Are Operating Cash Flows a Superior Predictor of Future Operating ...

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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 ...
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

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The objectives of financial reporting, FASB (2008).

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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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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

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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).

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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.

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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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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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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.

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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.

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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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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.

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References 1] 2]

3] 4] 5] 6] 7] 8] 9] 10]

11] 12]

13] 14] 15]

16]

17] 18] 19] 20] 21] 22]

Al-Akra M., M. J. Ali, and O. Marashdeh (2009), "Development of Accounting Regulations in Jordan", The International Journal of Accounting 40, pp. 163-186. Al-Debi'e, M. M. (2011), "Working Capital Management and Profitability: The Case of Industrial Firms in Jordan", European Journal of Economics, Finance and Administrative Sciences, 63, pp.75-86. Amir, E. and B. Lev (1996), "Value-Relevance of Nonfinancial Information: The Wireless Communications Industry", Journal of Accounting and Economics, 22, pp.3-30. ASE (2007), Amman Stock Exchange, http://www.ammanstockex.com.jo. Arthur, N., Cheng, M. and Czernkowski, R. (2010). "Cash flow disaggregation and the prediction of future earnings". Accounting & Finance, 50, pp. 1–30. Barth, M. E., (2000). Valuation-based research implications for financial reporting and opportunities for future research. Accounting and Finance, 40, pp. 7-31. Barth, M. E., D. P. Cram, and K. K. Nelson (2001) "Accruals and the Prediction of Future Cash Flows", Accounting Review 76, pp. 27-58. Berger, P., E. Ofek, and I. Swary (1996), "Investor valuation of the abandonment option", Journal of Financial Economics, 42, pp. 257–287. Beaver, W. H. (1998), Financial reporting: an accounting revolution. 3rd Edition, Prentice-Hall, Engelwood Cliffs, NJ. Charitou, A., C. Clubb and A. Andreou (2001), "The Effect of Earnings Permanence, Growth and Firm Size on the Usefulness of Cash Flows and Earning in Explaining Security Returns: Empirical Evidence for the UK", Journal of Business Finance & Accounting, 28, pp. 563-594. Clinch, G., Sidhu B. and Sing, S. (2002), "The Usefulness of direct and Indirect Cash flow Disclosures", Review of accounting studies, 7, pp.383-404. Collins, D. W., E. L. Maydew, I. S. Weiss (1997), "Changes in the Value-relevance of Earnings and Book Values Over the Past Forty Years", Journal of Accounting and Economics, 24, pp. 39-67. Dechow, P.M. (1994), "Accounting Earning and Cash Flows as Measures of Firm Performance", Journal of Accounting and Economics, 18, pp. 3-42. Dechow, P.M., S. P. Kothari, and R. L. Watts, (1998), "The Relation between Earnings and Cash Flow", Journal of Accounting and Economics, 25, pp.133-68. Farchadfar, S., C. Ng, and M. Brimble (2009), "The Relation Ability of Earnings and Cash Flow Date in Forecasting Future Cash flows: Some Australian Evidence", Pacific Accounting Review, 20, pp. 254-68. Financial Accounting Standards Board (FASB) (1978), Statement of Financial Accounting Concepts 1: Objectives of Financial Reporting by Business Enterprises, Financial Accounting Standards Board, Stamford, CT. Finger, C. A. (1994) "The Ability of Earnings to Predict Future Earnings and Cash Flow", Journal of Accounting Research 32, pp. 210-223. Francis J. and K. Schipper (1999), "Have Financial Statements Lost Their Relevance", Journal of Accounting Research, 37, pp. 319-52. Habib, A. (2010), "Prediction of Operating Cash Flows: Further Evidence from Australia", Australian Accounting Review, 20, pp. 134–143. Hausman, J. A. (1978), "Specification Tests in Econometrics," Econometrica, 46(6), 1251– 1271. Hayn, C. (1995), "The Information Content Losses", Journal of Accounting and Economics, 20, pp. 125-53. International GAAP (2008), Generally Accepted Accounting Practice Under International Financial Reporting Standard, John Wiley & Sons, Ltd.

46 23]

24]

25]

26] 27] 28] 29]

European Journal of Economics, Finance and Administrative Sciences – Issue 40 (2011) Jabr, J. Z. and M. M. Al-Debi'e (2008), "The Effect of the Sign of Accounting Earnings and Operating Cash Flows on Their Information Content", Jordan Journal of Business Administration, 4, pp. 1-23. Kim, M. and W. Kross, (2005) "The Ability of Earnings to Predict Future Operating Cash Flows Has Been Increasing—Not Decreasing", Journal of Accounting Research, 43, pp. 753780. Lev, B. (1989), "On the Usefulness of Earnings and Earnings Research: Lessons and Directions from Two decades of Empirical Research", Journal of Accounting Research, Supplement, pp.153-92. Lev, B. (1997), The boundaries of financial reporting and how to extend them, Working Paper, New York University, New York, NY. Lev, B., S. Li and T. Sougiannis (2010), "The Usefulness of Accounting Estimates for Predicting Cash Flows and Earnings" Review of Accounting Studies, 15, pp. 779-807 Percy, M. and D. J. Stokes (1992), "Further Evidence on Empirical Relationships between Earnings and Cash Flows", Accounting and Finance, 32, pp.27-49. Theil, H. (1966), Applied Economic Forecasting, North- Holland, Amsterdam.