes and following DRS, we treat interest expense and interest payable as operating items, and we treat

all cash and other financial assets as financing items. For example, treating any portion of cash as an operating asset

would require changes in that portion to be included in accruals and that would be inconsistent with the way we

normally think of accruals. We do not expect these decisions to materially affect our inferences.

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free cash flow variable in (1) as free cash flow available for distribution to all providers of

capital, the right-hand-side of (6). We reproduce DRS‟ INCOME disaggregation in (7) below.

INCOME = ACCRUALS + ∆CASH + DIST_D + DIST_EQ (7)

In their study of whether the weights on factors hypothesized to affect optimal cash

holdings have changed over time, BKS extend Opler et al. [1999] and develop a model of the

optimal change in cash as follows.

∆CASHt = α0 + α1∆CASHt-1 + α2CASHt-1 + α3INDSIGMAt + α4∆MTBt

+ α5∆SIZEt + α6∆FCFt + α7∆NWCt + α8∆CAPEXPt

+ α9∆LEVt + α10∆R&Dt + α11D∆DIVt + α12∆ACQEXPt + εt (8)

In (8) ∆CASHt-1 and CASHt-1, together, proxy for the difference between the optimal and

actual levels of the cash balance at the end of year t-1. The sum of ∆CASHt-1 and CASHt-1,

is highly correlated with the residual from a model predicting the optimal level of cash at

the end of year t-1 and, therefore, we expect negative coefficients on ∆CASHt-1 and

CASHt-1 as firms holding excess (insufficient) cash at the end of year t-1 tend to move

downward (upward) towards optimum levels during year t.

INDSIGMAt represents the mean of the distribution of standard deviations of free cash

flow, computed over the most recent five years (ending with year t-1), across all firms

with firm j‟s two-digit SIC code. We expect a positive coefficient on INDSIGMAt as we

expect firms in industries with more volatile free cash flows to hold more cash for

precautionary reasons.

∆MTBt represents the change in firm j‟s market to book ratio. We cannot predict the sign

of the coefficient on ∆MTBjt, because, depending on the firm‟s circumstances increases in

the market-to-book ratio can proxy either for increased growth opportunities (Harris and

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Marston [1994]) or for reductions in the firm‟s risk characteristics (Fama and French

[1993], Fergusen and Shockley [2003]). In the former (latter) case, we would expect a

positive (negative) coefficient on ∆MTBjt as the need for precautionary amounts of cash

increases (decreases) with growth opportunities (risk).

∆SIZEt is measured as the natural log of the change in total assets. We expect a negative

coefficient on ∆SIZEt as larger firms benefit from economies of scale that reduce the need

for cash.

We expect a positive coefficient on ∆FCFt as the change in cash is a positive component

of this variable.

∆NWCt represents the change in the firm‟s non-cash working capital. We expect a

negative coefficient on ∆NWCt as firms can substitute working capital for cash on hand.

∆CAPEXPt represents the change in the firm‟s capital expenditures. This variable could

have a positive coefficient on this variable as firms with growing capital expenditures

keep more cash on hand as a precaution against failing to find financing for new capital

expenditures. The variable could also have a negative coefficient as firms may draw on

cash reserves to invest in long-term operating assets.

∆LEVt represents changes in firm j‟s leverage, and we expect a positive sign as increased

leverage increases the risk of failing to find cost effective financing for new investment

opportunities.

∆R&Dt represents changes in R&D expenditures. This variable could have a positive sign

as the firm takes precaution to have access to cash to finance growth opportunities

created by investment in R&D. The variable might also have a negative sign as the firm

draws on cash reserves to take advantage of potentially profitable R&D investments.

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D∆DIVt indicates whether or not cash dividends on common stock increased during year t.

This variable could have a positive coefficient on this variable as a firm increasing

dividends needs to hold more cash in order to avoid missing a dividend payment. The

variable might also have a negative coefficient as the firm draws on excess cash reserves

to pay dividends.

∆ACQEXPt represents the change in cash outflows on acquisitions, and this variable

might have a positive coefficient as the firm needs more cash to avoid missing acquisition

opportunities. On the other hand, the coefficient might be negative, if the firm draws on

excess cash reserves to make acquisitions.

Finally, the residual of the regression, εt, represents the unexplained (abnormal) portion

of the change in cash.

We estimate (8) cross-sectionally for firms with the same two-digit SIC code, save the

coefficient estimates and combine them with actual year t data to predict the change in cash in

year t. We refer to the predicted change in cash as the normal change (N∆CASH), and we refer to

the difference between the actual change and the predicted change as the abnormal change in

cash holdings (ABN∆CASH). We now have proxies for all the variables in our fully

disaggregated model of firm j‟s income for year t:

INCOME = NACC + ABACC + N∆CASH + ABN∆CASH + DIST_D + DIST_EQ (9)

To test our hypotheses about differences in the persistence of various combinations of the

variables in (9), we estimate various forms of the following regression:

INCOMEt+1 = β0 + β1NACCt + β2ABACCt + β3N∆CASHt + β4ABN∆CASHt+

+ β5ABN∆CASHt- + β6DIST_EQt + β7DIST_Dt + εt+1 (10)

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where ABN∆CASHt+ and ABN∆CASHt

- represent positive and negative ABN∆CASH,

respectively.

To evaluate market efficiency with respect to differences in the persistence of the various

income components in (10), we adopt the Mishkin (1983) approach introduced to the accounting

literature by Sloan (1996) and applied by DRS, among many others. We infer overweighting

(underweighting) of a specific income component if the market attributes a higher (lower)

valuation coefficient to it than the weight implied in its association with future income. We

jointly estimate the following efficient forecasting and rational expectations pricing models:

INCOMEt+1 = β0 + β1NACCt + β2ABACCt + β3N∆CASHt + β4ABN∆CASHt+

+ β5ABN∆CASHt- + β6DIST_EQt + β7DIST_Dt + εt+1 (11)

ARETt+1 = γ (INCOMEt+1 - β0*- β1

*NACCt - β2

*ABNACCt - β3

*N∆CASHt - β4

*ABN∆CASHt

+

- β5*ABN∆CASHt

- - β6

*DIST_EQt - β7

*DIST_Dt) + εt+1 (12)

where ARET is annual buy-and-hold stock return calculated starting four months after the fiscal

year-end, adjusted by the return on the CRSP size-decile portfolio in which the firm belongs.

Market efficiency with respect to a specific component of income imposes the constraint

that the valuation coefficient equals its counterpart in the forecasting model. This non-linear

constraint requires that the stock market rationally anticipates the future income implications of

each current income component. As in Mishkin (1983), we estimate (11) and (12) using iterative

weighted nonlinear least squares. The test statistic is a likelihood ratio distributed asymptotically

Chi-square (q):

2 × n × ln(SSRc / SSR

u)

where:

q = the number of constraints imposed by market efficiency

19

n = the number of observations in each equation

SSRc = the sum of squared residuals from the constrained weighted system

SSRu = the sum of squared residuals from the unconstrained weighted system

We reject the rational pricing of any component of income if the above likelihood ratio statistic

is sufficiently large.

4. Sample and data sources

Examining our hypotheses regarding market efficiency with respect to persistence

characteristics of earnings components requires access to corporate financial statement and

returns data. Table 1 describes our sample selection process, which begins with all 398,649 firm-

year observations on the 2008 COMPUSTAT annual database spanning the years 1971-2008.

We drop 94,074 observations in regulated industries (SIC codes 4900-4999 and 6000-6999).

Relative to other industries, regulated industries, including the financial and utilities industries,

have different earnings management incentives related to regulatory requirements and different

financial statement structures. Next, we lose 202,556 (10,040) observations without sufficient

data to estimate our model of normal changes in cash (accruals). Then we lose another 10,752

observations without one-year ahead earnings information. Finally, 26,630 observations do not

have sufficient returns data to conduct our market efficiency tests, leaving us with a sample of

54,597 observations.

(Insert Table 1 here)

5. Descriptive statistics and estimation of normal and abnormal changes in cash

20

Table 2 shows the results of estimating model (8) where we separate changes in cash into

those that move the company towards the optimum, those that move the company‟s cash balance

away from the optimum in the direction of excess cash, and those that move the company away

from the optimum in the direction of insufficient cash. To estimate the optimal change in cash,

we rely on the model developed in Bates et al. [2009] which, in turn, builds on the models of

optimal levels of cash holdings developed in Opler et al. [1999].

(Insert Table 2 here)

As expected, Table 2 shows that the coefficients on ∆CASHt-1 and CASHt-1 are significantly

negative, indicating that firms starting the year with either excess or insufficient cash tend to

move towards a normal level during year t.11

The coefficient on INDSIGMA is positive and

significant, as expected, indicating that firms in industries with volatile cash flows tend to retain

more cash for precautionary reasons.12

The coefficient on MTB is positive and significant (at the

5% level, two tailed), indicating that the greater growth opportunities outweigh the lower risk for

firms with higher market to book ratios in predicting the direction of the change in cash. As

expected, the coefficient on SIZE is positive and significant, indicating that economies of scale

allow larger firms to retain less cash. As expected, the coefficient on ∆FCF is positive and

significant, since the change in cash is a positive component of the change in free cash flow. As

11

Ideally, rather than ∆CASHt-1 and CASHt-1, we would use the negative of the residual from a levels model of the

normal cash on hand at the beginning of the year to indicate the direction of the year t change in cash for firms

moving towards an estimated optimal level. However, using ∆CASHt-1 and CASHt-1 to proxy for the direction and

distance from the optimum at the end of year t-1 has the following advantages: (a) it follows the approach used in

BKS; (b) it mitigates measurement error inherent in using the residual from a regression as a proxy for an underlying

construct; and (c) ∆CASHt-1 and CASHt-1 are highly correlated with the residual from a regression predicting the

optimal level of cash at the end of year t-1. 12

Unless otherwise specified, “significant” means statistically significant at the 1% level (two-tailed).

21

expected, the coefficient on ∆NWC is negative and significant, indicating that the relatively

liquid nature of working capital creates a substitution effect between changes in working capital

and changes in cash. The coefficient on ∆CAPEXP is negative and significant, indicating that the

need to draw on cash reserves outweighs any growth opportunities associated with large capital

expenditures. Surprisingly, the coefficient on ∆LEV is negative and significant, suggesting that

countervailing forces outweigh the pressure to increase cash to avoid default as leverage

increases. Finally, R&D expenditures, dividend payments and acquisition expenditures all use

cash, so it is not surprising to find a negative relation between these three variables and the

change in cash.

Table 3 provides descriptive statistics for the whole sample and two subsamples. The first

(second) subsample includes observations with positive (negative) residuals from model (8).

While we see only a relatively small difference in total income between the two subsamples,

Table 3, Panel D, shows that the two subsamples contain observations with quite different

income component characteristics. In fact, the means and medians of all income components

differ significantly between the two subsamples. Firms accumulating excess cash in year t [i.e.,

positive residual from model (8)] have significantly smaller accruals (both normal and abnormal)

and significantly larger free cash flow. By construction, firms accumulating excess cash have

larger total changes in cash, driven by the abnormal component. However, the predicted (i.e.,

normal) change in cash is smaller in the subsample with positive residuals from model (8). With

respect to the rest of the components of free cash flow, the positive (negative) abnormal change

in cash subsample has significantly larger distributions to debt (equity) holders. Finally, firms

accumulating excess cash in year t have larger abnormal returns in year t+1.

22

(Insert Table 3 here)

Table 4 shows a correlation matrix including all of the income component variables and the

returns variable. On a univariate basis, relative to the relation between accruals and next year‟s

earnings (0.12 Pearson correlation), we see a stronger relation between free cash flows and next

year‟s income (0.41 correlation). Consistent with Sloan [1996], it looks like the market does not

effectively distinguish differences in persistence of cash flows and accruals. The univariate

results are consistent with market underreaction to the persistence of free cash flows and

overreaction to the persistence of accruals, as we see a positive (negative) relation between free

cash flow (accruals) and next year‟s returns. Consistent with Xie [2001], the abnormal

component of accruals drives the negative relation between total accruals and future returns;

whereas, consistent with DRS, net distributions to debt and equity holders drives the positive

relation between free cash flow and future returns. Also, net distributions to equity holders drives

the large persistence of free cash flow relative to other earnings components.

(Insert Table 4 here)

Consistent with the descriptive statistics in Table 3, when we split the samples based on the

sign of abnormal changes in cash we see large differences in univariate correlations between the

variables. In particular, consistent with our arguments in support of H3 in the positive (negative)

residual subsample, the abnormal change in cash is negatively (positively) associated with next

year‟s income. Furthermore, it appears that the market does not recognize these differences in

persistence, as Table 4 Panel B (C) shows that the abnormal change in cash is negatively

23

(positively) correlated with future returns. The univariate results should be read with caution,

since Table 4 shows the well-known negative relation between free cash flow and accruals

(-0.658 correlation).

6. Results

6.1 Persistence results

Table 5 reports the first set of tests of our hypotheses. As described in Table 5, INCOME is

highly persistent, with a coefficient of 0.741, which approaches the equivalent of a random walk

model of annual earnings (e.g., Lookabill [1976]). However, as hypothesized, the persistence of

the components of earnings differ from each other. Consistent with DRS, Sloan [1996] and

others, relative to accruals, Table 5 shows a significantly higher persistence coefficient on free

cash flow (0.752 versus 0.672). Similarly, consistent with Xie [2001] and DRS, respectively,

normal levels of accruals have higher persistence than abnormal accruals (0.558 versus 0.536),

and the persistence of distributions to equity holders (0.711) drives the higher persistence of free

cash flow.

(Insert Table 5 here)

With respect to our new variables, normal and abnormal changes in cash, consistent with H1,

we find significantly higher persistence of negative abnormal changes in cash than positive

abnormal changes in cash (0.588 versus 0.421). We attribute this finding to the hypothesized

general condition that, as the firm moves away from the estimated optimal amount of cash,

predictions of future earnings decline. That is, as the firm accumulates excess cash, it becomes

24

more likely that managers will invest in low NPV projects or consume the cash in perquisites.

Similarly, as the firm accumulates less cash than the predicted optimum, managers may have to

forego investment in positive NPV projects. Consistent with H2 and H4, respectively, we find

that positive abnormal cash changes have significantly less persistence than abnormal accruals

(0.421 versus 0.538) and normal changes in cash (0.421 versus 0.556). Consistent with H3, we

find stronger persistence of negative abnormal cash changes than abnormal accruals (0.588

versus 0.538, significant at the 5% level, one tailed). Finally, consistent with the arguments and

evidence in DRS, changes in cash have significantly less persistence (0.610) than the persistence

of net distributions to debt holders (0.699) which in turn have significantly less persistence than

net distributions to equity holders (0.744).

Overall, our results are consistent with prior research, and the variables we introduce to

literature related to the persistence of earnings components behave according to our hypotheses.

Our new variables provide more detailed analysis of the change in cash component of free cash

flow. Essentially, we use the BKS model of optimal changes in cash to split each firm-year‟s

change in cash into three components: normal changes in cash (the model‟s predicted amount),

abnormal increases (positive residuals from the model) and abnormal decreases (negative

residuals from the model). Relative to abnormal accruals, normal changes in cash and abnormal

cash decreases, abnormal increases in cash have low persistence in relation to next year‟s

earnings, presumably due to costs associated with moving away from the estimated optimum and

towards excessive cash balances. Relative to normal and insufficient changes in cash, excessive

cash increases are likely to be invested in low or perhaps even negative return projects and to

provide unneeded perquisites for the firm‟s employees/managers. Thus, excessive increases in

cash have low persistence as they are associated with relatively low future income. On the other

25

hand, insufficient cash increases (negative residuals from the model) have relatively high

persistence as they, too, lead to lower future income due to costs associated with missed

investment opportunities.

6.2 Market pricing results

Table 6 shows the results of our tests of the market efficiency hypothesis (H5) using the

Mishkin test introduced by Sloan [1996] and used in Xie [2001], DRS and many other related

papers. Note that we report mean annual coefficients in Table 5; whereas, consistent with prior

research, Table 6 reports Mishkin test results using the entire sample pooled across firms and

over time. Therefore, the actual persistence parameters reported in Table 6 are somewhat

different from those in Table 5.

(Insert Table 6 here)

Panel A of Table 6 presents results for the basic income autoregression model. The

persistence coefficient on INCOME is 0.773. If the market correctly anticipates the persistence

of current income, then the implied persistence parameter should be the same as the actual

persistence parameter. However, we find that the implied persistence parameter is 0.697, which

is significantly less than 0.773. It appears that the market underreacts to the overall persistence of

income. This result contrasts with Sloan‟s [1996] and DRS‟s findings, possibly due to

differences in sample composition.

Panel B presents the results for the test of market efficiency with respect to accruals and free

cash flow components of income. Consistent with the results in Table 5, accruals are

26

significantly less persistent than free cash flows (0.684 versus 0.774). We find that the implied

persistence coefficient of accruals in stock prices is significantly higher than the actual

persistence coefficient (0.750 versus 0.684); whereas the implied persistence coefficient of free

cash flows in stock prices is significantly lower than the actual persistence coefficient (0.614

versus 0.774). These results are consistent with Sloan [1996], suggesting that the market

overestimates the persistence of accruals but underestimates the persistence of free cash flows.

Following DRS, Panel C further decomposes free cash flows into changes in cash, net

distributions to equity holder and net distributions to debt holder. We continue to find that net

distributions to equity holders have the highest persistence level among all components of

earnings (0.789), followed by net distributions to debt holders (0.671). The corresponding

implied persistence parameters in stock prices are 0.579 for net distributions to equity holders

and 0.487 for net distributions to debt holders. It appears that the market underreacts to the

persistence of these two free cash flow components. However, we find that the implied

persistence coefficient of changes in cash is not significantly different from the actual persistence

coefficient of changes in cash (0.557 versus 0.573), suggesting that the market anticipates the

lower persistence of changes in cash and efficiently incorporates the information in stock price.

Panel D reports the results after we disaggregate accruals and changes in cash into normal

and abnormal components. Consistent with Xie [2001], we observe market overpricing of

abnormal accruals, but not normal accruals. We also fail to reject the hypothesis that the market

efficiently anticipates and prices the persistence of normal changes in cash. However, the market

seems to be significantly less efficient in pricing the abnormal changes in cash (0.344 implied

market persistence coefficient versus 0.411 actual persistence coefficient).

27

The last panel (Panel E) presents results for our full decomposition of earnings. Consistent

with our prediction (H1) and results in Table 5, the persistence coefficient on negative abnormal

changes in cash is significantly greater than that on positive abnormal changes in cash (0.577

versus 0.310). The market seems to be able to anticipate the persistence of positive abnormal

changes in cash and efficiently prices this earnings component (0.309 versus 0.310). We find that

the mispricing of abnormal changes in cash is primarily driven by negative changes in cash

(0.402 versus 0.577). The market appears to underestimate the persistence of negative changes in

cash. Consistent with Xie [2001], we show that the overpricing of abnormal accruals is more

severe than normal accruals because we reject the null hypothesis that the market overprices both

components to the same extent. We also reject the null hypotheses that the market mispricing of

abnormal accruals is similar to either positive or negative abnormal changes in cash. Finally, the

likelihood ratio statistic of 342.13 easily rejects the null hypothesis that the market efficiently

prices all earnings components.

Overall, based on the results of the Mishkin test, we conclude that the market overestimates

the persistence of accruals, especially abnormal accruals, but underestimates the persistence of

free cash flows, especially net distributions to equity holders, net distributions to debt holders,

and insufficient increases in cash.13

6.3 Hedge-portfolio results

Table 7 shows the returns to a trading strategy associated with each of the earnings

components. Consistent with prior literature (e.g., Sloan [1996]), a strategy that goes long in

13

Following Kraft et al. [2007], we also perform year-by-year Mishkin tests and calculate the mean coefficient from

the forecasting equation and the pricing equation. Similar to Kraft et al.‟s [2007] findings, the accrual anomaly

disappears in the year-by-year analysis. Nevertheless, all the results with respect to free cash flow components

remain robust. Furthermore, we use the OLS approach to examine market pricing of various earnings components as

advocated by Kraft et al. [2007] and obtain similar results.

28

stocks with large negative accruals (bottom decile) and short in stocks with large positive

accruals (top decile) produces a hedge portfolio return of 11.2% annually. Consistent with Xie

[2001], the total accruals result appears to be driven by returns to a similar trading strategy with

long positions in the bottom decile and a short position in the top decile of abnormal accruals,

which produces a hedge portfolio return of 10.7% (t-statistic = 9.5). When applied to normal

accruals, the trading strategy produces insignificant trading profits. These results should be

interpreted with caution as the correlation matrix in Table 4 finds a significant negative relation

between accruals and free cash flows, and Table 7 also suggests that a trading strategy that is

long in stocks in the highest free cash flow decile and short in stocks in the lowest free cash flow

decile generates an 11.8% abnormal return (t-statistic = 10.9). Interestingly, with respect to our

insufficient cash increase variable, a trading strategy of long positions in decile 10 (least negative)

and short position in decile 1 (most negative) produces a hedge portfolio return of 9.3%.

Furthermore, abnormal returns increase monotonically from the 10th decile to the 1st decile of

this component of the change in cash and free cash flow.

(Insert Table 7 here)

7. Conclusion

This paper develops an approach allowing more detailed evaluation of the DRS finding that

the change in cash component of free cash flow has less persistence than the distributions to

capital providers components of free cash flow and that the market appears to overreact to the

persistence characteristics of the change in cash in a similar manner as the market overreacts to

the persistence characteristics of accruals. We replicate the DRS finding that the net change in

29

cash component of earnings has less persistence than the net distributions of free cash flow. We

also replicate the results in Xie [2001] indicating that abnormal (discretionary) accruals drive the

evidence of lower persistence of accruals.

In evaluating market efficiency with respect to the persistence characteristics of earnings

components, we find evidence of market inefficiency with respect to the persistence of abnormal

accruals. We also find evidence that the market underreacts to the persistence of free cash flow,

and underreaction to the following three components of free cash flow drives the underreaction

to the total: (i) underreaction to the persistence of negative abnormal changes in cash (i.e.,

insufficient increases); (ii) underreaction to the persistence of net distribution to debt holder cash

flows; and (iii) underreaction to the persistence of net distribution to equity holder cash flows.

The relatively large persistence of these three components of free cash flow is consistent with

our hypotheses. When changes in cash are abnormally negative, the firm is moving in the

direction of having insufficient cash to meet its precautionary needs. As a result, the firm is more

likely to miss positive NPV investment opportunities, and thus negative abnormal changes in

cash correspond to lower future earnings. The market apparently misunderstands this relation.

The market also apparently underreacts to the persistence characteristics associated with the

good (bad) earnings prospects signaled by net distributions to (receipts from) debt and equity

holders. Overall, our evidence does not support the DRS hubris hypothesis, because we find no

evidence of market inefficiency with respect to the persistence characteristics of excessively

positive changes in cash, which might be used for investment in negative NPV projects.

Our research question is particularly important in light of the FASB‟s disclosure framework

project that seeks to define essential information for investor decisions. Understanding

differences in the persistence of various income components provides important input into

30

policy-making debates about information to require in financial reports without creating

disclosure overload. As currently organized, neither financial statements nor financial statement

footnotes provide a clear distinction between accrual and free cash flow components of any

dimension of net income (e.g., income from continuing operations, net income, etc.). If, indeed,

components of accrual driven and free cash flow driven earnings have different implications for

the prediction of future earnings and cash flows, then disclosing the make-up of the different

income components would provide useful (and, therefore, “essential”) information for investors.

31

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Figure 1 Hypothesized relations between future earnings, normal changes in cash and abnormal

changes in cash

Changes in cash holdings

Future earnings

Normal changes in cash holdings

Positive abnormal

changes in cash holdings

Negative abnormal changes in

cash holdings

35

Table 1 Sample selection procedure

All firm-years on the 2008 version of Compustat annual database 398,649

Less:

Firm-years in the regulated industries (SIC 6000-6999, 4900-4999) (94,074)

Firm-years without sufficient data to estimate normal change in cash holdings (202,556)

Firm-years without sufficient data to estimate normal level of accruals (10,040)

Firm-years with missing earnings information for year t+1 (10,752)

Firm-years with missing size-adjusted stock returns in year t+1 (26,630)

Final sample 54,597

36

Table 2 Estimation of normal change in cash holdings

Mean Std. Dev. t value Q1 Median Q3

INTERCEPT 0.006 0.013 2.83 ***

-0.005 0.006 0.013

∆CASHt-1 -0.027 0.058 -2.83 ***

-0.065 -0.037 0.011

CASHt-1 -0.108 0.082 -8.02 ***

-0.148 -0.104 -0.062

INDSIGMAt 0.022 0.048 2.85 ***

0.003 0.020 0.032

∆MTBt 0.003 0.008 2.10 **

-0.000 0.002 0.005

∆SIZEt 0.191 0.053 22.05 ***

0.151 0.181 0.226

∆FCFt 0.091 0.055 10.01 ***

0.030 0.106 0.137

∆NWCt -0.092 0.079 -7.06 ***

-0.151 -0.104 -0.009

∆CAPEXPt -0.072 0.064 -6.87 ***

-0.108 -0.058 -0.024

∆LEVt -0.026 0.033 -4.81 ***

-0.045 -0.023 0.001

∆R&Dt -0.095 0.202 -2.86 ***

-0.132 -0.040 0.020

D∆DIVt -0.002 0.006 -2.16 **

-0.005 -0.001 0.002

∆ACQEXPt -0.056 0.103 -3.34 ***

-0.111 -0.038 -0.001

# of years 37

Ave. # of obs. 2,757

Adj. R2

35.16%

We run the following regression model cross-sectionally within each year over 1972 to 2008 to estimate normal

change in cash holdings:

∆CASHit = α0 + α1∆CASHit-1 + α2CASHit-1 + α3INDSIGMAit + α4∆MTBit + α5∆SIZEit + α6∆FCFit + α7∆NWCit

+ α8∆CAPEXPit + α9∆LEVit + α10∆R&Dit + α11D∆DIVit + α12∆ACQEXPit + εit

The sample contains 102,019 firm-year observations that have sufficient data to calculate all variables in the model.

We report the mean, median, standard deviation, first quartile (Q1) and third quartile (Q3) of the distribution of each

coefficient across all years. t values are calculated using the standard error of the mean coefficients. ***

, **

, and *,

respectively, indicate 0.01, 0.05 and 0.10 significance levels in two-tailed tests.

CASH = the balance of cash and short-term investments (CHE), scaled by average total assets (AT). MTB = market

to book, calculated as [book value of total assets (AT) – book value of equity (CEQ) + market value of equity

(PRCC_F×CSHO)] / total assets (AT). SIZE = the natural log of total assets (TA). FCF = free cash flows, scaled by

average total assets. Free cash flows are defined as income (IB) less total accruals. Total accruals are the change in

noncash assets (∆AT – ∆CHE) less the change in nondebt liabilities (∆LT – ∆DLTT – ∆DLC). NWC = net working

capital (WCAP) less cash and short-term investments (CHE), scaled by average total assets (AT). CAPEXP = capital

expenditures (CAPX) scaled by average total assets (AT). LEV = the sum of long-term debt (DLTT) and debt in

current liabilities (DLC), divided by total assets (AT). R&D = R&D expense (XRD) scaled by average total assets

(AT). D∆DIV = 1, if common dividends (DVC) increase in the current year; and 0 otherwise. ACQEXP = cash

outflows on acquisitions (AQC) scaled by average total assets (AT). INDSIGMA = the mean of the standard

deviations of FCF over 5 years for all firms in the same industry (2-digit SIC).

37

Table 3 Descriptive statistics

Panel A: Full sample (N = 54,597) Variable Mean Std. Dev. Q1 Median Q3

INCOMEt 0.022 0.135 0.005 0.048 0.087

INCOMEt+1 0.018 0.158 0.003 0.047 0.087

ACCRUALt 0.044 0.159 -0.032 0.036 0.113

NACCt 0.068 0.198 -0.036 0.052 0.156

ABNACCt -0.024 0.229 -0.131 -0.013 0.096

FCFt -0.023 0.177 -0.076 0.006 0.071

∆CASHt 0.012 0.118 -0.018 0.002 0.034

N∆CASHt 0.012 0.066 -0.012 0.013 0.037

ABN∆CASHt 0.000 0.093 -0.039 -0.009 0.027

DIST_EQt -0.019 0.140 -0.015 0.004 0.027

DIST_Dt -0.015 0.107 -0.045 0.000 0.026

ARETt+1 0.004 0.517 -0.306 -0.062 0.208

Panel B: Subsample of firms with positive abnormal change in cash holdings (N = 22,827) Variable Mean Std. Dev. Q1 Median Q3

INCOMEt 0.019 0.153 0.000 0.049 0.096

INCOMEt+1 0.018 0.171 0.002 0.051 0.095

ACCRUALt -0.012 0.152 -0.074 -0.005 0.061

NACCt 0.054 0.202 -0.051 0.042 0.145

ABNACCt -0.067 0.228 -0.172 -0.048 0.056

FCFt 0.032 0.170 -0.019 0.052 0.115

∆CASHt 0.073 0.134 0.006 0.039 0.094

N∆CASHt 0.009 0.075 -0.021 0.003 0.030

ABN∆CASHt 0.068 0.094 0.015 0.037 0.082

DIST_EQt -0.035 0.170 -0.023 0.002 0.028

DIST_Dt -0.000 0.102 -0.017 0.001 0.030

ARETt+1 0.014 0.530 -0.304 -0.059 0.219

Panel C: Subsample of firms with negative abnormal change in cash holdings (N = 31,770) Variable Mean Std. Dev. Q1 Median Q3

INCOMEt 0.025 0.121 0.008 0.046 0.082

INCOMEt+1 0.017 0.148 0.004 0.045 0.081

ACCRUALt 0.085 0.152 0.001 0.066 0.146

NACCt 0.078 0.194 -0.026 0.059 0.163

ABNACCt 0.007 0.225 -0.100 0.010 0.122

FCFt -0.062 0.171 -0.108 -0.023 0.033

∆CASHt -0.031 0.081 -0.040 -0.007 0.004

N∆CASHt 0.014 0.059 -0.003 0.018 0.041

ABN∆CASHt -0.048 0.052 -0.061 -0.033 -0.016

DIST_EQt -0.007 0.113 -0.012 0.005 0.027

DIST_Dt -0.026 0.109 -0.062 -0.002 0.023

ARETt+1 -0.004 0.507 -0.307 -0.065 0.200

38

Panel D: Comparison of firms with positive and negative change in cash holdings Diff. in Mean t value Diff. in Median z value

INCOMEt -0.006 -4.55 ***

0.003 7.02 ***

INCOMEt+1 0.000 0.74

0.006 11.12 ***

ACCRUALt -0.097 -74.15 ***

-0.071 -77.00 ***

NACCt -0.024 -14.13 ***

-0.017 -15.02 ***

ABNACCt -0.074 -38.21 ***

-0.058 -40.49 ***

FCFt 0.094 63.39 ***

0.075 81.51 ***

∆CASHt 0.104 104.23 ***

0.046 135.01 ***

N∆CASHt -0.005 -7.71 ***

-0.015 -37.80 ***

ABN∆CASHt 0.116 168.16 ***

0.070 199.62 ***

DIST_EQt -0.027 -21.31 ***

-0.003 -10.00 ***

DIST_Dt 0.026 28.30 ***

0.003 30.92 ***

ARETt+1 0.018 4.10 ***

0.006 2.65 ***

This table reports descriptive statistics on income, the accrual and cash flow components of income and size-

adjusted stock returns for our sample firms from 1972 to 2008. Panel A contains the full sample. Panel B and C

contain subsamples of firms with positive and negative abnormal change in cash holdings, respectively. Panel D

compares the means and medians of the variables between subsample of firms with positive and negative abnormal

change in cash holdings. t tests are used to test differences between the means. Wilcoxon two-sample tests are used

to test differences between the medians. ***

, **

, and * denote significance at the 0.01, 0.05 and 0.10 levels,

respectively (two-tailed tests).We winsorize all variables at 1% and 99% levels.

INCOME = income before extraordinary items (IB) scaled by average total assets (AT). ACCRUAL = total accruals,

defined as the difference between change in noncash assets (AT – CHE) and change in nondebt liabilities (LT –

DLTT – DLC), scaled by average total assets (AT). ABNACC = performance-matched abnormal accruals (Kothari et

al. [2005]). We match each firm-year with another from the same industry and year on return on assets. ABNACC is

calculated as the modified Jones model residual in year t minus the matched firm‟s modified Jones model residual in

year t. NACC = normal accruals, calculated as the difference between ACCRUAL and ABNACC. FCF = free cash

flows, scaled by average total assets. Free cash flows are defined as the difference between INCOME and

ACCRUAL. CASH = the balance of cash and short-term investments (CHE), scaled by average total assets (AT).

N∆CASH = normal level of change in cash holdings, defined as the predicted value of the model in Table 2.

ABN∆CASH =abnormal level of change in cash holdings, defined as the residual of the model in Table 2. DIST_EQ

= net capital distributions to equity holders [(-1) × (∆AT - ∆LT – IB)], scaled by average total assets (AT). DIST_D

= net capital distributions to debt holders [(-1) × (∆DLTT + ∆DLC)], scaled by average total assets (AT). ARETt+1 =

annual buy-and-hold stock return calculated starting four months after the fiscal year-end, adjusted by the CRSP

size-decile portfolio return in which the firm belongs.

39

Table 4 Correlation matrix

Panel A: Full sample (N = 54,597) INCOMEt+1 ACCRUALt NACCt ABNACCt FCFt ∆CASHt N∆CASHt ABN∆CASHt DIST_EQt DIST_Dt ARETt+1

INCOMEt+1 0.120***

0.129***

-0.032***

0.412***

0.082***

0.132***

-0.016***

0.390***

0.042***

0.196***

ACCRUALt 0.220***

0.520***

-0.658***

-0.141***

0.121***

-0.350***

-0.221***

-0.617***

-0.058***

NACCt -0.702***

-0.032***

0.034***

0.111***

-0.056***

0.008**

-0.102***

-0.002

ABNACCt -0.451***

-0.133***

-0.016***

-0.205***

-0.172***

-0.354***

-0.039***

FCFt 0.235***

0.067***

0.303***

0.569***

0.579***

0.070***

∆CASHt 0.689***

0.833***

-0.365***

-0.008**

-0.013***

N∆CASHt 0.310***

-0.273***

-0.129***

-0.021***

ABN∆CASHt -0.271***

0.118***

0.004

DIST_EQt -0.042***

0.057***

DIST_Dt 0.048***

ARETt+1

Panel B: Subsample of firms with positive abnormal change in cash holdings (N = 22,827) INCOMEt+1 ACCRUALt NACCt ABNACCt FCFt ∆CASHt N∆CASHt ABN∆CASHt DIST_EQt DIST_Dt ARETt+1

INCOMEt+1 0.158***

0.144***

-0.022***

0.477***

-0.083***

0.013***

-0.193***

0.436***

0.035***

0.192***

ACCRUALt 0.228***

0.485***

-0.527***

0.032***

0.149***

-0.106***

-0.209***

-0.529***

-0.050***

NACCt -0.718***

0.010 0.054***

0.101***

-0.013*

0.016**

-0.094***

0.012*

ABNACCt -0.379***

-0.034***

0.008 -0.070***

-0.160***

-0.284***

-0.044***

FCFt -0.052***

-0.037***

-0.078***

0.635***

0.474***

0.069***

∆CASHt 0.811***

0.876***

-0.563***

-0.086***

-0.056***

N∆CASHt 0.540***

-0.443***

-0.112***

-0.047***

ABN∆CASHt -0.547***

-0.036***

-0.041***

DIST_EQt -0.076***

0.080***

DIST_Dt 0.035***

ARETt+1

40

Panel C: Subsample of firms with negative abnormal change in cash holdings (N = 31,770) INCOMEt+1 ACCRUALt NACCt ABNACCt FCFt ∆CASHt N∆CASHt ABN∆CASHt DIST_EQt DIST_Dt ARETt+1

INCOMEt+1 0.101***

0.116***

-0.040***

0.386***

0.328***

0.257***

0.230***

0.345***

0.047***

0.200***

ACCRUALt 0.201***

0.512***

-0.702***

-0.062***

0.088***

-0.383***

-0.334***

-0.671***

-0.059***

NACCt -0.726***

-0.036***

0.087***

0.117***

-0.042***

-0.011**

-0.098***

-0.012**

ABNACCt -0.465***

-0.119***

-0.050***

-0.236***

-0.235***

-0.382***

-0.030***

FCFt 0.376***

0.185***

0.547***

0.629***

0.637***

0.067***

∆CASHt 0.755***

0.631***

0.003 -0.051***

0.016***

N∆CASHt 0.245***

-0.055***

-0.140***

0.003

ABN∆CASHt 0.273***

0.177***

0.035***

DIST_EQt 0.014***

0.038***

DIST_Dt 0.055***

ARETt+1

This table presents pairwise Pearson correlations for the earnings components. Panel A contains the full sample. Panel B and C contain subsamples of firms with

positive and negative abnormal change in cash holdings, respectively. ***

, **

, and * indicate 0.01, 0.05 and 0.10 significance levels in a two-tailed test,

respectively.

INCOME = income before extraordinary items (IB) scaled by average total assets (AT). ACCRUAL = total accruals, defined as the difference between change in

noncash assets (AT – CHE) and change in nondebt liabilities (LT – DLTT – DLC), scaled by average total assets (AT). ABNACC = performance-matched

abnormal accruals (Kothari et al. 2005). We match each firm-year with another from the same industry and year on return on assets. ABNACC is calculated as the

modified Jones model residual in year t minus the matched firm‟s modified Jones model residual in year t. NACC = normal accruals, calculated as the difference

between ACCRUAL and ABNACC. FCF = free cash flows, scaled by average total assets. Free cash flows are defined as the difference between INCOME and

ACCRUAL. CASH = the balance of cash and short-term investments (CHE), scaled by average total assets (AT). N∆CASH = normal level of change in cash

holdings, defined as the predicted value of the model in Table 2. ABN∆CASH =abnormal level of change in cash holdings, defined as the residual of the model in

Table 2. DIST_EQ = net capital distributions to equity holders [(-1) × (∆AT - ∆LT – IB)], scaled by average total assets (AT). DIST_D = net capital distributions

to debt holders [(-1) × (∆DLTT + ∆DLC)], scaled by average total assets (AT). ARETt+1 = annual buy-and-hold stock return calculated starting four months after

the fiscal year-end, adjusted by the CRSP size-decile portfolio return in which the firm belongs.

41

Table 5 Regression analyzing the persistence of earnings components

MODEL 1 MODEL 2 MODEL 3 MODEL 4 MODEL 5

Coeff. t value Coeff. t value Coeff. t value Coeff. t value Coeff. t value

INTERCEPT 0.001 0.67

0.005 2.42 **

0.007 3.99 ***

0.008 3.75 ***

0.012 6.23 ***

INCOMEt 0.741 49.84 ***

ACCRUALt

0.672 50.78 ***

0.610 40.31 ***

NACCt

0.558 33.47 ***

0.561 33.89 ***

ABNACCt

0.536 30.25 ***

0.538 30.58 ***

FCFt

0.752 58.29 ***

∆CASHt

0.610 31.17 ***

N∆CASHt

0.556 23.85 ***

0.556 23.66 ***

ABN∆CASHt

0.479 18.19 ***

ABN∆CASHt+

0.421 12.46 ***

ABN∆CASHt-

0.588 21.87

***

DIST_EQt

0.744 43.70

0.711 40.48 ***

0.699 40.30 ***

DIST_Dt

0.669 40.92

0.616 36.10 ***

0.609 34.98 ***

Adj. R2

41.05% 41.35%

38.30%

35.48%

35.69%

Test of coefficient combinations

Coeff. t value

Coeff. t value

Coeff. t value

Coeff. t value

ACCRUAL – FCF -0.080 -11.08 ***

ACCRUAL – ∆CASH 0.000 0.02

DIST_EQ – DIST_D 0.075 4.16 ***

0.095 5.59 ***

0.090 5.40 ***

NACC – ABNACC

0.022 6.55 ***

0.023 6.76 ***

N∆CASH – ABN∆CASH

0.077 2.99 ***

N∆CASH – ABN∆CASH+

0.135 4.18 ***

N∆CASH – ABN∆CASH-

-0.032 -1.21

NACC – N∆CASH

0.002 0.11

0.005 0.22

ABNACC– ABN∆CASH

0.057 2.81 ***

ABNACC– ABN∆CASH+

0.117 4.29 ***

ABNACC– ABN∆CASH-

-0.050 -1.92 *

N∆CASH – DIST_EQ

-0.155 -7.14 ***

-0.143 -6.61 ***

N∆CASH – DIST_D

-0.060 -2.96 ***

-0.053 -2.64 **

ABN∆CASH – DIST_EQ

-0.232 -7.97 ***

42

ABN∆CASH+ – DIST_EQ

-0.278 -8.28

***

ABN∆CASH- – DIST_EQ

-0.111 -3.66

***

ABN∆CASH – DIST_D

-0.137 -6.57 ***

ABN∆CASH+ – DIST_D

-0.188 -6.88

***

ABN∆CASH- – DIST_D

-0.021 -0.85

This table reports mean coefficients from annual estimations of the persistence of different components of net income. Specifically, we estimate the following

four models annually:

MODEL 1: INCOMEit+1 = β0 + β1INCOMEit + εit

MODEL 2: INCOMEit+1 = β0 + β1ACCRUALit + β2FCFit + εit

MODEL 3: INCOMEit+1 = β0 + β1ACCRUALit + β2∆CASHit + β3DIST_EQit + β4DIST_Dit + εit

MODEL 4: INCOMEit+1 = β0 + β1NACCit + β2ABNACCit + β3N∆CASHit + β4ABN∆CASHit + β5DIST_EQit + β6DIST_Dit + εit

MODEL 5: INCOMEit+1 = β0 + β1NACCit + β2ABNACCit + β3N∆CASHit + β4ABN∆CASH+

it + β5ABN∆CASH-it + β6DIST_EQit + β7DIST_Dit + εit

The sample includes 54,597 firm-year observations between 1972 and 2008. t values are based on the standard error of the mean coefficients across the years.

The adjusted R2 is the mean across the years.

***,

**, and

* indicate 0.01, 0.05 and 0.10 significance levels in a two-tailed test, respectively.

INCOME = income before extraordinary items (IB) scaled by average total assets (AT). ACCRUAL = total accruals, defined as the difference between change in

noncash assets (AT – CHE) and change in nondebt liabilities (LT – DLTT – DLC), scaled by average total assets (AT). ABNACC = performance-matched

abnormal accruals (Kothari et al. 2005). We match each firm-year with another from the same industry and year on return on assets. ABNACC is calculated as the

modified Jones model residual in year t minus the matched firm‟s modified Jones model residual in year t. NACC = normal accruals, calculated as the difference

between ACCRUAL and ABNACC. FCF = free cash flows, scaled by average total assets. Free cash flows are defined as the difference between INCOME and

ACCRUAL. CASH = the balance of cash and short-term investments (CHE), scaled by average total assets (AT). N∆CASH = normal level of change in cash

holdings, defined as the predicted value of the model in Table 2. ABN∆CASH =abnormal level of change in cash holdings, defined as the residual of the model in

Table 2. DIST_EQ = net capital distributions to equity holders [(-1) × (∆AT - ∆LT – IB)], scaled by average total assets (AT). ABN∆CASH+ = positive

ABN∆CASH values and 0 for negative ABN∆CASH values. ABN∆CASH- = negative ABN∆CASH values and 0 for positive ABN∆CASH values. DIST_D = net

capital distributions to debt holders [(-1) × (∆DLTT + ∆DLC)], scaled by average total assets (AT). ARETt+1 = annual buy-and-hold stock return calculated

starting four months after the fiscal year-end, adjusted by the CRSP size-decile portfolio return in which the firm belongs.

43

Table 6: Simultaneous nonlinear least squares estimation of the persistence parameters for the

accrual and cash flow components of net income and the corresponding implied persistence

parameters that are embedded in stock returns

Forecasting Coefficients Valuation Coefficients Test of Market

Efficiency βi = βi*

L-R statistic

Parameter

Coeff.

Std. Err

Parameter

Coeff.

Std. Err

Panel A: Aggregate earnings INCOMEit+1 = β0 + β1INCOMEit + εit

ARETt+1 = γ (INCOMEt+1 - β0* + β1

*INCOMEit + εit

β1 0.773 0.003 ***

β1*

0.697 0.015 ***

23.80 ***

γ 1.058 0.018 ***

Panel B: Decomposing earnings into accruals and free cash flows INCOMEit+1 = β0 + β1ACCRUALit + β2FCFit + εit

ARETt+1 = γ (INCOMEit+1 - β0* - β1

*ACCRUALit - β2

*FCFit )+ εit

β1 0.684 0.004 ***

β1*

0.750 0.018 ***

719.12 ***

β2 0.774 0.004 ***

β2*

0.614 0.016 ***

13.59 ***

γ 1.024 0.018 ***

Panel C: Decomposing free cash flows into change in cash, equity distributions and debt distributions INCOMEit+1 = β0 + β1ACCRUALit + β2∆CASHit + β3DIST_EQit + β4DIST_Dit + εit

ARETt+1 = γ (INCOMEit+1 - β0* - β1

*ACCRUALit - β2

*∆CASHit - β3

*DIST_EQit - β4

*DIST_Dit) + εit

β1 0.608 0.004 ***

β1*

0.686 0.020 ***

13.80 ***

β2 0.573 0.005 ***

β2*

0.557 0.022 ***

0.55

β3 0.789 0.004 ***

β3*

0.579 0.019 ***

118.14 ***

β4 0.671 0.006 ***

β4*

0.487 0.028 ***

40.24 ***

γ 0.956 0.017 ***

Panel D: Decomposing accruals and change in cash into normal and abnormal levels INCOMEit+1 = β0 + β1NACCit + β2ABNACCit + β3N∆CASHit + β4ABN∆CASHit + β5DIST_EQit + β6DIST_Dit + εit

ARETit+1 = γ (INCOMEit+1 - β0*- β1

*NACCit - β2

*ABNACCit - β3

*N∆CASHit - β4

*ABN∆CASHit - β5

*DIST_EQit

- β6*DIST_Dit) + εit

β1 0.554 0.005 ***

β1*

0.591 0.023 ***

2.50

β2 0.530 0.005 ***

β2*

0.584 0.022 ***

5.70 **

β3 0.542 0.009 ***

β3*

0.567 0.040 ***

0.39

β4 0.411 0.007 ***

β4*

0.344 0.032 ***

4.11 **

β5 0.745 0.004 ***

β5*

0.511 0.021 ***

127.24 ***

β6 0.613 0.007 ***

β6*

0.398 0.030 ***

48.72 ***

γ 0.897 0.017 ***

β1* = β2

* and

β1 = β2 71.58

***

β3* = β4

* and

β3 = β4 116.35

***

β1* = β3

* and

β1 = β3 1.47

β2* = β4

* and

β2 = β4

399.77

***

β1* = β1 and β2

* = β2

and β3

* = β3 and β4

* = β4 and β5

* = β5 and β6

* = β6

377.41

***

44

Panel E: Decomposing abnormal changes in cash holdings into positive and negative INCOMEit+1 = β0 + β1NACCit + β2ABNACCit + β3N∆CASHit + β4ABN∆CASH

+it + β5ABN∆CASH

-it

+β6DIST_EQit + β7DIST_Dit + εit

ARETit+1 = γ (INCOMEit+1 - β0*- β1

*NACCit - β2

*ABNACCit - β3

*N∆CASHit - β4

*ABN∆CASH

+it

- β5*ABN∆CASH

-it - β6

*DIST_EQit - β7

*DIST_Dit) + εit

β1 0.554 0.005 ***

β1*

0.591 0.023 ***

2.53

β2 0.529 0.005 ***

β2*

0.584 0.022 ***

5.83 **

β3 0.545 0.009 ***

β3*

0.568 0.040 ***

0.34

β4 0.310 0.010 ***

β4*

0.309 0.044 ***

0.00

β5 0.577 0.013 ***

β5*

0.402 0.058 ***

8.56 ***

β6 0.719 0.005 ***

β6*

0.502 0.022 ***

94.73 ***

β7 0.601 0.007 ***

β7*

0.394 0.030 ***

44.49 ***

γ 0.899 0.017 ***

β1* = β2

* and

β1 = β2

74.83

***

β3* = β4

* and

β3 = β4

272.17

***

β3* = β5

* and

β3 = β5

8.55

**

β1* = β3

* and

β1 = β3

0.91

β2* = β4

* and

β2 = β4

600.54

***

β2* = β5

* and

β2 = β5

23.96

***

β1* = β1 and β2

* = β2

and β3

* = β3 and β4

* = β4 and β5

* = β5 and β6

* = β6 and β7

* = β7

342.13

***

The sample contains 54,597 firm-year observations between 1972 and 2008. L-R statistic is a likelihood ratio test

based on the ratio of the sum of squared errors from the constrained and unconstrained specifications with respect to

the coefficients in the Mishkin [1983] test. ***

, **

, and * indicate 0.01, 0.05 and 0.10 significance levels in a two-

tailed test, respectively.

INCOME = income before extraordinary items (IB) scaled by average total assets (AT). ACCRUAL = total accruals,

defined as the difference between change in noncash assets (AT – CHE) and change in nondebt liabilities (LT –

DLTT – DLC), scaled by average total assets (AT). ABNACC = performance-matched abnormal accruals (Kothari et

al. 2005). We match each firm-year with another from the same industry and year on return on assets. ABNACC is

calculated as the modified Jones model residual in year t minus the matched firm‟s modified Jones model residual in

year t. NACC = normal accruals, calculated as the difference between ACCRUAL and ABNACC. FCF = free cash

flows, scaled by average total assets. Free cash flows are defined as the difference between INCOME and

ACCRUAL. CASH = the balance of cash and short-term investments (CHE), scaled by average total assets (AT).

N∆CASH = normal level of change in cash holdings, defined as the predicted value of the model in Table 2.

ABN∆CASH =abnormal level of change in cash holdings, defined as the residual of the model in Table 2.

ABN∆CASH+ = positive ABN∆CASH values and 0 for negative ABN∆CASH values. ABN∆CASH

- = negative

ABN∆CASH values and 0 for positive ABN∆CASH values. DIST_EQ = net capital distributions to equity holders [(-

1) × (∆AT - ∆LT – IB)], scaled by average total assets (AT). DIST_D = net capital distributions to debt holders [(-1)

× (∆DLTT + ∆DLC)], scaled by average total assets (AT). ARETt+1 = annual buy-and-hold stock return calculated

starting four months after the fiscal year-end, adjusted by the CRSP size-decile portfolio return in which the firm

belongs. ARETt+1 = annual buy-and-hold stock return calculated starting four months after the fiscal year-end,

adjusted by the CRSP size-decile portfolio return in which the firm belongs.

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Table 7 Descriptive statistics for portfolios formed on decile rankings of earnings components

Ranking Variable

Rank INCOMEt ACCRUALt NACCt ABNACCt FCFt ∆CASHt N∆CASHt ABN∆CASHt ABN∆CASHt+

ABN∆CASHt-

DIST_EQt DIST_Dt

Panel A: Mean value of ranking variable for each decile

1 (low) -0.273 -0.231 -0.257 -0.471 -

0.403

-0.160 -0.103 -0.130 0.003 -0.160 -0.319 -0.241

2 -0.059 -0.078 -0.094 -0.223 -

0.159

-0.049 -0.036 -0.062 0.009 -0.085 -0.065 -0.093

3 -0.002 -0.031 -0.036 -0.134 -

0.081

-0.021 -0.014 -0.040 0.016 -0.062 -0.020 -0.045

4 0.023 -0.002 0.003 -0.078 -

0.038

-0.007 -0.000 -0.027 0.025 -0.048 -0.006 -0.020

5 0.039 0.023 0.036 -0.033 -

0.008

-0.000 0.009 -0.016 0.035 -0.038 0.001 -0.004

6 0.053 0.048 0.069 0.006 0.018 0.006 0.018 -0.004 0.047 -0.030 0.007 0.002

7 0.058 0.076 0.107 0.048 0.042 0.017 0.027 0.009 0.064 -0.023 0.015 0.010

8 0.086 0.112 0.156 0.098 0.071 0.036 0.038 0.029 0.087 -0.017 0.027 0.027

9 0.112 0.171 0.233 0.173 0.110 0.073 0.056 0.063 0.126 -0.011 0.047 0.056

10

(high)

0.174 0.354 0.467 0.377 0.281 0.231 0.125 0.182 0.269 -0.004 0.125 0.155

Panel B: Mean value of future annual size-adjusted returns (ARET) for each decile

1 (low) -0.042 0.027 -0.011 0.017 -

0.087

-0.019 -0.008 -0.038 0.012 -0.045 -0.069 -0.062

2 -0.004 0.046 0.008 0.029 -

0.045

-0.015 0.011 -0.022 0.026 -0.028 -0.026 -0.015

3 0.014 0.027 0.006 0.028 -

0.002

0.013 0.012 -0.006 0.027 -0.016 0.018 -0.007

4 0.027 0.022 0.008 0.020 -

0.000

0.008 0.027 -0.005 0.024 -0.018 0.013 -0.000

5 0.024 0.025 0.007 0.021 0.006 -0.010 0.023 0.017 0.020 -0.011 0.022 -0.009

6 0.018 0.017 0.012 0.010 0.022 0.018 0.010 0.029 0.026 -0.005 0.016 0.016

7 0.007 -0.001 0.010 0.001 0.036 0.021 0.004 0.025 0.028 -0.004 0.026 0.023

8 0.000 -0.024 0.004 -0.012 0.031 0.021 0.013 0.021 0.001 0.018 0.011 0.026

9 -0.005 -0.022 0.001 -0.025 0.044 0.027 -0.007 0.027 0.013 0.019 0.012 0.043

10

(high)

-0.003 -0.080 -0.007 -0.054 0.031 -0.027 -0.049 -0.010 -0.036 0.048 0.013 0.024

HEDGE

(10 -1) 0.039 -0.107 0.004 -0.071 0.118 -0.008 -0.041 0.028 -0.048 0.093 0.082 0.086

t value 3.43 -9.50 0.40 -6.64 10.86 -0.71 -3.69 2.65 -3.06 6.65 8.08 7.96

This table examines the economic significance of stock returns associated with each component of earnings. We

rank the sample on each component of earnings in each year and assign equal number of observations to decile

portfolios based on the rankings. Panel A summarizes mean values of each component of earnings for each decile.

Panel B reports mean value of annual size-adjusted stock returns for each corresponding decile. We calculate the

return for a hedge portfolio consisting of a long position in the highest decile and a short position in the lowest

decile for each component of earnings.

INCOME = income before extraordinary items (IB) scaled by average total assets (AT). ACCRUAL = total

accruals, defined as the difference between change in noncash assets (AT – CHE) and change in nondebt liabilities

(LT – DLTT – DLC), scaled by average total assets (AT). ABNACC = performance-matched abnormal accruals

(Kothari et al. 2005). We match each firm-year with another from the same industry and year on return on assets.

ABNACC is calculated as the modified Jones model residual in year t minus the matched firm‟s modified Jones

46

model residual in year t. NACC = normal accruals, calculated as the difference between ACCRUAL and ABNACC.

FCF = free cash flows, scaled by average total assets. Free cash flows are defined as the difference between

INCOME and ACCRUAL. CASH = the balance of cash and short-term investments (CHE), scaled by average total

assets (AT). N∆CASH = normal level of change in cash holdings, defined as the predicted value of the model in

Table 2. ABN∆CASH =abnormal level of change in cash holdings, defined as the residual of the model in Table 2.

ABN∆CASH+ = positive ABN∆CASH values and 0 for negative ABN∆CASH values. ABN∆CASH

- = negative

ABN∆CASH values and 0 for positive ABN∆CASH values. DIST_EQ = net capital distributions to equity holders

[(-1) × (∆AT - ∆LT – IB)], scaled by average total assets (AT). DIST_D = net capital distributions to debt holders

[(-1) × (∆DLTT + ∆DLC)], scaled by average total assets (AT). ARETt+1 = annual buy-and-hold stock return

calculated starting four months after the fiscal year-end, adjusted by the CRSP size-decile portfolio return in which

the firm belongs. ARETt+1 = annual buy-and-hold stock return calculated starting four months after the fiscal year-

end, adjusted by the CRSP size-decile portfolio return in which the firm belongs.