European Journal of Economics, Finance and Administrative Sciences
ISSN 1450-2275 Issue 40 (2011)
© EuroJournals, Inc. 2011
http://www.eurojournals.com/EJEFAS.htm
Are Operating Cash Flows a Superior Predictor of Future
Operating Cash Flows than Earnings? Evidence from Jordan
Mamoun M. Al-Debi'e
The University of Jordan, Faculty of Business
Department of Accounting, Amman, Jordan
Tel: +962-6-5355000; Fax: +962-6-5330695
E-mail: [email protected]
Abstract
This study examines the relative predictive ability of current operating cash flows and
current earnings for future operating cash flows for a sample of service and industrial
shareholding companies listed on Amman Stock Exchange in Jordan during the period
2000-2009. The results show that the predictive ability of operating cash flows is stronger
than that of earnings for future operating cash flows for one- to three-year-ahead forecast
horizons. Furthermore, the results reveal that such predictive ability is stronger for large
companies, companies with short operating cycle, and companies reporting positive
operating cash flows. These findings have important implications for valuation purposes
and raise questions regarding the value relevance of earnings compared with operating cash
flows.
Keywords: Operating Cash Flows, Earnings, Company Size, Operating Cycle, Negative
Operating Cash Flows, Prediction, Jordan.
1. Introduction
The purpose of this study is to examine the relative ability of operating cash flows and earnings in
predicting future operating cash flows in Jordan.
The primary objective of financial reporting is to provide useful financial information to help
investors, creditors, and others to assess the amount, timing and uncertainty of prospective net cash
inflows to the related enterprise1. Besides the three common statements that companies include in their
annual reports, The 1997 Company Law of Jordan No. 22 requires that public shareholding companies
should prepare their accounts in accordance with International Accounting and Auditing Standards.
According to this 1997 Company law, the preparation of the statement of cash flows becomes
mandatory. The 2002 Securities Law of Jordan No. 76 requires all public shareholding companies to
fully comply with the International Financial Reporting Standards (IFRS) requirements in the
preparation of their annual reports. Furthermore, an amendment to the Securities Law of Jordan in
2004 asserted on the adoption of IFRS by all Jordanian companies subject to Jordan Securities
commission monitoring (Al-Akra et al., 2009).
The purpose of the statement of cash flows is to give users of financial statements a basis on
which to evaluate the entity's ability to generate cash and cash equivalents and its needs to utilize those
1
The objectives of financial reporting, FASB (2008).
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European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
cash flows2. The purpose of providing cash-based information along-with accruals-based information
is that accruals-based information mitigates the timing and matching problems inherent in cash flow
information.
Francis and Schipper (1999) state that financial information is value-relevant if it contains the
variables used in a valuation model or assists in predicting those variables. The market value of a
company's share is measured by the present value of future expected cash flows discounted at an
appropriate rate. Therefore, the value-relevance of earnings depends on its ability to predict future cash
flows.
Numerous previous studies documented that the value-relevance3 of earnings has declined over
time. For example, Lev (1989) argued that the explanatory power of the returns-earnings regression is
too low to be economically relevant. Lev (1997) reported a steady decline in the value-relevance of
earnings over time. Amir and Lev (1996) showed that earnings, book values, and operating cash flows
have no information content on a stand-alone basis in regards to firm value in the intangible-intensive
cellular industry. Furthermore, Hayn (1995) suggested that the reason behind the decline in the valuerelevance of earnings is due to firms increasingly reporting negative earnings. Other researchers
suggested that the value-relevance of earnings and book values move inversely to one another specially
when earnings are negative or contain nonrecurring items (e.g. Berger et al., 1996; Collins et al., 1997).
Notwithstanding the above findings, the Financial accounting standard board (FASB) (1978)
statement states that current earnings are a better predictor of future net cash inflows than current cash
flows.
In the absence of research in Jordan on the relative predictive ability of operating cash flows
and earnings for future operating cash flows, this paper examines this issue and takes into
consideration the effect of firm characteristics such as company size, length of operating cycle, and
sign of operating cash flows.
The rest of the study is organized as follows. The researcher starts with reviewing related
previous literature on the predictive ability of operating cash flows and earnings. Then the study
methodology is introduced including the study sample and period, the variables under examination,
and models of the study. The final part of the study reports the empirical results and conclusions of the
study.
2. Literature Review
Finger (1994) tested the predictive ability of earnings for future earnings and operating cash flows as
well as the predictive ability of operating cash flows for future operating cash flows over the period
(1935-1987). Time series regression models were ran for each of the 50 companies included in her
sample to examine the ability of earnings (operating cash flows) to predict future earnings and
operating cash flows (operating cash flows) over one through eight years ahead. She used both withinsample and out-of-sample prediction tests. For the purpose of out-of-sample prediction tests she used
the random walk model and Root Mean Square Errors were calculated. Overall, the results show that
earnings, alone and with operating cash flows, are a significant predictor of operating cash flows.
Moreover, she found that operating cash flows are a better short-term predictor of operating cash flows
than are earnings, both within- and out-of-sample, and the two predictors are nearly equivalent in the
long-term.
Barth et al. (2001) aimed at examining the role of accruals in predicting future operating cash
flows in the US over the period (1987-1996). They developed their research model on the basis of
Dechow et al. (1998) accrual process model. Consistent with their predictions, they found that
disaggregating earnings into cash flow and six major accrual components -change in receivables,
2
3
International GAAP (2008, p. 2564)
An accounting amount is defined as value relevant if it has a predicted association with stock prices. See (Beaver, 1998
and Barth, 2000).
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European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
change in inventories, change in payables, depreciation, amortization, and other accruals- significantly
enhances the predictive ability of earnings for future operating cash flows. Moreover, they documented
the same results when using different proxies – share price, returns, and discounted cash flows- for
future operating cash flows and when controlling for operating cycles and industry memberships.
Clinch et al. (2002) examined the incremental explanatory power of operating cash flows
components over aggregate operating cash flows for stock returns as well as the incremental predictive
ability of these components of future operating cash flows in Australia during the period (1992-1997).
They argued and found that disaggregating operating cash flows into cash and accrual components
(under the indirect method) and into cash inflows and cash outflows components (under the direct
method) improves the explanatory power of the relationship between stock market returns and
operating cash flows compared with that of the relationship between returns and aggregate operating
cash flows. Furthermore, decomposing operating cash flows into its components, especially under the
direct method, increases the predictive ability of one-year-ahead operating cash flows.
Kim and Kross (2005) investigated the relationship between current earnings and one-yearahead operating cash flows over the period (1973-2000). Although the relationship has been previously
investigated in the US4, they aimed at examining whether this relationship has improved or
deteriorated over time. They used time- series as well as annual cross-sectional regressions of oneyear-ahead operating cash flows on current earnings. In-sample and out-of-sample prediction tests were
used. The accuracy of predictions was tested using Theil's U. The results show that the accuracy of
future operating cash flows predictions based on current earnings has increased over time regardless of
age, size, dividend-paying ability, and result of operations of the company. However, their results show
that in one-year-ahead predictions of operating cash flows current earnings performs better than current
operating cash flows.
Additionally, Jabr and Al-Debi'e (2008) examined the effect of the sign of earnings and
operating cash flows on their information content in regards to stock returns in Jordan over the period
(1995-2003). The results of the regression models show that positive earnings and positive operating
cash flows (when using the level and the change specifications signs) have significant information
content, however, when the level or change sign of either variable is negative then the variable loses its
information content. Furthermore, the results suggest that Amman Stock Exchange (ASE) does not
react identically to the sign of both variables; ASE reacts positively to positive earnings and negative
operating cash flows and negatively to negative earnings and positive operating cash flows.
Farshadfar et al. (2008) tested the predictive ability of earnings and operating cash flows for
one-year-ahead operating cash flows in Australia over the period (1992-2004). They also used two
traditional measures of cash flows5. Size has been included, as a contextual variable, in the regression
models. They used OLS as well as fixed affects regression models. Both within-sample and out-ofsample prediction tests were employed. The results show that operating cash flows are a better
predictor of future operating cash flows than earnings. The traditional measures of cash flows are
found to be less informative compared with reported operating cash flows regarding the prediction of
on-year-ahead operating cash flows. Finally, they provided evidence that the predictability of operating
cash flows is superior to earnings regardless of the size of company, and the predictability of earnings
and operating cash flows in large companies is significantly greater than that in medium and small
companies.
In a recent study for Habib (2010), the predictive ability of current operating cash flows and
earnings for future operating cash flows in Australia during the period (1992-2007) has been examined.
He extended prior Australian research on cash flow prediction by examining future cash flow
predictions for one-, two- and three-year-ahead forecast horizons6. Furthermore, he considered
4
5
6
See Barth, Cram, and Nelson (2001) and Dechow, Kothari, and Watts (1998).
The two traditional measures are; Earnings plus depreciation and amortization expenses, and working capital from
operations.
See Percy and Stokes (1992), Clinch, Sidhu and Sing (2002), and Farshadfar, Ng and Brimble (2008).
39
European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
company size, operating cycle, cash flow variability, and whether the operating cash flows of the
company are positive or negative as additional contextual variables that are likely to affect the
predictive ability of current operating cash flows and earnings for future operating cash flows. Finally,
he compared between a cross-sectional operating cash flows prediction approach and a companyspecific time series prediction approach. The results, based on an out-of-sample prediction approach
with forecast errors measured using Theil's U (Theil, 1966), revealed that current operating cash flowsbased prediction model has the strongest predictive ability for future cash flows. The predictive ability
of this model is larger for smaller companies, companies with a long operating cycle, companies
generating negative cash flows and companies characterized by high cash flow variability.
Arthur et al. (2010) examined the incremental information content of the components of
operating cash flows in Australian over the period (1992-2005). Their aim was to determine whether
decomposing operating cash flows into cash and accrual components and into core and non-core
components7 would improve the explanatory power and predictive ability of those components with
respect to future earnings. The results showed that disaggregating operating cash flows into the lowest
level subcomponents based on reported information yields a significant increase in explanatory power
over the model which just uses aggregate operating cash flows. Furthermore, the results showed that
the prediction error for the disaggregated model with respect to future earnings is significantly lower
than that of the aggregated model. Finally, they found that decomposing or combining core
components into receipts and payment has the same explanatory power, whereas combining non-core
components yields a lower explanatory power.
Lev et al. (2010) examined the usefulness of accounting estimates for predicting operating cash
flows, free cash flows, net income, and operating income in the US over the period (1988-2004). They
used in-sample and out-of-sample prediction tests. They found that current operating cash flows, for
one- to three-year forecast horizons, is a better predictor of future operating cash flows and free cash
flows than current net income8. Furthermore, they presented results suggesting that operating cash
flows and the changes in working capital items (except for inventory) outperform current earnings and
disaggregated estimates-based accruals in predicting future operating cash flows and free cash flows.
3. Methodology
3.1. Study Variables and Models
To achieve the main objective of this study; which is to examine the relative ability of current
operating cash flows and earnings in predicting future operating cash flows, the following variable
were measured:
•
Operating cash flows (ROCFit). This is calculated by dividing reported annual operating
cash flows by average of total assets for company i in year t.
•
Net income (ROAit). This is calculated by dividing annual net income by average of
total assets for company i in year t.
Both variables were deflated by average total assets to reduce the effect of heteroscedasticity9.
The study also provides many diagnostic checks for the effect of size, length of operating cycle,
and sign of operating cash flows on the relative ability of current operating cash flows and earnings in
predicting future operating cash flows.
7
8
9
Core components include cash received from customers and cash paid to supplies and employees. Non-core components
include income taxes paid, interest paid or/and received, dividends paid, and other operating cash flows. This can be
looked at as resembling the direct method of preparing the operating cash flows section of the statement of cash flows.
However, decomposing operating cash flows into cash and accruals resembled the indirect method.
This result is inconsistent with Kim and Kross (2005) findings that in one-year-ahead predictions of operating cash flows
current earnings perform better than current operating cash flows.
See for example Habib (2010); Kim and Kross (2005).
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European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
Size is measured by total assets at the end of each year. Length of operating cycle (OCit) is
measured as follows:
OCit= RCPit + ICPit
Where,
RCPit is receivables conversion period; RCPit = 365(AvRecit / Sit)
ICPit is inventories conversion period; ICPit = 365(AvInvit / CGSit)
Sit: Net credit sales for company i in year t;
AvRecit: Average of receivables for company i, calculated by dividing (2) into the sum of
Receivable at the end of year t-1 and Receivables at the end of year t;
CGSit: Cost of goods sold for company i in year t;
AvInvit: Average of inventories for company i, calculated by dividing into (2) the sum of
Inventories at the end of year t-1 and Inventories at the end of year t.
The following OLS regression models are used in this study to test the predictive ability of
current operating cash flows and earnings for one- to three-year-ahead operating cash flows:
The operating cash flows Model:
ROCFit +1,…,t + 3 = α it + β it ROCFit + ε it .
The Earnings Model:
ROCFit +1,…,t + 3 = γ 1it + γ 2it ROAit + ε1it .
3.2. Study Sample
The study sample includes all Service and Industrial public shareholding companies listed on Amman
Stock Exchange (ASE) during the period (2000-2009). The total number of Service and Industrial
companies listed on ASE in the year (2010) is (68) and (77) respectively. The study excludes financial
and insurance companies because they are subject to special regulations. The preparation and
disclosure of the statement of cash flows in accordance to IFRS became mandatory in the year 1997 in
Jordan, however, due to data availability, the study period started with the year 2000. Data required to
calculate all study variables as well as control variables must be available for two consecutive years at
least in order to include the company in the analysis. Applying the aforementioned criteria resulted in
excluding only (1) company. The total number of observations varies according to the forecast horizon
and ranges between (923-746) company-year observations for one- and three-year-ahead forecast
horizons respectively.
3.3. Descriptive Statistics and Correlation Analysis
Table (1) reports descriptive statistics for the main variables of the study and two company
characteristics (Size and length of Operating Cycle). Table (3) reports Spearman correlation
coefficients between ROCF, ROA, and ROCFt+1.
The descriptive statistics, reported in table (1), are calculated for all observation with nonmissing information on current operating cash flows, current earnings, total assets, and length of
operating cycle (Panel A). Descriptive statistics are also calculated for two size groups (small and
large) (Panels B&C) and two operating cycle groups (Short and long) (Panels D&E).
Panel A's statistics show that the mean and median values of ROCF (0.057 and 0.056
respectively) are higher than those of ROA (0.029 and 0.032 respectively) during the study period,
which is an indication of accrual-related adjustments that decrease earnings but do not decrease
operating cash flows; such as depreciation expense. Furthermore, the mean values of ROCF and ROA
are positive and the standard deviation of ROA (0.077) is lower than that of ROCF (0.095). These
results are consistent with prior research (e.g. Dechow et al., 1998).
Panels B and C of table (1) show that the mean ROCF value of large companies (0.074) is
higher than that of small companies (0.043) and the mean ROA value of large companies (0.021) is
lower than that of small companies (0.031). This result indicates that large companies have larger
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European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
accrual-related adjustments compared with small companies. The standard deviation for both ROCF
and ROA for small companies is higher than that of large companies indicating that ROCF and ROA of
small companies are less stable compared with large companies.
Finally, Panels D and E show that companies that have short-operating-cycle-companies are, on
average, more profitable that long-operating-cycle companies. The mean ROA and ROCF values for
short-operating-cycle-companies (0.085 and 0.031 respectively) are higher than those of longoperating-cycle-companies (0.027 and 0.024 respectively). It also worth noticing the big difference
between ROCF and ROA for short-operating-cycle-companies which can be explained by the fact that
large companies have, on average, shorter operating cycles compared with small companies.
Table 1:
Descriptive Statistics for Main Variables of the Study and Two Company Characteristics (Size and
Length of Operating Cycle)
Panel A
All Observations (N=723)
Mean
Median
Std. Deviation
Percentiles
25
75
-0.006
0.120
-0.007
0.071
70.34
306.50
7811456.00
39286127.00
ROCF
0.057
0.056
0.095
ROA
0.029
0.032
0.077
OC
269.85
161.42
540.789
Total Assets
54954473.77
16762069.00
1.113E8
Panel B
Small Companies (N=289)
ROCF
0.043
0.031
0.095
-0.023
0.107
ROA
0.031
0.032
0.074
-0.008
0.072
OC
299.65
210.37
485.844
94.53
341.68
Total Assets
6517724.34
6358334.00
3406329.06
3348818.00
9177794.00
Panel C
Large Companies (N=289)
ROCF
0.074
0.077
0.089
0.020
0.125
ROA
0.021
0.025
0.072
-0.007
0.060
OC
265.24
115.42
685.329
56.01
210.59
Total Assets
1.22E8
52864413.00
1.530E8
30782387.00
1.19E8
Panel D
Short Operating Cycle (N=289)
ROCF
0.085
0.077
0.090
0.020
0.146
ROA
0.031
0.034
0.079
-0.002
0.068
OC
59.43
57.14
31.880
34.06
86.30
Total Assets
84948117.37
22372135.00
1.457E8
10984180.00
65330431.00
Panel E
Long Operating Cycle (N=289)
ROCF
0.027
0.022
0.093
-0.033
0.093
ROA
0.024
0.025
0.076
-0.010
0.069
OC
535.01
341.48
781.579
255.86
489.50
Total Assets
24471411.16
12250995.00
4.520E7
5150529.50
22492526.50
The table reports descriptive statistics for the main variables of the study as well as two of the three firm characteristics;
company size and length of operating cycle over the period 2000-2009 after deleting outliers using Cook's D and percentile
1 and percentile 99. Number of observations used in calculating these statistics is less than that used in the regression
models since I required all variables and company characteristics to be available for each company-year to be included in
the descriptive statistics. ROCF is current operating cash flows divided by average of total assets. ROA is current earnings
divided by average of total assets. ROCFt+1 is predicted (one-year-ahead) operating cash flows divided by average of total
assets. OC is length of operating cycle. N is number of company-year observations. I used the top and bottom 40% of
observations in calculating descriptive statistics for small and large companies and short and long operating cycles.
Table (2) shows a significant and strong correlation between ROCF and ROCFt+1 (0.490) and a
weak and insignificant correlation between ROA and ROCFt+1 (0.055), which is a preliminary
indication of the superiority of current operating cash flows over current earnings in predicting future
operating cash flows. In addition the table shows a significant and weak correlation between ROCF
and ROA.
42
Table 2:
European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
Pearson Correlation Coefficients
ROCF
ROA
ROA
0.094*
ROCFt+1
0.490***
0.055
The table reports Pearson correlation coefficients between the main variables of the over the period 2000-2009 after
deleting outliers using Cook's D and percentile 1 and percentile 99. Number of observations used in calculating these
statistics is 731 company-year observations. ROCF is current operating cash flows divided by average of total assets. ROA
is current earnings divided by average of total assets. ROCFt+1 is predicted (one-year-ahead) operating cash flows divided
by average of total assets. OC is length of operating cycle. N is number of company-year observations.
*** Significant at the 0.0001 level.
* Significant at the 0.01 level.
4. Results
4.1. Pooled OLS Regressions
The operating cash flows (OCF) model and the earnings model, using pooled OLS and fixed effect
regressions10 have been used in order to answer the research questions. The results are reported in table
(3). Panel (A) reports the OLS results of the OCF model for one– to three-year-ahead forecast horizons
as well as the results of the Earnings model for one– to three-year-ahead forecast horizons. It is clear
that the predictive ability of current operating cash flows is stronger than that of current earnings for
future cash flows for all forecast horizons. The adjusted-R2 of the OCF model is higher, for every
forecast horizon, than that of the Earnings model. Overall, the predictive ability of both the OCF model
and the Earnings model declines as the forecast horizon increases. This result is consistent with prior
research conducted in different countries (e.g. Habib, 2010; Finger, 1994).
4.2. Fixed Effect Regressions
Panel (B) reports the fixed effect results of the OCF model for one– to three-year-ahead forecast
horizons as well as the results of the Earnings model for one– to three-year-ahead forecast horizons.
The Hausmen (1978) test shows that the study data requires the fixed effect rather than the random
effect regression. This means that the relation under examination varies across companies rather than
over time. The reason behind using another regression specification is to check whether the results are
affected by the type of regression used. The results reported under panel (B) confirm the superiority of
the OCF model over the Earnings model for one- and three-year-ahead forecast horizons.
Table 3:
α
β
Adj-R2
N
α
10
The Predictive Ability of Operating Cash Flows and Earnings for One- to Three-Year-Ahead
Forecast Horizons.
Panel A: OLS Regression Results
One-year ahead
Two-year-ahead
OCF
Earnings
OCF
Earnings
0.025
0.032
0.030
0.033
(7.757)***
(10.006)***
(8.582)***
(9.816)***
0.435
0.475
0.348
0.411
(14.539)***
(11.743)***
(11.164)***
(9.590)***
0.187
0.129
0.129
0.099
916
923
835
830
Panel B: Fixed Effect Regression Results
One-year ahead
Two-year-ahead
OCF
Earnings
OCF
Earnings
-
Three-year ahead
OCF
Earnings
0.031
0.036
(8.902)***
(10.171)***
0.345
0.365
(10.888)***
(8.161)***
0.136
0.080
746
755
Three-year ahead
OCF
Earnings
-
The Hausman (1978) test shows that the fixed effect rather that the random effect is more appropriate for the study data.
43
Table 3:
European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
The Predictive Ability of Operating Cash Flows and Earnings for One- to Three-Year-Ahead
Forecast Horizons. - continued
0.069
0.270
0.020
0.192
0.095
0.172
(1.873)*
(5.261)***
-0.539
(3.573)**
(2.488)**
(2.971)**
Adj-R2
0.315
0.293
0.284
0.296
0.282
0.243
916
923
835
830
746
755
N
The table reports pooled OLS and Fixed effect regression tests of the predictive ability of current operating cash flows and
current earnings for future operating cash flows, over the period 2000-2009 after deleting outliers defined using Cook's D
and the top and bottom 1% of observations.
The Operating Cash Flow (OCF) model is as follows:
ROCF it+1,t+3=αit +βit ROCFit +εit
The Earnings model is as follows:
ROCF it+1,t+3=αit +βit ROCFit +εit
ROCFit+1,…,t+3 is predicted operating cash flows divided by average of total assets for company i in year t+1 to year t+3.
ROCFit is current operating cash flows divided by average of total assets for company i in year t. ROAit is current earnings
divided by average of total assets for company i in year t. N is the number of company-year observations. t-values are
between parentheses.
*** Significant at the 0.0001 level.
** Significant at the 0.01 level
* Significant at the 0.1 level
β
4.3. Company Characteristics and the Predictive Ability of Operating Cash Flows and Earnings
for Future Operating Cash Flows
Prior research has documented that partitioning operating cash flows and earnings based on size, length
of operating cycle, and sign of operating cash flows may be useful in isolating some systematic and
significant inter-company differences which may be of value to the predictive ability of operating cash
flows. For example, Charitou et al. (2001) argued that unexpected earnings, and therefore unexpected
operating cash flows, are less valued for large companies compared with small companies since more
information is available about earnings and operating cash flows for large companies before they are
announced. Moreover, Hayn (1995) argued that small companies are more likely to report losses than
large companies and therefore small companies earnings are less persistent than large companies. This
stability in earnings of large companies is expected to increase the predictive ability of future operating
cash flows. Therefore, the author expects that the predictive ability of both the OCF model and the
Earnings model to be greater for large companies compared with small companies and to be greater for
companies reporting positive operating cash flows compared with those reporting negative operating
cash flows since negative earnings are transitory in nature.
Regarding the length of operating cycle, Dechow (1994) argued that the longer the operating
cycle of the company the poorer the predictive ability of both the OCF model and the Earnings model.
Al-Debi'e (2011) documented a significant and negative relationship between length of operating cycle
and profitability for industrial companies in Jordan; the longer the operating cycle the less profitable is
the company. Therefore, it is expected that the relative predictive ability of both models to be greater
for companies with short operating cycles compared with those with longer operating cycles.
To examine the effect of firm characteristics on the relative predictive ability of current
operating cash flows and earnings for future operating cash flows, the sample observations have been
partitioned over the study period according to companies' size, length of operating cycle, and sign of
current operating cash flows respectively. Then the top and bottom 40% of observations were used in
running both the OCF model and the Earnings model. For example, when using the size characteristic,
and after sorting the sample observations according to the size of companies, both models for the
largest 40% of size observations and the smallest 40% of size observations have been run and so on so
forth for other firm characteristics. The results are reported in table (4).
Table (4) reports the adjusted R2's of pooled OLS regressions for both the OCF model and the
Earnings model. The results confirm that the predictive ability of current operating cash flows and
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European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
earnings for one- to three-year-ahead operating cash flows of both models is greater for large
companies compared with small companies, for companies that have short operating cycles compared
with those with long operating cycles and for companies reporting positive operating cash flows
compared with those reporting negative operating cash flows. Furthermore, the results show that the
predictive ability of the OCF model is better than that of the Earnings model for all forecast horizons,
however, this predictive ability declines as the forcast horizon increases. Interestingly, the results of the
current study are consistent with the results of prior research (e.g. Habib, 2010; Farshadfar et al.,
2008).
Table 4:
Firm Characteristics and the Predictive Ability of Operating Cash Flows and Earnings
Firm Size
One-year ahead
Two-year-ahead
Three-year ahead
Category
OCF
Earnings
OCF
Earnings
OCF
Earnings
Small
0.021
0.038
0.019
0.031
0.004
0.023
Large
0.393
0.241
0.298
0.223
0.288
0.183
N
364 and 368
370 and 367
336 and 335
333 and 340
296 and 303
301 and 302
Operating Cycle Length
One-year ahead
Two-year-ahead
Three-year ahead
Category
OCF
Earnings
OCF
Earnings
OCF
Earnings
Short
0.362
0.182
0.272
0.178
0.187
0.099
Long
0.049
0.059
0.045
0.063
0.065
0.053
N
335 and 336
331 and 341
299 and 307
299 and 305
271 and 277
271 and 284
Operating Cash Flows Sign
One-year ahead
Two-year-ahead
Three-year ahead
Category
OCF
Earnings
OCF
Earnings
OCF
Earnings
Negative
-0.001
0.015
0.020
0.000
0.024
0.041
Positive
0.184
0.119
0.157
0.121
0.086
0.67
N
297 and 628
305 and 623
259 and 571
268 and 568
231 and 518
235 and 518
The table reports adjusted-R2's of pooled regression models over the period 2000-2009 after deleting outliers defined using
Cook's D and the top and bottom 1% of observations. Sample observations were sorted according to firm size measured by
total assets, operating cycle length measured as the sum of receivables conversion period and inventories conversion period,
and sign of operating cash flows, respectively. The pooled regression models were ran for the top and bottom 40% of
observations.
The OCF model is as follows:
ROCF it+1,t+3=αit +βit ROCFit +εit
The Earnings model is as follows:
ROCF it+1,t+3=γ1it + γ2it ROAit +εit
ROCFit+1, t+3 is predicted operating cash flows divided by average of total assets for company i in year t+1or year t+3.
ROCFit is current operating cash flows divided by average of total assets for company i in year t. ROAit is current earnings
divided by average of total assets for company i in year t.
Conclusions
The study aimed at examining the relative predictive ability of both current operating cash flows and
current earnings for future operating cash flows. The study also examined the effect of company size,
length of operating cycle, and sign of current operating cash flows on the predictive ability of current
operating cash flows and current earnings for future cash flows. As expected, the OCF model is
superior to the earnings model in predicting one- through three-year-ahead operating cash flows. Both
the OCF model and the Earnings model have higher explanatory powers for large companies, shortoperating-cycle companies, and companies that report positive operating cash flows, however, the OCF
model still has higher explanatory power than that of the Earnings model after taking into
consideration company characteristics. All results are consistent with prior research and raise questions
about the value relevance of earnings compared with operating cash flows.
45
European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011)
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