persistence of cash flow components inot future cash flows - cheng & hollie

7/31/2019 Persistence of Cash Flow Components Inot Future Cash Flows - Cheng & Hollie

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The Persistence of Cash Flow Components into Future Cash Flows

C. S. Agnes Cheng*

Securities Exchange Commission, Washington, DCUniversity of Houston, Houston, Texas 77204-4852

[email protected]

Dana HollieUniversity of Houston

C.T. Bauer College of BusinessDepartment of Accountancy & Taxation

334 Melcher Hall, Suite 390-FHouston, Texas 77204-4852

[email protected]


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Abstract

We examine the persistence of cash flow components in predicting future cash flows. In thisstudy, we evaluate six cash flow components (which parallels the direct method of the cash flowstatement): cash flows related to sales, cost of goods sold, operating expenses, interest, taxes, andother. Consistent with our predictions, we find that the cash flow components from various

operating activities persist differentially. We find that cash related to sales, cost of goods sold,operating expenses and interest persists a great deal into future cash flows; cash related to otherhas lower persistence; and cash related to taxes has no persistence. We then incorporate accrualcomponents into our persistence regression model and find that the persistence of cash flowcomponents are generally higher than those of accruals; however, accrual components doenhance model performance. Our findings are consistent with the AICPAs and financial analystsrationale for their recommendation that the financial effects of a companys core and non-core cashflows should be distinguished. We also corroborate prior research that both direct and indirect

methods of reporting cash flows can be useful for assessing future cash flows. Our findings arerelevant to financial statement users and regulators who are interested in better predicting future cash

flows and researchers using cash flow prediction models to measure financial reporting quality.

JEL Classification:

Keywords: cash flows; accruals; cash flow prediction; direct method, indirect method


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I. Introduction

The purpose of this paper is to document the extent of the persistence of current period cash

flow components into future cash flows. It is well known that predicting cash flows is important for

liquidity and solvency analysis1 and for firm valuation especially when earnings are of a lower

quality.2Moreover, the persistence of cash flows into the future is an essential attribute for

prediction. The usefulness of cash flow information has been documented by its greater persistence

over accruals; however, the differential persistence of components of aggregated cash flows into

future cash flows has not been thoroughly investigated. The objective of this study is two-fold. First,

we address the issue of whether cash flow components, as defined, persist differentially relating to

future cash flows. Second, we examine whether the components of cash flows have the potential to

improve prediction model performance. Our findings shed additional light on the importance of cash

flow components in predicting future cash flows and have implications for financial statement users

and accounting policy making on the reporting of cash flows.

The Special Committee on Financial Reporting formed by the American Institute of Certified

Public Accountants (AICPA) stated that financial statements serve users as a model of a companys

business and provide considerable insight into the relations between transactions and events and the

financial impact of those transactions and events on the company a key goal of financial analysis

(AICPA). In general, the closer the display in financial statements maps transactions and events, the

more insight it provides. Hence, the AICPA recommends that firms should distinguish between the


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incidental activities) cash flows, thereby, presenting the best possible information in which to analyze

trends in a firm without the potential distortional effects of non-core activities. Not surprisingly,

financial analysts are increasingly demanding detailed information on cash flows.3,4 The AICPA

defines core activities as usual or recurring operations and recurring non-operating gains and

losses.5

Core activities should generate cash flows that persist higher into the future than cash

flows from non-core activities.

Krishnan and Largay (2000), using a small sample, show that cash flow components reported

from the direct method and estimated direct method6 perform better than accrual components

reported from the indirect method in predicting future cash flows. Barth, Cram and Nelson (2001)

(hereafter referred to as BCN) show that accrual components predict future cash flows incremental to

aggregated operating cash flows. Taken together, we propose that cash flow components from

various operating activities should persist differentially into future cash flows, and that the inclusion

of cash flow components into the cash flow prediction model should enhance prediction model

performance beyond that of accrual components.7

It is plausible to assume different levels of

persistence for cash flow components derived from the current reporting system; however, the

degrees of the differences call for empirical assessment. We estimate cash flow components based

3 For example, Kyle Loughlin, an analyst at Standard & Poor and head of its chemical industry team states: I would always

favor more information [over] less. Transparency and clear information about the cash flow generated from core businessactivities is part and parcel to good credit analysis. So, if the details are made available in a timely manner, it is an importantconsideration, especially in this environment. (Chang, 2002)4 In a workshop offered by debt-rating agency, the presenters stressed the importance of disaggregating the cash flows based ontheir operational functions and their persistence in predicting future cash flows, the most crucial information for credit analysis.They specially mentioned the importance of taxes and the difficulty of separating cash flow paid for taxes into operating andnonoperating activities. They concur that more detailed and more transparent cash flow information will be helpful for theiranalyses


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on the current reporting system (indirect method) for a large sample of firms. We examine whether

these components persist differentially into the future, and assess whether the cash flow prediction

model can be improved by considering these cash flow components. Our empirical findings

enhance our understanding of the behavior of cash flow components and support the demand for

detailed cash flow information in accordance with the direct method.

Following the typical order of revenue and expenses reported in the multiple income

statement, we disaggregate cash flows into six major componentscash flows from sales, cost of

goods sold, operating expenses, interest, taxes, and other.8 We examine these components to

determine whether they persist differentially into the future. We then assess whether the explanatory

power of the cash flow prediction model is enhanced by this disaggregation of cash flows. If

activities affecting these components do not change a great deal over time, then we should see high

persistence of current cash flow to future cash flows and we should not see that decomposing cash

flows into different components improves cash flow prediction. We predict that cash flow activities

affecting sales, cost of goods sold and operating expenses are core operating activities and should be

highly persistent. Financing activities related to interest payments involve long-term financing and

should be stable across time. Tax payment activities are affected by operating, non-operating and

financing transactions. They are also affected by time-dependent tax strategies. Therefore; we do not

expect tax payments to be highly persistent. Activities that affect our other category include one-

time operating and non-operating activities, and thus should not be highly persistent.


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Our methodology is similar to Barth, et al. (2001); however, we focus on both the

coefficients and the explanatory power in regressing next-period cash flows on this years cash flow

components. We conduct pair-wise comparisons of the coefficients on the components to assess if

there exist significant differences in the persistence of the components. We also evaluate if the

prediction model using cash flow components perform better than the model using only aggregate

cash flows. As expected, we find that the six cash flow components persist differently into future

cash flows. We find that cash flows from sales, cost of goods sold, operating expenses, and interest

have similar persistence and persist more than cash flows for other expenses. We find cash related to

taxes has no persistence. We also find that disaggregating cash flows into these six components

significantly improves performance of the prediction model.

BCN find that accrual components are important in predicting future cash flows and that they

are positively associated with future cash flows.9 The reasons that these accruals are related to future

cash flows are mostly due to the realization of previously recognized accruals10 such as the purchase

of more inventories to prepare for future sales or an increase in accounts receivables to be collected

in the future. The persistence of these accrual components into future cash flows depends mostly on

how they revert to cash in the future. On the other hand, the persistence of cash flow components

also depends on the persistence of the cash flow generating abilities of current operations. Since the

persistence of accruals and cash flows is affected by different activities (or affected differently by

the same activities), we expect accrual components and cash flow components complement each

other in predicting future cash flows. As expected, we find that cash flow components and accrual

components explain future cash flows beyond the other. Our empirical findings are consistent with


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companys core and non-core cash flows should be distinguished in the financial statements.

Furthermore, we concur with prior research that both the direct and indirect methods of reporting

cash flows can be useful for assessing future cash flows (e.g., Krishnan & Largay,2000). Our

findings are relevant to financial statement users who are interested in better predicting a firms

future cash flows and therefore are relevant to accounting policy makers. Moreover, our findings are

specifically relevant to academic research using cash flow prediction models to measure financial

reporting quality.11

The remainder of this paper is organized as follows. Section II provides a review of the

related literature. Section III describes the research design. Section IV presents the sample selection

criteria and discusses our empirical findings. Section V summarizes and concludes the paper.

II. Relevant Prior Literature

Statement of Financial Accounting concepts (SFAC) No. 1 issued by FASB (1978) describes

one of the objective of financial reporting is to provide information for users to better predict future

cash flows. SFAC No. 5 further claims that accrual earnings provide a better basis for assessing

firms future cash flows than past cash flow information alone (FASB, 1984, para. 24). Greenberg et

al. (1986) find evidence that current earnings are a better predictor of future cash flows than are

current period cash flows. In contrast, Finger (1994) find that current period cash flows have more

predictive ability than current period earnings in predicting next-period cash flows. However, Lorek

and Willinger (1996) show that cash flow prediction is enhanced by consideration of both earnings

and accrual accounting data. Dechow, Kothari and Watts (1998) develop a model which implies

earnings better predict future operating cash flows than current operating cash flows and their


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For earnings to enhance cash flow predictability beyond cash flows, behavior of accruals

should provide additional insights to the enhancement in prediction. Barth, Cram and Nelson (2001)

confirm that accruals improve cash flow prediction beyond aggregate cash flows. They extend the

Dechow et al. (1998) model by decomposing accruals into different components and show that

accrual components improve prediction of future cash flows over the aggregate accruals. Two

studies that have investigated cash flow components are Krishnan and Largay (2000) and Livnat and

Zarowin (1990). Using data prior to Financial Accounting Standard (FAS) No. 95, which requires

firms to report cash flows from(for) various activities, Livnat and Zarowin (1990) evaluate the value

relevance of cash flow components based on estimates derived from balance sheet and income

statement. For the components of operating cash flows, they provide estimates including cash

collected from customers, cash paid for cost of goods and operating expenses, taxes paid, interest

paid and other operating cash flows.12 They find that the value relevance of operating cash flow

components are different. Krishnan and Largay (2000), using a small sample of firms that provide

direct method cash flows, find that direct method cash flow information (i.e. components of

operating cash flows) perform better than indirect method cash flow information (i.e. earnings plus

accruals) in predicting future cash flows. They also analyze a larger sample by using estimated cash

collected from customers and estimated cash paid to suppliers and employees (similar to Livnat and

Zarowin) and they find cash flow components enhance cash flow prediction beyond aggregate cash

flows and accrual components.

To sum, Barth, et al. (2001) model the differential persistence of accrual components in

predicting future cash flows and document the superior model performance when current aggregate


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document the differential value-relevance of the cash flow components. Krishnan and Largay III

report cash flow components enhance cash flow prediction beyond accruals components. Taken

together, their results imply cash flow components persist differently in predicting future cash

flows.13 Thus, we extend prior research by directly examining the persistence of cash flow

components and their potential enhancement of cash flow prediction.

III. Research Design

Persistence of cash flow components depends on the persistence of the activities that generate

them. If activities affecting these components do not change over time, then we should observe

similar persistence across all components. Usually, firms activities are classified into three

categories: operating, financing, and investing. For firms to sustain their long-term operations and

growth, persistent cash flows from operations are the key attribute and many research studies focus

on improving forecasts of future cash flows from operations. Future cash flows from operations can

be improved by financing and investing activities. The major goal of our paper is to document the

average persistence of operating cash flow components with an objective in mind as to the usefulness

of detailed operating cash flows in assisting cash flow forecasts. Firms choose to invest more or

finance investing more can certainly affect the persistence of current cash flows. We do not

investigate the direct effects from financing and investing activities;14 we let our results reflect their

indirect effects on the persistence coefficients in our model.


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To understand how the components of cash flows persist into future cash flows, we focus on

comparing equations (1) and (2) as described below.

15

Equation (1) constrains the coefficients on the

cash flow components so they are equal. Equation (2) relaxes the constraint and disaggregates total

cash flows into six components to determine whether cash flow components reflect different

information related to future cash flows. A comparison of explanatory power between equations (1)

and (2) will determine whether the disaggregation of cash flows improves predictive ability relative

to aggregate cash flows (e.g. Barth, Cram and Nelson). A comparison of coefficients among cash

flow components in equation (2) will indicate if one component has higher impact on the dependent

variable than that from other components (e.g. Livnat and Zarowin).

CFOt+1 = + CFOt + t (1)

CFOt+1 = + C_SALESt + C_COGSt + C_OEt + C_INTt + C_TAXt + C_OTHERt + t (2)

Also written as: CFOt+1 = + CFO t + t

The variables are defined as follows:16

CFO = net cash flow from operating activities less the cash flows from extraordinary items anddiscontinued operations reported on the statement of cash flows (#308 - #124);

C_SALES = cash flows from sales, calculated as sales minus changes in accounts receivabletrade(#12-#151 or #12+#302)17

C_COGS = cash flows from cost of goods sold, calculated as cost of goods sold (exclude

depreciation) minus change in inventory plus change in accounts payable (#41-#3+#70 or

#41+#303+#70)18;C_OE = cash flow from operating and administrative expenses, calculated as operating expenses

(OE=#12-#41-#13) minus change in net operating working capital (NOWC= [#4#162(#5

15 To keep our model expression simple, we use indicating the coefficient and the error term for every variable and everymodel, respectively. Also, consistent with prior research, we use realized future cash flows as a proxy for future cash flows.(McNichols and Wilson, 1988; Penman and Sougiannis, 1998; Aboody et al., 1999; Barth, et al., 2001).16 Due to data availability and companies reporting patterns (most companies report under the indirect method); the cash flowcomponents are derived from the income statement the comparative balance sheets and cash flow statement (except taxes and


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#34)]) excluding changes in accounts receivabletrade, inventory, accounts payable, taxpayable and interest payable.19

C_INT = cash flow related to interest payment (#315):

20

C_TAX = cash flow related to tax payment (#317):21

C_OTHER = cash flows related to other revenue/expenses items including special and extraordinaryitems, calculated as cash flow from operations (#308) minus all other cash flow components (i.e.,cash flows related to sales, COGS, operating expenses, interest and taxes).

The six cash flow components are defined as cash flows from sales (C_SALES), cost of

goods sold (C_COGS), operating and administrative expenses (C_OE), interest (C_INT), taxes

(C_TAX), and other expenses (C_OTHER). All of the variables are scaled by the same scalar. We

have used total number of shares, beginning total assets, average total assets as the scalar. Our

results are qualitatively similar; our reported results are based on average total assets.

Our estimation method builds upon Livnat and Zarowin with several differences. The major

difference is that we use post-FAS 95 data. SFAS No. 95 allows firms to use either direct or indirect

method; however, if firms use direct method, a reconciliation between direct and indirect method is

also required. Most firms choose to use the indirect method and Compustat provides data items

based only on the indirect method. Therefore, we have to estimate cash flows components but we use

the information provided in the cash flow statement as much as possible.22 Livnat and Zarowin

estimate cash flow collected from customers by using sales reported on the income statement and add

changes in accounts receivables as reported in balance sheet. We follow their method but also use

reported changes in accounts receivables from the cash flow statement as an alternative

19 Operating expenses are calculated as sales (#12) minus cost of goods sold (#41) minus operating income before depreciation(#13).The net operating working capital (NOWC), is calculated as current assets (#4) minus cash (#162) minus (current liabilities(#5) minus debt in current liabilities (#34)). Changes in accounts receivable (AR) can be derived directly from the cash flow

statement (-#302) or indirectly from the comparative balance sheet (#151). Similarly, changes in inventory (Inv) and taxes


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measurement. Livnat and Zarowin estimate cash paid to suppliers, employees, etc., which we

separate into cash related to cost of goods and operating expenses. We assume accounts payable and

inventory accounts are related to cost of goods sold and accrued liability and other current accounts

are related to operating expenses. Our separation is not perfect and it may bias downward the

persistence coefficients for these two categories. Since we are interested in contrasting cash flows

related to sales to cash flows related to operating expenses, cash flows related to cost of goods sold

should behave more like cash flows related to sales than cash flows related to operating expenses. If

we cannot find such an result, it may be due to our assumption of these accounts, which may be a

limitation of the data. However, we actually find the persistence of cash flows related to cost of

goods sold is more similar to cash flows related to sales than cash flows related to operating expense.

Hence, we choose to maintain the separation between cash paid for cost of goods sold and for

operating expenses.

Livnat and Zarowin has to estimate cash paid for interest and for taxes, we use the reported

cash payment for these two purposes from the cash flow statement whenever we can. For other

operating cash flows, Livnat and Zarowin use income statement data only. We use reported cash

flows from operation subtracting previously estimated cash flows related to other purposes. Hribar

and Collins (2002) report significant measurement error using income statement and balance sheet

articulation than using the reported cash flow number. In the similar vein, we believe using the post-

SFAS No. 95 data, our measures should improve over the pre-SFAS No. 95 measures derived in

previous studies.

If we define core components as those components possessing high persistence, then some of


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more related to future cash flows than are the other components and should therefore persist more

than the other components. Interest should contribute less to predicting future operating cash flows

since interest expense is related to financing activities rather than operating activities and financing

activities may not have similar persistence to operating activities.23 Hence, we predict that cash

flows related to interest should persist less than SC&O. Taxes are related to all aspects of business

including both operating and non-operating activities. However, unlike other cash flow components,

which are affected by managers operating, financing, and investment activities, taxes are determined

mostly by tax policies and firm tax strategies, which can be quite different from the firms other

ongoing business activities. Therefore, we predict that cash flows related to taxes should persist less

than SC&O. Other expenses may consist of one-time charges like restructuring and special charges

that could have differing and unpredictable effects on cash flow predictability and are therefore

deemed to have low persistence.

The focus of our paper is to contrast the performance of equation (1) and (2) and contrast the

persistence coefficients among the cash flow components. However, we are also interested in

evaluating if the components of cash flows and accruals complement each other in predicting future

cash flows. We first replicate Barth et al. (2001) to ensure that our results with respect to cash flow

prediction are not data or time specific. We compute cash flows and accruals consistent with BCN in

order to make our comparisons.24 Equation (3), i.e. the BCN model, is written as follows:

CFOt+1 = 0 + 1CFO t + 2ARt + 3AP + 4INVt + 5DEPRt + 6OTHERt + 7AMORTt + t (3)

Also written as CFOt+1 = + CFO t + ACCt + t

The variables are defined as follows:


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828.26 We find that the means of our EARN, CFO, and ACC are slightly lower than BCNs (they

report 0.08 for each variable).

27

However, our standard deviations for EARN and ACC are slightly

higher, while our standard deviation for CFO is similar to theirs. It should be noted that all our

measures of cash flow are based on inflows (positive) and outflows (negative). Hence, we have

positive means and medians for C_SALES and negative means and medians for C_COGS, C_OE,

C_INT, and C_TAX, since they are all expenses. C_OTHER is positive, which suggests more other

sources of revenues than expenses.

{Insert Table 2 about here}

Table 2 reports the Pearson and Spearman correlations for all regression variables. The

correlation coefficient between C_Sales and C_COGS is particularly high (-0.939 and -0.907).28 The

correlation coefficient between EARN and CFO is 0.704 and 0.541, a correlation typical for these

two variables that does not generally cause problems when they are in the same model.


Regression summary statistics from equation (1) are presented in Panel A of Table 3. We use

a mean analysis of regression to test the significance of the coefficients.29Equation (1) serves as a

benchmark for assessing the relative predictive ability of aggregate cash flows to cash flow

components. Consistent with prior research, we find that aggregate cash flows in equation (1) are

26 BCN examines the 1987 to 1996 time period. We delete the observations for 1987 due to its lack of previous years data for

calculations.27 BCN report their statistics using only two digits: therefore, the difference between our numbers and theirs may be affected byrounding errors. For example, we report ACC as 0.047 and they report 0.04. It is likely that our number has a larger magnitudethan theirs since rounding up our number will lead to 0.05. When we check the sum of accruals of the means as reported (i.e.ACC = AR + INV AP - DEPR AMORT + OTHER: 0.019+0.012+-0.013+-0.042+-0.004+-0.018+ (-0.046)), we get 0.046 (a rounding error of 0.001); however, when we check the sum of accruals of BCN, we get ACC=0.01+0.01-0.01-0.05-0.01+(-0.01) =-0.06 while they report 0.04 (a rounding error of 0.02)28 This may cause multicollinearity problems in the regression The best way to deal with multicollinearity is to enlarge the


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significantly positive in the prediction equation. CFO explains 28.69% of the variation in next-period

cash flows.

30

We find that the coefficient for CFO has an average of .529 with a t-statistic of 27.34.

This finding suggests that more than 50% of the current years cash flows will persist into next years

cash flows.

Panel B of Table 3 presents results from estimating equation (2) in which cash flows are

disaggregated. Consistent with our predictions, the six components of cash flows are significant in

predicting future cash flows. The adjusted R2 increases from 28.69% for equation (1) to 31.97% for

equation (2), an 11% increase in explaining its variation.31 We also find that all six cash flow

components (C_SALES, C_COGS, C_OE, C_INT, and C_OTHER) are positive and significant,

except C_TAX which is significantly negative.

The coefficients for C_SALES and C_COGS are nearly .5 with a t-statistic around 26. The

coefficient for C_OE has a slightly higher average (.501) but a lower t-statistic (25.06). This finding

suggests that the persistence of C_OE has greater variability over time than that of C_SALES and

C_COGS. This is consistent with the stickiness of operating expense suggested in Anderson, Banker

and Janakiraman (2003) that many types of general administrative costs are sticky and do not vary

directly with revenue. Hence, its persistence may be higher than cost of goods sold but at the same

time has a higher variability. The coefficients on C_INT and C_OTHER have a value of .468 and

.412, respectively; C_INT has a t-statistic of 8, while C_OTHER has a t-statistic of 17. This outcome

indicates C_Other and C_INT have greater variability across years than for SC&O, especially for

C_INT. This finding is consistent with C_OTHER being composed of various other

expenses/revenues that vary more from year to year than the core expenses. C INT has higher


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flows. In deciding on the reporting requirement for the statement of cash flows (Statement of

Financial Accounting Standards No. 95), the FASB chose to include cash flows related to interest in

the operating section, while the AICPA suggests that interest be classified as a non-operating cash

flow item. Our results showing a high persistence for C_INT suggest that the categorization of cash

related to interest may not be an important issue. On the other hand, the coefficient and t-statistic on

TAX are -.187 and -2.99, respectively. This result implies that C_TAX does not persist into the next-

period cash flows. Two factors may affect the persistence of cash flow from taxes. First, the

persistence of taxes depends on the sources of income on which taxes are levied. Since the cash flow

statement does not provide taxes paid for operating and non-operating activities separately, it is

difficult to estimate, based on income statement and balance sheet data, how taxes should be

distributed among these activities. Second, taxes are affected by a firms tax strategy. Firms like to

defer taxes as much as possible, and the amount of taxes a company defers depends on the timing of

its real transactions. The fact that the coefficient is negative suggests that firms paying high taxes this

year tend to pay lower taxes next year.

To determine whether the cash flow components persist differentially statistically, we use a

pair-wise test of the differences in coefficients for equation 2, which are reported in Panel C of Table

3. Each row reports the differences between the corresponding values designated in the matrix. A

negative value for the mean pair-wise comparison suggests that the cash flow components designated

in the column heading are less persistent than the cash flow components labeled in the stub. For

example, a comparison of C_SALES and C_TAX reveals a mean difference of -.679, a t-statistic of -

11.62, and a p-value of


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C_SALES, C_COGS, and C_OE (C_INT) reveal positive (negative) but insignificant coefficients.

The comparisons among C_TAX and C_OTHER reveal significant negative values. Our findings

show that C_TAX is less persistent than C_OTHER and the difference between C_INT and

C_OTHER is insignificant. We find that SC&O and interest persist similarly and longer than taxes

and other expenses. The persistence of interest is also similar, although slightly higher, to that of

other expenses. Based on these pair-wise comparison tests, we conclude that cash flows from sales,

cost of goods sold, operating expenses, and interest have high persistence while cash flows from

taxes and other expenses have low persistence. If the consensus is that core cash flows should include

those components that persist, then it is likely that cash flow from sales, cost of goods sold, operating

expenses, and interest should be classified as core components of cash flows.

Regression summary statistics for equation (3) are presented in Panel A of Table 4. Equation

(3) replicates BCN and serves as a robustness check. It is also used to assess the potential predictive

ability of aggregate cash flows and accrual components. Our findings reveal an adjusted R2 of

34.27%, which is consistent with BCN (35%). We find that the coefficient for CFO has an average of

.592 (0.59 in BCN) with a t-statistic of 28.16. This finding suggests that almost 60% of current

period cash flows will persist to next years cash flows once the effects of the accrual components are

controlled for. The coefficients of the accrual components reported in Panel A have the same signs as

in BCN. The magnitudes are also similar except forINV and OTHER. We (BCN) have coefficients

of 0.245 (0.35) and 0.44 (0.15) forINV and OTHER, which implies a smaller inventory effect and

a greater effect of OTHER in our sample. AMORT is not significant in our sample.



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those reported for equation (2) in Table 3, which does not incorporate accrual components in the

model. When we include accrual components, the coefficients become larger. The Fama-Macbeth t-

statistics also increases for all variables except the coefficient on C_TAX, which becomes

insignificant.32

A comparison of the coefficients for the accrual components in equation (3) and (4) reveals

that the magnitudes of the coefficients ofAR, INV, AP, and OTHER decrease from 0.428,

0.245, 0.503 and 0.144 to 0.396, 0.190, 0.456 and 0.102, with OTHER having the greatest decrease,

approximately 30%. The coefficients of DEPR and AMORT increase from 0.472 and 0.081 to 0.507

and 0.108. This finding implies that, in contrast to long-term accruals, the significance of short-term

accruals and OTHER in equation 3 is partly due to their correlations to cash flows.

Panel C of Table 4 provides a pair-wise comparison test to confirm our conclusion (from

Panel C of Table 3) about the relative persistence of cash flow components once the effect of accrual

components is controlled for. Our findings are similar to those in Panel C of Table 3. We also

contrast the persistence coefficients between the cash flow and accrual components. The persistence

coefficients of cash flow components range from 0.481 to 0.524 (exclude taxes paid), they are

significantly higher than the persistence coefficients of the working capital accrual components (the

magnitudes of the coefficients range from 0.102 to 0.456).


Table 5 presents a pair-wise comparison test using each models adjusted R2.33 The adjusted

R2 increases from 28.69% for equation (1) to 31.97% for equation (2), 34.27% for equation (3), and

36.33% for equation (4). The improvement from equation to equation is around 23% (2.86, 2.81,


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and 2.06, respectively), with corresponding t-statistics of 6.05, 4.93, and 7.21, respectively. The

corresponding improvements are significant and positive. For example, the adjusted R2 from equation

(3) (i.e. the BCN model) to equation (4) increases 2.06%, which is positive and statistically

significant. This result indicates that disaggregating cash flows improves performance of the cash

flow prediction model significantly. Thus, we conclude that disaggregating cash flows into cash flow

components has the potential to enhance cash flow prediction whether or not the effect of accrual

components is controlled for.34

V. Summary and Conclusions

Consistent with our expectations, this study provides evidence that (a) cash flow components

reflect different information related to future cash flows and (b) the disaggregation of cash flow

components has the potential to enhance prediction model performance. Specifically, we find that

cash flows from sales, cost of goods sold, operating expenses, and interest have similar persistence in

predicting future cash flows, cash paid for other expense has low persistence and cash paid for taxes

has no persistence. We also find that both the cash flow components and accrual components

complement each other in explaining future cash flows.

Our findings will be useful from several perspectives. Financial analysts have been calling

for more detailed cash flow information. Our empirical results provide a benchmark for the

importance of the details of cash flows in predicting future cash flows. This findings echo the

demand from the financial analysts calling for more detained information.

Our findings also provide a basis for policy makers in evaluating the reporting of line items

for operating cash flows. Our other category is based on estimation, it includes special items and


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Keeping this in mind, the significant lower persistence of this category shall provide evidence for

regulators and for managers to consider reporting more detailed one-time items in their cash flow

statement. Moreover, cash paid to taxes has no persistence. In the income statement, tax expenses

are required to be allocated between the line items above and below income from continuing

operations. Policy makers may consider applying the same rule to cash paid for taxes.35 Also, our

finding on the importance of both the cash flow and accrual components in explaining future cash

flows imply that both direct and indirect method of reporting cash flows provide useful information.

Our empirical findings have implications to recent accounting research studies investigating

the quality of accounting information. For example, Dechow and Dechev (2002) report the

importance linking accruals and operating cash flows to identify earnings quality. In their model,

future operating cash flows is an important determinant and is needed to assess earnings quality. In

assessing the quality of financial reporting, Cohen (2004) evaluates the ability of accounting

information in predicting future cash flows. Our results can shed additional light and provide more

research avenues in this line of research.

Acknowledgment

The authors gratefully acknowledge the comments of Mary Geddie, K. (Shiva) Sivaramakrishnan,Scott Whisenant, and workshop participants at the University of Houston, the 2004 AmericanAccounting Association annual meeting, Douglas Hanna, reviewer for the 2005 JAAF conferenceand 2005 JAAF Conference participants.


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Table 1

Descriptive Statistics

STATS CFO C_SALES C_COGS C_OE C_INT C_TAX C_OTHER

Mean 0.054 1.210 -0.841 -0.297 -0.018 -0.020 0.020Std 0.137 0.808 0.700 0.283 0.019 0.024 0.120Median 0.071 1.062 -0.682 -0.243 -0.013 -0.012 0.008N 20,828

STATS EARN ACC AR INV AP DEPR AMORT OTHERMean 0.007 -0.047 0.019 0.012 0.013 0.042 0.004 -0.018Std 0.167 0.120 0.060 0.047 0.051 0.031 0.009 0.083Median 0.039 -0.039 0.011 0.002 0.009 0.036 0.000 -0.006

This table presents descriptive statistics for each of the regression variables and is based on pooled data.The regression variables are defined as follows (Compustat data items in parentheses):CFO = net cash flow from operating activities (#308)C_SALES = cash flows from sales are calculated as sales (#12) minus change in accounts receivable trade (#151);

C_COGS = cash flow from cost of goods sold is calculated as cost of goods sold (#41) minus [change in inventory (#3) minus change in accounts payable (#70)];C_OE = cash flow from operating and administrative expenses are calculated as operating expenses

36minus change in Net Operating Working Capital excluding changes

in accounts receivable-trade, inventory, tax payable and interest payable;C_INT = cash flow related to interest payment (#315);C_TAX = cash flow related to tax payments (#317);C_OTHER = cash flows related to other revenue/expenses items including special and extraordinary items are calculated as cash flow from operations (#308) minus all

other cash flow components (i.e., cash flows related to sales, COGS, operating expenses, interest and taxes).EARN = income before extraordinary items and discontinued operation (#18);AR = change in accounts receivable per the statement of cash flows (#302);INV = change in inventory per the statement of cash flows (#303);AP = change in accounts payable and accrued liabilities per the statement of cash flows (#304);DEPR = depreciation expense (#103);AMORT = amortization (#65);OTHER = net of all other accruals, calculated as EARN (CF + AR + INV - AP DEPR AMORT).

ACC=EARN CFO or alternatively ACC = AR + INV AP - DEPR AMORT + OTHER

36

Operating expenses are calculated as sales (#12) minus cost of goods sold (#41) minus operating income before depreciation (#13).


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Table 2

Pearson (Spearman) above (below) the diagonal Correlation Table

EARN ACC CFO C_SALES C_COGS C_OE C_INT C_TAX C_OTHER AR INV AP DEPR AMORT OTHER

EARN 0.588 0.704 0.071 -0.023 0.232 0.084 -0.344 -0.031 0.124 0.121 -0.065 -0.203 -0.133 0.565

ACC 0.334 -0.161

-0.015^

-0.035 0.085 0.099 -0.174 -0.064 0.415 0.420 -0.039 -0.414 -0.137 0.720

CFO 0.541 -0.483 0.100

0.002!

0.208 0.016^ -0.267 0.019 -0.213 -0.220 -0.045 0.115 -0.042 0.057

C_SALES 0.147 0.017 0.111 -0.939

-0.405 -0.120 -0.168 -0.137 -0.043 0.014^ 0.006! 0.058 -0.012! 0.026

C_COGS -0.069 -0.079 0.007! -0.907 0.150

0.139 0.095 0.096 0.007! -0.054 -0.010! -0.014 0.040 -0.033

C_OE 0.007! -0.053 0.101 -0.458 0.186 -0.083

0.051 -0.268 -0.022 -0.010 -0.052 -0.026 -0.067 0.097

C_INT 0.222 0.108 0.056 -0.147 0.213 -0.135 -0.220

0.096 0.115 0.097 0.107 -0.175 -0.103 -0.004!C_TAX -0.581 -0.188 -0.333 -0.235 0.163 0.070 -0.151 -0.006

-0.048 -0.107 -0.021 0.118 0.044 -0.122

C_OE 0.056 -0.046 0.082 -0.142 0.117 -0.179 0.104 -0.052 0.047 0.001! 0.079 -0.108 -0.033 -0.122

AR 0.193 0.437 -0.232 -0.036 0.026 -0.076 0.146 -0.046 0.040 0.229

0.466 -0.157 -0.033 -0.028

INV 0.196 0.459 -0.230 0.069 -0.106 -0.070 0.075 -0.127 0.008! 0.222 0.347

-0.140 -0.058 0.028

AP 0.076 0.006! 0.009! 0.017^

-0.004! -0.082 0.134 -0.016^

0.075 0.448 0.293 -0.077

-0.047 -0.011

DEPR -0.114 -0.456 0.280 0.136 -0.102 0.049 -0.210 0.078 -0.128 -0.196 -0.144 -0.117 -0.074

-0.093

AMORT -0.066 -0.029 -0.029 0.036 -0.011! -0.070 -0.126 -0.027 -0.068 -0.003! -0.040 -0.075 -0.138 -0.093

OTHER 0.171 0.448 -0.168 0.059 -0.097 0.022 -0.027 -0.157 -0.120 -0.077 0.037 -0.006! -0.040 -0.025*All variables are defined in Table 1. Significant at 0.05, ! Insignificant at 0.01, All others are significant.


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Table 3Summary Statistics from Regressions of Future Cash Flow on Current Cash Flow and Components of Cash Flow from Operations

Eq. (1): CFOt+1 = + CFOt + t+1

Eq. (2): CFOt+1 = t+1 + C_SALESt + C_COGSt + C_OEt + C_INTt + C_TAXt + C_OTHERt + t+1

Panel A: Regression Results for Equation (1)

Adj. R2 Intercept CFOtYearly Avg. 28.69% .039 .529

t-statistic 13.32 27.34

p-value


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Table 3 (Contd)Summary Statistics from Regressions of Future Cash Flow on Current Cash Flow and Components of Cash Flow from Operations

Panel C: Pair-wise Test of Differences in the Coefficients for Equation (2)

Variable C_SALES C_COGS C_OE C_INT C_TAX C_OTHER

0.004 0.008 -0.025 -0.679 -0.081

1.85 1.36 -0.30 -11.62 -7.85

C_SALES 0.0862

0.1969 0.7693


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Table 4 (Contd)Summary Statistics from Regressions of Future Cash Flow on Current Cash Flow and Accrual Components

and Regressions of Future Cash Flow on Current Cash Flow and Accrual Components

Panel C: Pair-wise Test of Differences in the Coefficients for Equation (4)

Variable C_SALES C_COGS C_OE C_INT C_TAX C_OTHER

0.003 0.006 0.077 -0.597 -0.066

1.54 0.94 1.06 -11.58 -5.91

C_SALES0.1448

0.3630 0.3051


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Table 5

Pair-wise Test of Differences in Adjusted R-squares for Equations 1, 2, 3 and 4

Equation 2 3 4

1 2.86%

6.05

persistence of cash flow components inot future cash flows - cheng & hollie

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