国際会計研究学会年報 2006年度

The Time-Series Properties of Operating
Evidence from Japan
Cash Flows
M asumi Nakashima
Fukushima College

David A. Ziebart
University of Kentucky

Abstract
This study provides evidence on the time-series properties ofoperating cash ﬂows (OCF) and the expectation models forpredicting one year ahead OCF for a sample of Japanese ﬁrms.
First,we assess the OCF time-series pattern for an average ﬁrm
using the cross-sectional sample autocorrelation function.
Next, we study whether individual ﬁrmsʼOCF can be accuratelydescribed bya random walk (RW) process. In addition,we
examine the diversity of individual ﬁrmsʼOCF processes using
the Box-Jenkins methodology. Finally, we compare the accuracy of forecasts from ﬁrm-speciﬁc ARIM A, RW, andmultivariate, time-series regression approaches (M ULT).
The results indicate that, (1) the OCF time-series can begenerally depicted as a moving average process, (2) a numberof individually identiﬁed OCF processes diﬀer signiﬁcantly
from RW, (3) there is diversity in the ﬁrm-speciﬁc ARIMA
structures for OCF, and (4) that, when predicting OCF,
MULT outperforms the ﬁrm-speciﬁc ARIMA as well as thepremier RW. We ﬁnd that one year ahead CF prediction isenhanced by accrual components as well as the prior CF.

We appreciate anonymous reviewers and the moderator Lisa Owens and Atul Rai at the 2005
Southeast Regional American Accounting Association meeting and The chair Ichiro M ukai, Clemence
Garcia, Toshio Kimura and Kentaro Nomura at the 23rd Conference of Japanese Association forInternational Accounting Studies at Doshisha University for their valuable comments and helpful
suggestions. We thank Jackie Warren, Marian and Elizabeth Tisdale for their proofreading andfriendship. We also thank Shannon Wardlow at Converse College for the help in collecting data. We
gratefully acknowledge the ﬁnancial support of Fukushima College.

75

1. INTRODUCTIONﬁrmʼ
s earnings performance is aﬀected bycash ﬂows. However, Dechow and Di-

The relevance and usefulness of thechev (2002) ﬁnd that the measure of ac-

cash ﬂow and accrual components of cur-crual quality is positively related to earn-rent earnings for ﬁrmsʼexpected future

ings persistence.

attributes have been examined widely (e.
g. Sloan 1996; Barth et al.

Thus, from the previous evidence, it

2001; De-seems that the cash ﬂow and accrualchow and Dichev 2002; Tazawa 2004).

components of current earnings have an

The FASB asserts that information about

impact on the time-series properties ofenterprise earnings and its components

accounting information.

measured by accrual accounting general-

number of studies have been conductedly provides a better indication of enter-

regarding the time-series properties ofprise performance than information a-accounting earnings, a few studies exam-

bout current cash receipts and payments

ine the time-series properties and the(FASB 1978, para. 44).

Barth et al.

predictive ability of the cash ﬂow and(2001) ﬁnd that the cash ﬂow and accrual

accrual components of current earnings

components of current earning have sub-for future attributes. In particular, whilestantially more predictive ability for fu-

most prior studies focus on the U.S.ﬁrms,

ture cash ﬂows rather than several lags of

we investigate Japanese companies toaggregate earnings.

Sloan (1996) indi-

determine if the U.S.-based results hold.

cates that earnings performance attribut-

An analysis of operating cash ﬂow time-able to the accrual component of earnings

series properties is important since re-shows lower persistence than earnings

searchers try to use models of cash ﬂows

performance attributable the cash ﬂows,

to value securities; this requires esti-

and that most of the mean reversion takes

mates of expected future cash ﬂows. In

place in the ﬁrst year. He also ﬁnds that

addition, researchers utilize predicted

1)

Although a2)

Foster (1977) asserts that because management knows the stochastic process generate the reported
accounting series when making ʻsmoothingʼdecisions, an estimate of the expected accounting information is based on the past accounting series. In this context, knowing of a descriptively valid time-seriesmodel of accounting information is critical in determining the weights to be placed on each past
periodʼs accounting information.
In Japan, Sakurai (1994) investigates the time-series properties of accounting earnings numbers on
the basis of ʻ
averageʼresults of annual data from Japanese 794 listed ﬁrms over the period 1977‑1988
cross-sectionally and ﬁnds that the time-series properties of accounting earnings follow the random
walk models, consistent with Ball and Watts (1972)． M ori (1997) provides evidence on the timeseries properties of industryʼs average annual accounting earnings,using data of Bank of Japanʼs“Shuyo Kigyo Keiei Bunseki”for periods from 1951‑1990 and ﬁnd that undeﬂated earnings do notfollow the RW models and the M A models can be described for deﬂated earnings.

76

The Time-Series Properties of Operating Cash Flowsearnings as surrogates for future cash

(Lorek and Willinger 1996).

3)

ﬂows.

This study contributes to the cash ﬂow

For a set of Japanese companies, we

forecasting literature in following re-investigate whether the cash ﬂow andspects; ﬁrst, for a set of Japanese compa-

accrual components of current earnings

nies, we not only develop the expectation

can be used as predictors of future cash

models appropriate to the time-series

ﬂows. One way of predicting future cash

properties of operating

ﬂows is to estimate the process using thethrough the use of the B-J methodology,

cash ﬂows

4)time-series properties of past cash ﬂows.

but we also compare the predictive per-First,in this studywe assess the operating

formance of the various cash ﬂow expec-

cash ﬂow time-series properties of antation models for future cash ﬂows. Pre-“average”ﬁrm using the cross-sectional

vious studies have not tested operating

sample autocorrelation function. Second,

cash ﬂows time-series through the use of

we investigate whether individual ﬁrmsʼ

the B-J methodology for individual Japa-

operating cash ﬂows processes can benese ﬁrms. Second, we assess the accura-described by a random walk model.

cy of the forecasts generated from theThird, using the Box-Jenkins methodolo-

univariate cash ﬂows models against thegy (Box and Jenkins 1994), we identify

forecasts generated from the multivar-

and estimate ﬁrm-speciﬁc ARIMA models

iate,time-series regression model to inves-

for operating cash ﬂows.

Fourth, wetigate the role of accrual components.

compare the predictive performance ofFurthermore, since our variables are der-randomwalk models, ﬁrm-speciﬁc

ived from the statements of cash ﬂows,

ARIMA models, and multivariate, time-

not the balance-sheet approach, they areseries regression models (M ULT) usingmuch less noisy.

prior accrual components and operating

The reminder of the paper is organizedcash ﬂows as independent variables

as follows; Section Two reviews the

In order to predict earnings, researchers need to estimate a process that describes thetime series
properties of past earning. Also, they want to obtain better earnings expectations models in order tostudy the relation between stock prices and earnings. They need to know the time-series of pastearnings to try to derive a surrogate for the marketʼs expectation of earnings (Watts and Zimmerman,
1986, pp. 129 ‑136)．
Even if cash ﬂow information has predictive value, in order to prove that the cash ﬂow information
is useful for decision making, it is necessary to clarify that cash ﬂow information makes a diﬀerence todecisions, by improving decision makersʼcapacities to predict or by conﬁrming or correcting theirearlier expectations (FASB, 1980, para. 51) and to alter the securityʼs price (Yurikusa, 2001, p. 10)．
The marketʼs expectation of future earnings is likely to impound this information. Thus, in trying toinvestigate the information content of cash ﬂows, it is important to identify the expectation models inorder to describe the unexpected cash ﬂows appropriately (Yurikusa, 2001, pp. 139 ‑140)．

77

extant research. Section Three develops

erties and predictive ability of undeﬂated

the hypotheses. Section Four describes

cash ﬂow, cash ﬂow per-share and cash-

the data sample and speciﬁes the cash

ﬂow deﬂated by total assets. They identi-

ﬂow expectation models, and reports di-fy a diverse set of ﬁrm-speciﬁc ARIMA

agnostic tests. Section Five presents themodels for cash ﬂow and suggest that

results from the analyses.

multivariate, time-series prediction mod-Section Six

summarizes and concludes this study.

els (M ULT), which employ lagged values of earnings and short-term accruals,

2. LITERATURE REVIEW

outperform ﬁrm-speciﬁc ARIM A models,common-structure ARIM A (SAR andIn the U.S., Hopwood and M ckeown

SM A) models, and multivariate cross-(1992) investigate the time-series proper-

sectional regression models in one-step-ties of earnings per share and cash ﬂows

ahead quarterly cash-ﬂow predictions.

per share for all manufacturing ﬁrms.

McLeay et al. (1997) provide evidence

The averages of the autocorrelations arethat using the Ljung-Box serial correla-presented for both earnings per share andtion test of goodness-of-ﬁt, the random

operating cash ﬂows per share.

walk model does not ﬁt cash ﬂow data

Bothindividually-identiﬁed and premier mod-well.

The integrated moving average

els are compared on the basis of their ﬁt(IM A) and the exponentially-weighted

and the accuracy of predictions. Theirmoving average (EWM A) models haveresults suggest that for both variables

better ﬁts, particularly the EWM A mod-individually-identiﬁed models outperfor-els.

m premier models. They also ﬁnd that

Dechow and Dichev (2002) examine

the time-series properties of cash ﬂows

the relation between accrual quality andare quite diﬀerent than those of earnings,

earning persistence, based on the ﬁrm-

and that cash ﬂow time-series are consid-

speciﬁc regression, using the variableerably less predictable.

from the statements of cash ﬂows fromLorek et al. (1993) provide evidence

1987 through 1999. They ﬁnd that ﬁrms

that univariate ARIMA models of cashwith low accrual quality have more ac-

ﬂows generate more accurate forecasts

cruals that are unrelated to cash ﬂow

than the multivariate cross-sectional re-realization, and so have more noise andgression models. They focus on quarterly

less persistent in earnings.

data rather than annual data and ﬁndshow that accrual quality and level ofthat SAR ARIMA models are good candi-

accruals are incremental to each other indates for modeling and predicting cash

explaining earnings persistence with ac-

ﬂow series. Lorek and Willinger (1996)

crual quality the more powerful determi-

provide evidence on the time-series prop-

nant.

78

They also

The Time-Series Properties of Operating Cash Flows

In Japan, Yurikusa (2001) investigates

the quality of accruals and the predictive

the time-series properties of cash ﬂows

ability of forecast models for future cashfrom operations (OCF), cash ﬂows fromﬂows across 1,381 ﬁrms employing theﬁnancing (FCF), and cash ﬂows fromincome statement and balance sheet data

investing (ICF) of an “average” ﬁrmspanning 1989‑2001. He ﬁnds that afterusing funds ﬂow statements data for Jap-

controlling for forecast errors with theanese listed parent ﬁrms from 1987

model based only on cash ﬂows, the ac-

through 1999. He provides evidence thatcruals quality is positively related to theoperating cash ﬂows are better describedpredictive ability of the model including

by the integrated moving average models

accruals.

(IM A) since the IM A models outperformthe white-noise (WN) models and theindicate that the predictive ability of the

3. HYPOTHESES DEVELOPM ENT

IMA models for OCF and ICF are superi-Ali (1994) suggests that the mean seri-or to the WN models and the RW models.

al correlation for changes in cash ﬂows

For FCF,the predictive abilityofthe IM A

for the low operating cash ﬂows (CFO)

models and the WN models are superiorgroup is −0.08 and for the high CFO

to the RW models. He indicates that thegroup is −0.43. These results indicate

RW models do not always suit the time-that cash ﬂows exhibit signiﬁcant levels ofseries properties of cash ﬂows.

mean reversion for both groups, and arandom walk (RW) models. His results

Tazawa (2001) investigates the serial

diﬀerenced CFO has a ﬁrst-order negative

correlations of earnings changes,accruals

autocorrelation. Therefore, this evidencechanges, and cash ﬂow changes across 1,

suggests the time-series of CFO do not

012 ﬁrms through the use of the income

follow a random walk.

statement and balance sheet data over(1997) suggest that the ACFs tend to1967 to 1997. He provides evidence thatdecay after lag one and the PACFs tend to

the serial correlation for earnings changes

dampen towards zero for cash ﬂows, sug-is close to zero and ﬁnds that accruals

gesting an IM A (d, 1) process.

oﬀset the negative correlation in cash ﬂow

also suggest that a test of residualchanges to produce earnings changes that

autocorrelation using the Box-Pierce Q-

are much less negatively serially correlat-

statistic conﬁrms that for d＝1, residualed. In addition, his evidence, consistentautocorrelation is not signiﬁcantlywith Dechow et al.(1998), shows that cur-diﬀerent from zero at the 5% level andrent earning is a better predictor of futurethat the residual shocks are independent.

operating cash ﬂows.

Also, Tazawa

Lorek and Willinger (1996) suggest that

(2004) examines the a relation between

the SACFs and PACFs of the undeﬂated

M cLeay et al.

They

79

cash ﬂows exhibit a monotonic decline in

H1: The time-series properties of the oper-

the SACF at the seasonal lags (n＝4, 8,

ating cash ﬂows of an “
average”ﬁrm

and 12). This evidence supports the sea-

follow a random walk model and thesonal autoregressive models (SAR)

autocorrelation for anylag τ for oper-(000)×(100).

ating cash ﬂows changes is zero.

They also ﬁnd that theseasonal diﬀerenced cash ﬂows exhibit a
singular spike at lag 4 and this is suppor-

Hopwood and M ckeown (1992), Lorek

tive of the seasonal moving-average proc-and Willinger (1996), and McLeay et al.

ess models (SMA) (000)×(011).

(1997) discuss if individual ﬁrmsʼoperat-

Although previous studies provide evi-ing cash ﬂow diﬀer from a random walkdence that accounting earnings of anmodel. Hopwood and M cKeown (1992)“average” ﬁrm are generated by a ran-suggest that most of the cash ﬂow models

5)

dom walk process, few studies investigate

do not involve diﬀerencing, 50 of the 60

whether the time-series properties ofoper-

cash ﬂow series contain no diﬀerencing

ating cash ﬂows of an “average”ﬁrm canand that the most frequently identiﬁed

be described by a random walk. Becausemodels are AR (000)×(100) model (6 ofthe patterns of cash ﬂows as components

the 60 ﬁrms) and M A (001)×(000) mod-of current earnings over time aﬀect ﬁrmsʼ

el (5 of the 60 ﬁrms). Lorek and Willin-

ability to sustain future cash ﬂows, thisger (1996) suggest that only one ﬁrm ofstudy examines the time-series behavior

62 ﬁrms follows a random walk modelof operating cash ﬂows of an “average”

when ﬁrm-speciﬁc ARIMA models are

ﬁrm and determines if the OCF time-

identiﬁed for individual ﬁrmsʼquarterly

series process of an “average”ﬁrm can be

cash ﬂow series.

characterized by a random walk. Also,

speciﬁc ARIM A models for cash ﬂows,

we determine whether the deﬂator aﬀects

suggesting that 38 of 62 ﬁrm-speciﬁcthe underlying time-series properties of

ARIMA models for the cash ﬂow series

operating cash ﬂows. Thus, this leads toare the seasonal AR models or the season-

the following hypothesis;

al MA models.

They identify ﬁrm-

The most frequently

identiﬁed ﬁrm-speciﬁc ARIMA structureis the (000)×(011) SMA model, appearing 10 of 62 times for the cash ﬂows series.
Ball and Watts (1977) and Sakurai (1994) test the null hypothesis that autocorrelation for any lag
for earnings changes is zero, and Ball and Watts (1977) provide evidence that the median autocorrelations for all ﬁve lags for the changes in earnings per share are close to zero and are insigniﬁcantly
diﬀerent from zero and Sakurai (1994) suggests that the null hypothesis is not rejected at 5% level for
87.3% (693 ﬁrms) of the 794 ﬁrms. These two results suggest that the time-series properties ofaccounting earnings follow the random walk models.

80

The Time-Series Properties of Operating Cash Flows

If the best ﬁtting model is identiﬁed for

multivariate, time-series regression cash

individual ﬁrms, a number of ﬁrmsʼesti-

ﬂow prediction models are superior tomated processes may diﬀer signiﬁcantly

common-structure ARIM A models andfrom random walk models. There might

random walk models. They also demon-

be diversity among the processes generat-

strate that cash ﬂow prediction is enhan-

ing individual ﬁrmsʼ operating cash

ced by considering prior earnings andﬂows.

accruals.

As Lorek and Willinger (1996)

M cleay et al.(1997) suggest

assert,the cash ﬂow time-series properties

that the predictive ability of an

of an “average”ﬁrm may not necessarily

exponentially weighted moving average

be similar to those of individual ﬁrms

model is superior to the predictive ability

because the mean ACF and PACF valuesof random walk models as well as inte-

may mask ﬁrm-speciﬁc behavior.

We

grated moving average models by theidentify the ARIMA models to estimate

comparison among the mean absolutethe processes of cash ﬂows time-series forerror from those models.

each ﬁrm, using the B-J methodology.

Beaver asserts that accruals reﬂect

Thus, this leads to the following hypothe-managementʼ
s expectations about futuresis.

cash ﬂows and are based on an information system potentially more comprehen-

H2: Anumber ofﬁrmsʼestimated processes
generating operating

cash ﬂows

signiﬁcantlydiﬀer from a random walk.

sive than past and current cash ﬂows
(Beaver 1998, pp. 5‑6).

Barth et al.

(2001, pp. 56‑57) suggest that each accrual component may have a signiﬁcantHopwood and McKeown (1992) indi-

relation with future cash ﬂows.

Thiscate that for cash ﬂows, individually-

implies that the accrual components aid

identiﬁed models outperform the premierin predicting future cash ﬂow beyond

models. This suggests that individually-

current cash ﬂow. A number of studies

identiﬁed models are uniformly the mostthat ﬁnd that accrual components of cur-

accurate although their advantage overrent earnings have more predictive ability

the Brown-Rozeﬀand Griﬃn-Watts mod-for future cash ﬂows have been conducted

els are small for one-quarter-ahead fore-

in the US (e.g. Lorek and Willinger 1996;

casts. Lorek et al. (1993) suggest that

Dechow et al. 1998; Barth et al. 2001).

the univariate autoregressive-integrated-

To examine this, we compare the accu-

moving average models for quarterlyracy of the forecasts of future operating

cash ﬂows provide more accurate cash-

cash

ﬂow prediction than multivariate cross-identiﬁed ARIMA models with (1) fore-

sectional models. In addition, Lorek andcasts based on random walk models ap-

Willinger (1996) provide evidence that

plied for all the sample ﬁrms, and (2)ﬂows basedonindividually81forecasts obtained from a multivariate,through the issuance of American De-

time-series regression that uses past val-positary Receipt than before the reportingues of accrual components. These results

system of consolidated ﬁnancial state-

will provide evidence that accrual compo-

ments was introduced in Japan in 1977.

nents are useful in cash ﬂow prediction.

Although these Japanese SEC ﬁrms

Hence, this leads to the following hypoth-should have prepared and submitted theesis;

consolidated ﬁnancial statements in ac-

6)

cordance with the Japanese GAAP to7)

H3: There is no diﬀerence in absolute percentage error among (1) the fore-

M inistry of Finance as well as the othercasts of multivariate, time-series re-counting Deliberation Council (BADC)

gression models using accrual components, (2) the forecasts from ﬁrm-

approved as exception that these Japa-

speciﬁc ARIMA models, and (3) the

dated ﬁnancial statements in accordance

forecasts of a simple random walkwith the U.S. GAAP to M inistry of Fi-

model.

nance in stead of the Japanese GAAP

Japanese listed ﬁrms, the Business Ac-

nese SEC ﬁrms could submit just consoli-

8)statements. However ﬁrms enrolled in

4. RESEARCH M ETHOD OLOGY
4.1 Data Selection

the SEC after 1977 have not been approved as the exceptional ﬁrms, and havesubmitted each statement to the SEC andthe Financial Service Agency, respective-

The data used in this studyare from thely. Since data from these ﬁrms enrolledannual reports for the Japanese ﬁrms

in the SEC after 1977 are available justenrolled in the U.S. Securities and Ex-

from the year 1999 , we drop these ﬁrms

change Commission (SEC). These Japa-

from sample in this study. Some of thenese ﬁrms have prepared and disclosed

Japanese ﬁrms have abolished the enroll-consolidated ﬁnancial statements in ac-

ment in the SEC since 2003. Therefore,

cordance with the U.S. GAAP in order towe employ data of these Japanese SEC

ﬁnance from the US capital market

ﬁrms whose data spanning the years 1989

9)

10)

Accounting Principle for Consolidated Financial Statements has required Japanese listed ﬁrms toprepare and submit consolidated ﬁnancial statements to the M inistry of Finance since then.
The Japanese listed ﬁrms submit consolidated ﬁnancial statements to M inistryofFinance than before
1998. With one of series of structural reform of ﬁnancial systemin Japan, the role which Ministry of
Finance is responsible as for securities transactions is moved to the Financial Supervisory Agency(FSA)，the Japanese listed ﬁrm submitted them to the FSA from 1998‑2000. The FSA was reorganizedto establish the Financial Service Agency, the Japanese listed ﬁrm submitted to the Financial
Service Agency since July, 2000.

82

The Time-Series Properties of Operating Cash Flows

TABLE 1
Sample ﬁ
rms
Industry
Food
TextilesChemicals
M achinery
Electric
instruments

Transportationequipments
Precision
instruments
Trading andretailmerchandise

N u m b erof Firms

Name of Firms

1
1
1
2

Nippon Ham
Wacol
Fuji Photo Film
Komatsu
Kubota13

HitachiToshiba

NEC
Sanyo
Pioneer

Matsushita
TDK
Murata

1

Honda

2

Canon

Ricoh

4

ItochuMarubeni

to 2003 is available as sample.

M itsubishi
denki
Sony
Omron
M akita

KyoceraM itsui

Itoyokado

Japanese ﬁrms,there are just ﬁve years of

There are several justiﬁcations regard-

the Japanese standard consolidated cash

ing why we use reported cash ﬂow andﬂow statement data available, there areaccruals based on the SECʼs standard

ﬁfteen years of the SEC data available.

cash ﬂow statements for the Japanese

Fifteen yearsʼdata works for time-seriesﬁrms in this study. First, while for the

analyses. Second, employing the Japa-

Since,once these Japanese SEC ﬁrms chose to submit consolidated ﬁnancial statements in accordancewith the Japanese GAAP to the Ministry of Finance, this exception was not approved any more, thenumber of these exceptional ﬁrms decreased year by year. Also,because the ﬁrms which were enrolled
inthe SEC after 1977, such as, Toyota, NTT, or Orix were not approved for this exception, these ﬁrms
need to prepare one consolidated ﬁnancial statements in accordance with the U.S. GAAP to submit
them to the SEC and another consolidated ﬁnancial statements in accordance with the Japanese GAAP
to submit them to the Financial Service Agencyin Japan. Preparing two kinds ofconsolidated ﬁnancial
statements incurred burdensome costs over these ﬁrms. Thus, in order to mitigate the burden andavoid investors confused about the dual standard problem, the Financial Service Agency has approved
all Japanese ﬁrms which are enrolled in the U.S. SEC can submit consolidated ﬁnancial Statements inaccordance with the U.S. GAAP to the Financial Service Agency but in Japanese since 2003.
These ﬁrms include Advantest, Konami, Toyota, Nomura Holdings, NTT, NTT docomo, Orix, andTrend M icro.
These ﬁrms are Nippon Ham,Toshiba,Mitsubishidenki,Omron,Murata,Itochu,M arubeni,Komatsu, and Mitsubishishoji. In 2005, there are 23 ﬁrms which submit the U.S.GAAP consolidated ﬁnancial
statements to the Financial Service Agency.

83nese ﬁrmsʼdata in accordance with the U.

Therefore, in order to examine regarding

S. GAAP will make our results more com-whether the role of accruals for future

parable with the prior studies of the U.S.

cash ﬂows should be applied to the Japa-

ﬁrms. One of objectives of this study is tonese ﬁrms, we employ the same cash ﬂow

investigate Japanese ﬁrms to determine if

statement data following the U.S.GAAP.

the U.S.-based research results hold.

Third, although the cash ﬂow and ac-

Although, since the Accounting Standard

crual components of current earnings canBoard of Japan (ASBJ) respond tobe estimated from analyzing balance

world globalization and has tried to let

sheets and income statements, there may

the Japanese GAAP approaching the U.S.

be measurement errors (e.g. Bahnson et

GAAP, there are still minor diﬀerences ofal. 1996; Cheng et al. 1997; Nakashimaaccounting standards between the U.S.

2004). Furthermore, although the listed

and Japan, that is, there are diﬀerent

ﬁrms in Japan had produced funds ﬂows

accounting methods and the disclosurestatements from 1987 through 1999, the

13)

11)

format of ﬁnancial statements. As forfunds ﬂows statements had been unavail-statements of cash ﬂows, we can ﬁndable.

many similarities between the U.S. andwere not required to be audited by

Japan ; the structure, scope of cash

certiﬁed accountants.

ﬂows, the method of presenting operating

funds ﬂow statements were produced on

cash ﬂows; the direct method or indirect

an individual entitybasis,not on a consol-

method. However, there are still some

idated basis.

minor diﬀerences between the U.S. andJapan.

The diﬀerences in accounting

Also, the funds ﬂow statements
In addition, the

The FASB links a ﬁrmʼs future cash
ﬂows to dividends and capital gains

12)

standards between the U.S. and Japan

which investors receive.

Since anshould aﬀect the results of studies.

enterpriseʼs ability to generate favorable

Although as for tax aﬀect accounting, accounting for post-retirement beneﬁt, accounting for foreigncurrency translation and accounting for ﬁnancial instrument, ASBJ has reformed the Japanese accounting standards to be harmonized with the U.S. GAAP, there are still diﬀerences in accounting forlease, business combination, and impairment between the U.S. and Japan.
For the Japanese GAAP statements of cash ﬂows, even if we choose a direct method or indirect
method in presenting OCF, the items “interests and dividends received”, “interests paid,”and “income
taxes paid”should be presented after “subtotal”and before“cash ﬂows from operating activities.”We
display “interests paid”and “income taxes paid”in the category of OCF of the cash ﬂows statement,
not in a footnote. Also, if OCF are presented based on indirect methods, we start pretax net incomesand provide a reconciliation of net incomes to net cash ﬂow from operating activities to compute thesubtotal. These items are major classes of deferrals of past operating cash ﬂows and accruals of
expected future operating cash ﬂows, including “interest and dividend revenues”and “interest expenses.”Including these “interest and dividend revenues”and “interest expenses”into the reconciliation of net incomes is a characteristic of the Japanese statements of cash ﬂows.

84

The Time-Series Properties of Operating Cash Flows

cash ﬂows aﬀects both its ability to pay

Accordingly, the original series of OCF

dividends and interest the market prices

can be described as a random walk proc-of its securities, expected cash ﬂows to

ess expressed in the following equation :investors and creditors are related to
expected cash ﬂows to the enterprise(FASB 1978, para.39). Therefore, thisstudy focuses on operating cash ﬂows as a

OCF ＝OCF

＋ε

(1)

where
OCF ＝operating cash ﬂows at time t,

14)

future attribute.

ε＝the white-noise forecast error attime t.

4.2. Cash FlowExpectation Models
We use a cross-section analysis of the

In order to test H2, for each ﬁrm, we
identify tentative B-J type ARIM A mod-ﬁrmsʼ autocorrelation function (ACF)

els consistent with the series.

M odeland partial autocorrelation function

identiﬁcation is carried out by examining(PACF) of the operating cash ﬂows series

the pattern of ACF and PACF.

in order to verify whether the diﬀerenced

potential models for OCF process are

OCF are essentially white noise. If none

described below, that is autoregressiveof the values of the ACF or PACF for the(AR (1)), moving average (M A (1)), or

ﬁrst diﬀerences series are close to zero,autoregressive-moving average (ARM A

and no conﬁdence limits on the plot for(1,1)). We obtain estimates of parame-

any lag are exceeded, then we infer thatters of the tentative model and then we

the ﬁrst diﬀerenced OCF is white noise.

generate the ﬁnal model for series of each

The

Cheng et al. (1997) ﬁnd that reported cash ﬂows from operations have more incremental valuerelevance in market than estimated cash ﬂows from operations. They suggest that errors in estimatesof cash ﬂows from operations may impede their utility in explaining security prices. Nakashima (2004)
discusses the diﬀerence between the amount of cashﬂow from operating computed from a balance sheet
and an income statement and the reported amount of cash ﬂows from operating in a statement of cash
ﬂows for SEC Japanese ﬁrms. She ﬁnds that for 75% of the sample, the measurement error inestimating operating cash ﬂows is less than 3% and that this proxy used in the study does not matchthe actual operating cash ﬂows for 100% of the sample.
Researchers to date have deﬁned these future attributes in many ways. Beaver (1998, pp. 69 ‑76)
develops three important links between earnings and stocks: a link between stock price and future
dividends, a link between future dividends and future earnings,and a link between future earnings andcurrent earnings and treats future earnings and future dividends as future attributes.Sakurai (1994,p.
100) predicts future accounting earnings to use as a proxy of future cash ﬂows in assessing the valueof ﬁrmsʼstock. Watts and Zimmerman (1986, p. 65) assert that underlying the tests of associationbetween abnormal returns and unexpected earnings and the information content of earnings is thenotion that earnings are measures ofcurrent and future cash ﬂows. On the other hand,for the cash ﬂow
literature, Hopwood and M ckeown (1992) predict OCF per share. Lorek et al. (1993) and Lorek andWillinger (1996) measure future attributes as estimated OCF by the balance sheet approach. Barth elal. (2001) deﬁne future attributes as OCF reported in the statements of cash ﬂows.

85

ﬁrm.

The independent variables for MULTOCF ＝δ＋ϕ (OCF

)＋ε,

(2)

are derived from the statement of cash

(3)

ﬂows. Each independent variable is lag-)＋ε Θ ε , (4)ged one period. The ﬁrst set of independ-

OCF ＝μ＋ε Θ ε ,OCF ＝δ＋ϕ (OCF
where

ent variables includes operating cash

OCF ＝operating cash ﬂows at time t,

ﬂows. The second,third,fourth,and ﬁfth

ϕ Θ ＝the parameters of the model,

sets of independent variables are accrual

μ,δ＝the constant coeﬃcients,

components.

We also employ a multivariate time-relies on time-series data for OCF, ΔAR,

series regression model (MULT) to pro-

ΔINV, ΔAP and DEP variables that arevide a comparative assessment of theobtained over the same time period we

predictive ability of the RW and ARIM A

use when the ARIMA models are esti-

models for future cash ﬂows.

mated.

M ULTIn this analysis, MULT

MULT allows ﬁrm-speciﬁc pa-

includes a richer set of independent varia-

rameter estimation and includes a larger

bles and is based on the following equa-

set of explanatory variables than doestion :RW and ARIM A models. M ULT, unlike

OCF ＝Φ＋ΦOCF＋Φ AR

the ARIM A and RW models,incorporates＋Φ INV＋Φ AP

a parsimonious set of accrual components

＋ΦDEP

＋μ(5)

in addition to past values of the cash ﬂow

where
OCF

series. The use of M ULT helps resolve＝cash ﬂow from operations
at time t-1,

Φ＝regression coeﬃcient,ARAP

components enhance the accuracy of cash
ﬂow predictions.

＝decrease (increase) inreceivables at time t-1,

INV

the discussion about whether the accrual

4.3. Diagnostic Tests＝decrease (increase) in

It is customary to have the process

inventory at time t-1,

stationary. This implies a constant vari-＝increase (decrease) inance, no trend, and seasonality removed.

accounts payable and accrued

Since plotting the series of operating cash

liabilities at time t-1,

ﬂows shows neither trend,nor seasonality＝depreciation expense atfor each ﬁrm, we employ the original

15)

DEP

time t-1,
μ＝current disturbance term at time t.

data. For detection of heteroskedasticity,
it is useful to review the residuals. A

Trends should be removed from the series to make data stationary. If the original series contain
trends, they can be transformed into series without trends by taking ﬁrst and second diﬀerences of the
data.

86

The Time-Series Properties of Operating Cash Flows

histogram of standardized residuals

coeﬃcients between the ﬁve variables inshows departures from the normal proba-

M ULT. The minimum, maximum andbility plot and it shows a positive kur-

median of the correlation coeﬃcients

tosis. Although there is a constant vari-(AR and AP) are .153, .971, and .760

ance, the data depicts heteroskedasticity.

respectively. This indicates that multicol-

There are two methods for solving thelinearity may only be problematic for aheteroskedasticity problem : transform-few of the variables. However, we do noting the data using logs, and deﬂating thebelieve this is a problem since our focus isvariable by some measure of size (M ad-

not on the statistical signiﬁcance of indi-dala 2001, pp. 212‑217). Although trans-

vidual coeﬃcients. Also, Dielman (2001,

forming the data to log is often used top. 368) asserts that for values larger thanremove the trend from the data, we

10 for the variance inﬂation factors

choose to use the deﬂator. After deﬂating(VIF), multicollinearity may bevariables, the histogram and the normal

inﬂuencing the least-squares estimates ofprobability plot look much better than

the regression coeﬃcients.

the original data.

mum, maximum and median of VIF re-

The mini-

Diagnostic tests are employed to checkgarding AP is 1. 234, 146.590, and 5.670

the validity of M ULT. First, an F-test isrespectively and there are seven of theemployed to assess the overall prediction

variables with a VIF larger than ten.

signiﬁcance by testing the observed F

Pelosi and Sandiﬀer (2002, p. 620) indi-value is highly signiﬁcant at .05 level. Ifcate that, although multicollinearity

F ＞F, the hypothesis that thecauses signiﬁcant problems with the re-prediction by MULT is not useful can be

gression coeﬃcients, the overall ability ofrejected at .05 levels. When the degree ofthe model to predict values of the depend-

freedom and F value of each ﬁrm areent variable is not aﬀected.

assessed, the null is rejected for only ﬁve
ﬁrms.

Third, when there is autocorrelation
between the error terms arising in time-

Second, multicollinearity among theseries data, this autocorrelation of residu-

independent variables should be inspect-

als may result in a misstatement of R

ed. Since serious multicollinearity may

values. Accordingly, we need to observe

result in overstated standard errors forthe plot of the residuals arising in series.

the coeﬃcients and instability in the esti-

The Durbin Watson test is not applicablemates, inferences regarding the statistical

if the models have lagged variables as

signiﬁcance of individual independent

dependent variables (Maddala 2001, pp.

variables may be impacted (Dielman

245‑246). The ACF and PACF of MULT

2001, p. 366). Speciﬁcally, in our study

plots show 95% conﬁdence limits and thethere are ten pairwise correlation

estimated coeﬃcients of the lagged resid87

ual are signiﬁcantly diﬀerent from zero.

distributed, we employ nonparametricAlso, none of these particular coeﬃcients

tests, a Friedmanʼs S statistic test to test

was signiﬁcant for MULT estimated on

the hypothesis three.

the cash ﬂow data for the 25 sample ﬁrms,
indicating that the residuals are white
noise and autocorrelation is not present in
the residuals. On the other hand, when

5. RESULTS
5.1. Descriptive Statistics

we observe the plot of the residuals aris-

Panel A of Table 2 presents descriptive

ing by multivariate regression, the ACF

statistics for the components of earnings.

and PACF of regression plots exceed

Consistent with Barth et al. (2001),Table

conﬁdence limits and the probability val-

2 reports that the means and medians of

ue of the Box-Ljung statistic is high at all

NI and OCF are positive and ACCRUALS

lags, indicating that the residuals arising

are negative.

in regression are not generated by a white

median of ACCRUALS come from thenoise process.

Therefore, we use theinclusion of DEP in ACCRUALS. Also,

M ULT model in this study,not regression

consistent with Barth et al. (2001), whilemodel.

current accruals i.e., ΔAR, ΔINV, andThe negative mean and

In addition, we compare the in-

ΔAP are very smaller in magnitude andcremental explanatory power of M ULT

more variable, DEP, a noncurrent ac-

employing the accrual components as in-cruals, are great in magnitude and lessdependent variables with the incremental

variable.

explanatory power of M ULT using only

Panel B of Table B provides the Pear-

lagged cash ﬂow variables as independent

son correlations which illustrate the rela-variables. The median adjusted R fortions between our sample variables.

M ULT using the accrual components isSpeciﬁcally, we ﬁnd OCF and accruals toincreased from the median adjusted R

be signiﬁcantly negatively correlated (−for MULT using only the cash ﬂows.

0.598), suggesting these correlations areThis increase in explanatory power isin agreement with existing studies (De-

attributable to including the set of ac-

chow et al.1998; Dechow and Dichev

crual components employed in MULT.

2002; Tazawa 2001) This means that the

Finally, another diagnostic test perfor-

negative correlations between accruals

med in this study in order to the check theand OCF oﬀset negative serial correlation

distribution of absolute percentage errors

in accrual changes and the negative serialof cash ﬂow prediction. If they are nor-correlation in OCF changes respectively

mally distributed, standard parametric(Dechow et al. 1998, p. 144). Thus, as

test are used to test the hypotheses three.

Dechow et al. (1998, p. 140) assert that

However, since they are not normally

accruals adjust cash ﬂows for temporary

88

The Time-Series Properties of Operating Cash Flows

cash ﬂows due to the outlay for thevalues are summed across ﬁrms and aver-

expected increase in long-term working

aged to obtain the values reported incapital and the diﬀerence in timing of

Table 3. The ACF of the series process

cash ﬂows for purchases and inﬂows fromhas one signiﬁcant spike at lag 1, where

sales, it is expected that this accrual ad-

the PACF exhibits gradual attenuation

justments help predict future cash ﬂows.

and the ACF and PACF are negative.
That is supportive of the ﬁrst-order M oving Average (MA (1)) models (Yaﬀee

5.2. Premier Model Analysis
Table 3 presents the sample autocor-

2000).

relation and partial autocorrelation func-

Panel A and C of the table 1 presenttion averaged across the ﬁrms, for thesimilar patterns. The patterns ofautocor-

original data, the ﬁrst diﬀerenced data,

relation in the operating cash ﬂow data

and the original data deﬂated by total

are unaﬀected by the deﬂator. Because

assets. In accordance with the methodol-

these two series exhibit nearly identicalogy of Foster (1977), these ﬁrm-speciﬁctime-series properties, these ﬁndings sug-

TABLE 2
Panel A: Descriptive Statistics on Variables
Sample of 399 Firm-Year Observations, 1989 ‑2003
VariableNI
OCF
ACCRUALS
AR
INV
AP
DEP

Mean
0.0207
0.0616−0.0551−0.0060−0.00400.00430.0408Median0.01790.0591−0.0556−0.0053−0.0038
0.00370.0037

S.D.
0.0241
0.0516
0.06820.02850.02230.02430.0191Minimum−0.0695−0.0903−0.7195−0.3447−0.1925−0.05670.0015

Maximam
0.12700.5966
0.16380.12400.24280.24070.1164

OCF−0.512−0.1850.579
1.000−0.598ACCRUALS
0.883
0.682
−0.872−0.5981.000

Panel B: Correlations
AR
INV
AP
OCF
ACCRUALS

AR
1.000
0.437
−0.814−0.512
0.883

INV
0.437
1.000
−0.487−0.1850.682

AP−0.814−0.4871.000
0.579−0.872The variables are deﬁned as follows.
NI＝income before extraordinary items and discontinued operations.
OCF＝cash ﬂow from operations
ACCRUALS＝total operating accruals, calculated as AR＋ LNV− AP−DEP
AR＝change in accounts receivable per the statement of cash ﬂows
INV＝change in inventory per the statement of cash ﬂows
AP＝change in accounts payable per the statement of cash ﬂows
DEP＝depreciation expense
All variables are deﬂated by total assets at the end of the year.

89

gest that this deﬂator does not aﬀect theof operating cash ﬂows are analyzed byunderlying time-series properties of cash

the individual ﬁrm model approach inﬂows. This result is consistent with Lorek

this section.

and Willingersʼ(1996) ﬁndings.

First, we examine the distribution of

Next, we investigate whether the time-

autocorrelations for operating cash ﬂows

series process of the ﬁrst diﬀerenced oper-changes for all lags in order to investigate

ating cash ﬂows of an “average” ﬁrm

whether individual ﬁrmsʼprocesses offollows a random walk model. From theoperating cash ﬂows diﬀer signiﬁcantly

distribution of autocorrelations for oper-from a random walk. The autocorrela-ating cash ﬂows diﬀerences reported intions for operating cash ﬂows changes forpanel B, the median ACF and PACF for

all lags are not close to zero. The ARIMA

all lags for the series are not close to zero(010) models are ﬁtted to the processes

and are insigniﬁcantly diﬀerent from zerofor each ﬁrm in order to investigate

at any reasonable probability level. Also,

whether the Box-Ljung statistics is statis-

we observe the plot of ACF and PACF fortically signiﬁcant at any lag. There are

the ﬁrst diﬀerenced operating cash ﬂows,

two ﬁrms that the Box-Ljung statistics tovalues of the ACF or PACF on the plot for

the right of the plot are not statistically

any lag exceed the conﬁdence limits on

signiﬁcant and the probability is substan-

the plots. Accordingly, the null hypothe-tially greater than .05.

sis is rejected. This ﬁnding suggests an-

hypothesis that the residuals are whitenual operating cash ﬂows for an “aver-noise cannot be rejected at .05, the residu-

age”ﬁrm in general cannot be character-als from the series of the two ﬁrms appear

ized as a random walk.

to be white noise. However,because mostSince the null

individual ﬁrmsʼOCF are statistically

5.3. Firm-Speciﬁc Models

signiﬁcant and the probability is smaller

In the previous section, the premierthan .05, the null hypothesis that the re-model is identiﬁed based on the averagesiduals are white noise can be rejected.

patterns in ﬁrm-speciﬁc ACF and PACF.

Also, when we examine the plot of the

It is possible that the cash ﬂow time-series

ACF and PACF for the individual ﬁrmsʼ

properties of an “average”ﬁrm may notﬁrst diﬀerenced series, there are just fournecessarily be similar to those ofindividu-

ﬁrms that the plotted autocorrelations allal ﬁrms (Lorek and Willinger 1996).

fall within the 95% conﬁdence intervals.

Because the aggregation of ﬁrm-speciﬁc

These results show that most individual

ACF and PACF values may conceal theﬁrmsʼoperating cash ﬂows cannot be

particular ﬁrm-speciﬁc properties, each

described by a random walk model.

ﬁrmʼ
s individual ACF and PACF values

Next, the time-series properties of indi-

are examined and time-series properties

vidual ﬁrmsʼcash ﬂow information are

90

The Time-Series Properties of Operating Cash Flows

TABLE 3
Cross-Sectional Sample Autocorrelations and Partial Autocorrelations (1990‑2003)
Panel A : No Diﬀerence of Undeﬂated OCF
ACF
LAG
Mean
S.D.
1
0.271
0.315

PACF
Mean
0.268

S.D.
0.320

2
3
4
5

0.062
0.101
0.029
−0.017

0.283
0.253
0.170
0.165

−0.1090.023−0.096−0.045

0.194
0.204
0.175
0.150

6
7
8
9
10
11

−0.020−0.064−0.168−0.140−0.100−0.111

0.123
0.133
0.165
0.144
0.159
0.160

−0.088−0.101−0.118−0.100−0.051−0.047

0.173
0.154
0.132
0.123
0.123
0.110

12
−0.0910.136−0.05713−0.0940.133−0.04514−0.0950.106−0.040Panel B : First Diﬀerences of Undeﬂated OCF
LAG
Mean
S.D.
Mean
1
2
−0.3090.228−0.3443−0.1520.216−0.341
4
0.058
0.236
−0.164
5−0.0310.122−0.143
6−0.0240.216−0.078
7
0.067
0.190
−0.084
8
0.020
0.163
−0.0629−0.0800.160−0.08910−0.0160.118−0.120
11
0.008
0.111
−0.13612−0.0190.118−0.039
13
0.024
0.098
−0.08714−0.0130.105−0.042
Panel C : No Diﬀerence of OCF Deﬂated by Total Assets
LAG
Mean
S.D.
Mean
1
0.179
0.283
0.179
2
−0.0040.219−0.128
3
0.087
0.206
0.071
4
−0.0110.150−0.104
5−0.0460.168−0.0626−0.0050.157−0.1027−0.0810.165−0.0818−0.1840.146−0.0959−0.1300.143−0.08110−0.0780.158−0.05111−0.0830.127−0.02612−0.0330.138−0.05313−0.052
0.113−0.04414−0.0580.085−0.077
Note: ACF＝Autocorrelations function. PACF＝Partial Autocorrlations function.

0.109
0.090
0.106
S.D.
0.166
0.200
0.189
0.218
0.181
0.208
0.150
0.120
0.138
0.130
0.136
0.090
0.112
S.D.
0.283
0.205
0.182
0.205
0.141
0.184
0.174
0.122
0.116
0.104
0.117
0.103
0.098
0.100

91

examined in accordance with the method-

5.4. The Predictive Ability

ology of B-J ; identiﬁcation of the model,

One-period-ahead operating cash ﬂow

estimation of parameters, and the diag-

predictions are generated for the three

nostic tests of model ﬁt. After the data isprediction models ; RW, ﬁrm-speciﬁc

made stationary a tentative model is

ARIMA, and MULT.

identiﬁed. The identiﬁcation procedure isestimated using data beginning with 1990

carried out by studying the behavior ofand ending with 2003, within sample.

the autocorrelation and partial autocor-

The accuracy of one-period-ahead cash

relation functions.

The time-series of

ﬂow predictions is assessed by comparing

observations for each ﬁrmʼs operating

the mean absolute percentage error

cash ﬂows beginning 1988 and ending 2003

(MAPE) ofeach model. Table 5 presents

are constructed. In the estimation step,

descriptive statistics for the MAPE of

we compute initial estimates for the pa-

each model.

rameters of the tentative model and allow

M APE from MULT is the lowest, while

the computer program to generate the

M APE from RW is the largest.

ﬁnal estimates by an iterative process.

indicates that the M ULT forecasts outper-

After estimation of the tentative model

form the ﬁrm-speciﬁc ARIMA as well as

16)

All models are

17)

The result shows thatThis

has been derived, diagnostic checks arecarried out to test the adequacy and thecloseness of the ﬁt of the model to data,by checking whether its residuals are random (white) noise.
Table 4 shows the frequency ofidentiﬁed ﬁrm-speciﬁc ARIMA model

TABLE 4
Fequencyof Identifed Firm-Speciﬁc ARIMA Model
Structure of CFO deﬂated byTotal Assets(pdp)×(PDQ)

Number of Firms(000)×(000)0structure of deﬂated operating cash ﬂows.

(100)×(000)3The most frequently identiﬁed ﬁrm-(200)×(000)4speciﬁc ARIM A structures are the(001)×(000)3(002)×(000)2(101)×(000)3(102)×(000)0(001), and ARIM A (101) appear each for(201)×(000)1three of the 25 ﬁrms we study.

(301)×(000)0(110)×(000)0(210)×(000)6(011)×(000)0there is a great diversity in the ﬁrm-(012)×(000)1speciﬁc ARIMA structures for operating(013)×(000)1(220)×(000)1ARIMA (210). There are four ﬁrms with
ARIMA (200).

ARIMA (100) ARIM A
Theremainder of the ﬁrms have various model structures. These results suggest thatcash ﬂows.

25

92

The Time-Series Properties of Operating Cash Flows

TABLE 5
Descriptive Statistics on Mean Absolute Percentage Errors of One-Period-Ahead
Deﬂated Cash FowPrediction
ARIM A

Model

Mean
0.3585Median0.2389S.D.
0.3280

Minimum
0.0000Maximum
1.0000RW

0.4076

0.29700.34060.00161.0000M ULT

0.30650.18290.30310.00131.0000

Where;
ARIM A＝Firm-speciﬁc univariate ARIMA model
RW ＝Random walk modelM ULT＝M ultivariate, time-series regression model

the RW forecasts.
Next, we use these prediction models as

pooled M APE metrics is M ULT (.276),
outperformingﬁrm-speciﬁc ARIM A

benchmark models. Table 6 shows the(.400) and RW(.347). This indicates that

M APE metrics for the deﬂated cash ﬂows

M ULT outperforms ﬁrm-speciﬁc ARIMA

series across the three cash ﬂow predic-

as well as RW. In addition, ﬁrm-speciﬁction models for each year (1990‑2003)

ARIMA enhances the predictive ability

and on a pooled basis across all years.

better than the premier random walk

We also assess the accuracy of one-year-

models do. The predictive performances

ahead cash ﬂow predictions by employingof the models are also compared by

the Friedman ANOVA ranks test. For

examining the average rank for each

each year, the cash ﬂow prediction model

model. MULT has the smallest average

yielding the smallest absolute percentage

rank for 1990, 1992, 1993, 1994, 1995, 1996,

error is given a rank of one; the next

1997, 1998, 1999, 2000, 2003, and on thesmallest error is given a rank of two andpooled basis. Friedmanʼs S-statistics are

so on until the model yielding the largest

signiﬁcant at 0.1 level for 1990, 1991, 1992,

error is given a rank of three. This test

1994, 1995, 1997, 1998, 2001, 2003, and on

examines whether there is a statisticallythe pooled basis. Overall, these results

signiﬁcant diﬀerence in the average ranks

suggest that MULT has a better predic-of the models compared.

tive performance. These results indicate

The best performing deﬂated cash ﬂows
prediction model on the basis of thethat cash ﬂow prediction is enhanced byconsidering accrual accounting data.

All variables employed in this analysis are deﬂated by total assets at the end of the year to reduce
heteroscedasticity.
We truncate all forecast errors greater 100 percent to 100 percent.

93

94

1990

1991

1992

1993

1994

1995

1996

PooledMAPE Avg Rank
0.347
1.92
0.400
2.77
0.276
1.31
14.000＊＊＊

Where:
ARIM A＝Firm-speciﬁc, univariate ARIM A model
RW ＝Random walk modelM ULT＝M ultivariate, time-series regression model
0.01 level
0.05 level
0.001 level

ARIM A
RW
M ULT

1998

1999

2000

2001

2002

2003

MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank
0.352
1.84
0.368
2.02
0.343
2.06
0.285
2.00
0.200
1.76
0.316
1.94
0.228
2.17
0.477
2.42
0.376
2.26
0.354
2.02
0.356
2.20
0.381
2.32
0.395
2.04
0.289
2.29
0.294
1.74
0.306
1.72
0.333
1.92
0.267
1.80
0.217
1.92
0.350
2.02
0.171
1.54
7.489
4.207
0.274
2.174
4.333
0.149
7.750
＊＊
＊
＊
＊＊

1997

MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank MAPE Avg Rank
0.418
2.15
0.391
2.38
0.497
2.10
0.358
1.90
0.411
2.20
0.434
2.04
0.455
2.08
ARIM A
0.440
2.20
0.361
1.78
0.515
2.26
0.430
2.24
0.385
2.16
0.492
2.25
0.408
2.04
RW
0.294
1.65
0.288
1.84
0.343
1.64
0.354
1.86
0.265
1.64
0.380
1.71
0.408
1.88
M ULT
Friedman
4.765
5.871
5.453
2.449
5.191
3.909
0.667
ANOVA
＊
＊＊
＊
＊
＊
SigniﬁcanceModel

TABLE 6
Mean Absolute Percentage Errors of One-Step-Ahead Deﬂated Cash Flows Predictions (1990‑2003)

The Time-Series Properties of Operating Cash Flows

6. CONCLUSIONS, LIM ITATIONS AND FUTURE RESEARCH
In this study, we provide evidence regarding the time-series properties of oper-

as well as RW. Consistent with the resultsin the U.S. (e.g. Loreck and Willinger
1996; Barth et al. 2001) , these ﬁndings
indicate that one year ahead cash ﬂow
prediction is enhanced by including accrual components.

ating cash ﬂows and the accuracy of the

Although the sample ﬁrms in this study

predictive performance of the expectation

may be representative of ﬁrms in Japanmodels for one year ahead operating cashthat complied with the U.S. GAAP, theﬂows. First, this study assesses operating

limited number may aﬀect the general-

cash ﬂows time-series properties of an

izability of our results.

“average”ﬁrm by cross-sectional sample

identiﬁcation of the best ﬁtting model

autocorrelation function. Secondly, we

may be limited since the standard errors

investigate the individual ﬁrmsʼoperating

for the ACF and PACF are large whencash ﬂows properties in accordance to thethere are less than ﬁfty observations.

B-J methodology. Thirdly, we compare

Nevertheless,it is expected that this study

the predictive performance of RW, ﬁrm-

provides a ﬁrst step in understanding thespeciﬁc ARIM A, and M ULT that employ

OCF time-series properties of Japanesepast values of accruals components andcompanies. The recent availability andoperating cash ﬂows as independent vari-

the long-term accumulation of actual

ables.

cash ﬂow statements data in Japan will

In addition,

Our results indicate that the time-series

provide opportunities for signiﬁcant fu-

properties of operating cash ﬂows of anture research to determine the robustness“average” ﬁrm can be depicted as aof these ﬁndings. Also, while we examine

moving average (MA (1)) process and,the predictive performance across various

in general, cannot be characterized as amodels using in-sample tests, the robust-

random walk. We provide evidence thatness of the results can be best determined

the autocorrelation patterns in operatingby out-of-sample forecast comparisons.

cash ﬂow data are unaﬀected by thedeﬂator we used.

Although we discuss the role ofthe cash

Our results suggest

ﬂow and accrual components of current

that, in general, individual ﬁrmsʼoperat-

earnings for future cash ﬂows, the quality

ing cash ﬂows cannot be described byof accruals has not examined yet. The

RW. Also, we identify the diversity inquality of accruals, and the predictiveﬁrm-speciﬁc ARIM A structures for oper-

performance and the quality of the indi-ating cash ﬂows. We provide evidence

vidual components of accruals should bethat the predictive accuracy of MULT isinvestigated in the future.

clearly superior to ﬁrm-speciﬁc ARIM A
95

REFERENCES
Ali, A. 1994. The incremental information content of earnings, working capital from opera-

Statement of Financial Accounting Concepts
No.2 : Qualitative Characteristics of Accounting Information.

tions, and cash ﬂows, Journal of Accounting
Research 32(1)(Spring): 61‑74.
Bahnson, P.R., P.B.W. Miller, and B.P. Budge.
1996. Nonarticulation in cash ﬂow statements

Foster, G. 1977. Quarterly accounting data :time-series properties and predictive ability
results. The Accounting Review 52 (January): 210‑223.

and implications for education, research andpractice, Accounting Horizon 10 (4),Gaynor, P.E., and R.C. Kirkpatric. 1994. TimeSeries Modeling and Forecasting in Business(December): 1‑15.
Ball, R. and R.L. Watts. 1972. Some time-and Economics. M cGraw-Hill.
Greene, W.H. 2003.
Econometric Analysis.

series properties of accounting income, Journal of Finance 29(3)(June): 663‑682.

Prentice-Hall.
Hopwood, W.S., and J.C.

McKeown. 1992.

Barth, M .A., D.P. Cram, and K.K.Nelson. 2001.
Accruals and the prediction of future cash
ﬂows, The Accounting Review 76(1), (Janu-

Empirical evidence on the time-series properties of operating cash ﬂows. Managerial
Finance 18: 62‑78.

ary): 27‑58.
Beaver, W.H. 1998. Financial Reporting an
Accounting Revolution. Prentice-Hall.

Kamata, N. and Saito, K. 1997. Genkinshushi
bunseki no shin giho ［New Approach ofCash Flow Analyses］. Chuokeizaisha.

Box, George E.P., G.M. Jenkins, and G.C.
Reinsel. 1994. Time Series Analysis, Forecasting and Control. Prentice-Hall.
Cheng, C.S.A., C. Liu, and T.F. Schaefer. 1996.
Earning permanence and the incremental in-

Lorek, K.S., T.F. Schaefer, and G.L. Willinger.
1993. Time-series properties and predictive
ability of funds ﬂow variables. The Accounting Review 68 (January): 151‑163.
Lorek, K.S., and G.L. Willinger. 1996. A

formation content of cash ﬂows from operations, Journal of Accounting Research 34 (1)(Spring): 173‑181.
Cheng, C.S.A., C.Liu, and T.F. Schaefer. 1997.

multivariate time-series prediction model for
cash-ﬂow data. The Accounting Review 71(January): 81‑101.

The value-relevance of SFAS no.95 cash ﬂows
from operations as assessed by security market eﬀects, Accounting Horizons 11(3)(September): 1‑15.
Dielman, T. 2001. Applied Regression Analysis
for Business and Economics, Duxbuy.
Dechow, P. M ., S. P. Kothari, and R. L. Watts.
1998. The relation between earnings and cash
ﬂows, Journal of Accounting and Economics
25: 133‑168.
Dechow, P. M ., and D. Dichev. 2002. The quality of accruals and earnings: the role of accruals estimation error. The Accounting
Review 77: 35‑59.
Financial Accounting Standards Board. 1978.
Statement of Financial Accounting Concepts
No.1 : Objectives of Financial Reporting by
Business Enterprises.
Financial Accounting Standards Board. 1980.

96

Maddala, G.S. 2001. Introduction to Econometrics. Wiley and Sons.
McLeay, S, J. Kassab,and M .Helan. 1997. The
incremental information content of accruals:
Evidence based on the exponential smoothingof levels and trends in pre-tax earnings, funds
ﬂow and cash ﬂow. Journal of Business
Finance & Accounting 24(7)&(8), (September): 1147‑1167.
Mori, H. 1997. Kaikeirieki to jikeiretsubunnseki［The Time-Series Analysis of Accounting Income］. Tokyo : M oriyamashoten.
Nakashima, M . 2004. Chokusetsuho cash ﬂow
information no yosoku noryoku ni kansuru
jissho kenkyuu ［The predictive ability of
direct method information : empirical evidence from Japan］, Accounting Progress 5:
107‑128.
Pelosi, M .K. and T.M. Sandifer. 2002. Doing
Statistics for Business with Excel-Data, Infer-

The Time-Series Properties of Operating Cash Flows
ence, and Decision Making. 2 edition, John
Wiley & Sons.
Sakurai, H. 1994. Kaikeirieki joho no yuyosei

Watt,R.S.,and J.L. Zimmerman.1986. Positive
Accounting Theory. Prentice-Hall.
White, H. 1980. A heteroskedasticity-consistent［The Usefulness of Accounting Income］.
Tokyo : Chikurashobou.
Sloan, R.G. 1996. Do stock prices fully reﬂect
information in accruals and cash ﬂows about

covariance matrix estimator and a direct testfor heteroskedasticity, Econometrica 48(M ay): 817‑838.
Yaﬀee, R. 2000. Time Series Analysis andfuture earnings ? The Accounting Review 71(3), (July): 289‑315.

Forecasting. San Diego : Academic Press.
Yoshida, K. 2002. Wagakuni ni okeru cash ﬂow

Suda, K. 2000. Zaimu kaikei no kino-riron tojissho ［Positive Theory of Financial Ac-

yosoku no bunnseki ［The analysis of cash
ﬂow prediction in Japan］, Contemporary

counting］. Tokyo : Hakutoshobo.
Tazawa, M. 2001. Kaikeirieki to cash ﬂow no

Disclosure Research 3 (March): 1‑13.
Yurikusa, H. 2001. Cash ﬂow kaikei joho nokankei-hasseikoumoku no yakuwari wo
toshite-［The relation between earnings andcash ﬂows-from the accruals］,SangyoKeiri 61

yuyosei ［The Usefulness of Cash Flow Accounting Information］. Tokyo ; Chuokeizaisha.

(1): 100‑114.
Tazawa, M . 2004. Hassei komoku no shitsu to
cash ﬂow yosoku no bunnseki ［The quality

Ziebart, D.A., and H.Guo. 2003. A general
analysis of the impact of scale diﬀerences
when interpreting R as an indicator of valueof accruals and the prediction of future cash
ﬂows in Japan］, Contemporary Disclosure
Research 5 (September): 11‑22.

relevance, The 2003 Meeting of the Southeast
Region of American Accounting Association.

97