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Do core and non-core cash flows from operations persist differentially in predicting future cash flows?

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Abstract

This study investigates the persistence of cash flow components (core and non-core cash flows) using a cash flow prediction model. By extending the Barth, Cram, and Nelson (Account Rev 76(January):27–58, 2001) model, we examine the role of cash flow components in predicting future cash flows beyond that of accrual components. We propose a cash flow prediction model that decomposes cash flows from operations into core and non-core cash flow components that parallel the presentation and format of operating income from the income statement. Consistent with the AICPA and financial analysts’ recommendations, and as predicted, we find that core and non-core cash flows defined in our paper are differentially persistent in predicting future cash flows; and these cash flow components enhance the in-sample predictive ability of cash flow prediction models. We also analyze the association of in-sample prediction errors with earnings, cash flow and accruals variability. We find that disaggregating cash flows improve in-sample prediction, especially for large firms with high cash flows and earnings variability.

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Notes

  1. For brevity, we use cash flows to mean cash flows from operations.

  2. In general, operating activities are considered as core business activities while financing activities are non-core business activities. In this paper, we define items that are related to operating activities such as sales, cost of goods sold and operating expenses as the core activities. Interest, taxes and non-recurring operating activities are considered as non-core. Strictly speaking, taxes paid for operating and financing activities should be categorized as core or non-core, respectively. However, reporting under current GAAP does not provide us with a clear distinction between the core and non-core cash flows. We allow the data to speak for itself.

  3. Most agree that sales, cost of goods sold and operating expenses are ‘core’ items and interest and taxes are typically categorized as non-core items. However, Fairfield et al. (1996) include interest in their definition of operating income in predicting next period’s ROE. Additionally, taxes should be affected by both core and non-core activities unless it is allocated between operating and non-operating items. Therefore, we chose to separate interest and taxes to examine their persistence.

  4. Accruals are measured as changes in balance sheet items. Changes in working capital items such as accounts receivable, inventory, accounts payable reported in cash flow statement are provided by Compustat. We use these accruals reported in the cash flows statement.

  5. For example, Cohen (2004) uses the BCN cash flow prediction model because it has the highest predictive ability compared to other models. In Cohen’s study, the use of the residual from the cash flow prediction model is used to proxy for the quality of financial reporting.

  6. The ability of current period earnings and current period cash flows to predict future cash flows is approximately equivalent for longer horizons (Finger 1994).

  7. Note that the core and non-core definitions for cash flows are constrained by our data availability and are different from the core and non-core definitions for earnings.

  8. Consistent with prior research (e.g., DKW and BCN), we examine the in-sample predictability of various cash flow prediction models by using goodness of fit tests across models: models with aggregated cash flows versus models with disaggregated cash flows (i.e., cash flow components).

  9. To keep our model expression simple, we use β indicating the coefficient and μ the error term for every variable and model. Consistent with prior research, our use of realized future cash flows as a proxy for future cash flows assumes rational expectations (McNichols and Wilson 1988; Penman and Sougiannis 1998; Aboody et al. 1999; Barth et al. 2001). All variables are scaled by average total assets.

  10. Compustat reports negative accruals from the cash flow statements, hence #302 represents decreases in accounts receivable. A similar reporting pattern is applied to other working capital accruals.

  11. Compustat item #303 reports changes in inventory and item #304 reports changes in accounts payable and accrued expenses from the cash flow statement. Accrued expense is related to operating expense, and we focus on accounts payable to measure cash flows related to cost of goods sold. As described previously, we measure accruals directly from the statement of cash flows to eliminate possible measurement errors that arise from measuring accruals from the balance sheet (Hribar and Collins 2002).

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

  13. The Net Operating Working Capital (NOWC) includes operating current assets such as accounts receivable and inventory minus operating current liabilities such as accounts payable, interest payable, and tax payable. NOWC is calculated as current assets (#4) minus cash and short-term investments (#1) minus [current liabilities (#5) minus debt in current liabilities (#34)]. To get C_OE, we use changes in NOWC minus changes in accounts receivable, inventory and accounts payable as provided by the cash flow statement.

  14. Compustat item #16 is income taxes, #71 is income taxes payable and #305 reports changes in taxes payable from the cash flow statement. We also calculate taxes paid as #16 minus accrued taxes payable (measured as change in #71 or #305). The resulted sample availability and results are similar.

  15. As described above, we use reported accruals in the cash flow statement such as accruals related to changes in accounts payable, inventory and accounts payable. For accruals that are not provided in cash flow statement such as those related to operating expenses and other, we use comparative balance sheet data.

  16. Compustat discontinued providing information on data item #103 in 1999. So, when #103 is not available, we use item #14 (depreciation and amortization) and subtracts amortization (#65) to derive depreciation.

  17. Note that the magnitude of our ACC is higher than BCN’s. BCN report their statistics using only two digits, therefore, the difference between our numbers and theirs may be affected by rounding errors. For example, we report ACC as −0.047 and they report −0.04. It is likely that our number has a larger magnitude than theirs since rounding up our number would lead to −0.05.

  18. Tolerance (TOL) is 1 minus the R 2 that results from the regression of the other variables in the model on that regressor. VIF for each independent variable is calculated as 1 over TOL (see Wooldridge 2000; Hair et al. 1998).

  19. For a pooled analysis, C_Sales has a VIF greater than 60 and C_COGS has a VIF greater than 40.

  20. To avoid the problem of cross-sectional dependence, we examine the mean coefficients from the annual regressions using Fama-MacBeth statistics that are equal to the mean of the estimated coefficients across 17 yearly regressions divided by the standard error of the coefficients (Fama and MacBeth 1973). Because the Fama-Macbeth statistics are based on the coefficients from the annual regressions, they are unaffected by the potentially inflated t-statistics in the annual regressions. We apply analysis of the difference in coefficients similar to DeFond and Hung (2003). Peterson (2006) suggests that there are limitations using Fama-MacBeth statistics when there are too few clusters in the sample. He suggests when both a firm and a time effect are present in the data, researchers can address one parametrically (e.g., by including time dummies) and then estimate standard errors clustered on the other dimension. Alternatively, researchers can cluster on multiple dimensions. We do not address his concern specifically, however, we do conduct pooled regression with control for year dummies and our conclusions remain the same.

  21. BSN reports 24% for the model with CFO only. This may be due to sample differences. When we analyze our sample focusing on the observations prior to 1997, we get an adjusted R-square, similar to BCN.

  22. In contrasting model performance, we compare adjusted R-squares from yearly regression between models (reported in Table 5). We also conduct Voung tests (Vuong 1989). Results from the Voung tests are similar to what we conclude based on mean analysis of the adjusted R-squared.

  23. Coefficient on ΔAP has a negative sign as reported by BCN. To make the comparison between coefficients in our study simpler, we use −ΔAP in our regression model. Hence, our positive coefficient on −ΔAP is consistent with their negative coefficient on ΔAP.

  24. We also conducted Voung tests for pooled regression and the model performance is significantly different between these models.

  25. Using ranks has its advantage. It controls for outliers without losing observations, the magnitude of the coefficients of the variables in the same model can be readily compared since the distribution of the variables are the same. However, if the model calls for strictly original measure, then the rank model will be biased. We also conduct the analysis based on the original variables and we see some weaker results for the relation between earnings variability and the dependent variables, however, our conclusions are in generally not altered (see Cheng et al. 1996).

  26. The studies to date on deferred taxes provide evidence that the deferred tax component of earnings has some incremental information content (see, for example Chaney and Jeter 1994).

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Acknowledgements

The authors gratefully acknowledge the comments of Cathy Zishan Liu, Mary Geddie, Kathleen Harris, K. (Shiva) Sivaramakrishnan, Scott Whisenant, Carol Shaokun Yu, and workshop participants at the University of Houston. We would especially like to thank our discussant Douglas Hanna and participants at the 2005 JAAF Conference, The Joint 14th Annual PBFEA and 2006 Annual FeAT Conference, and the 2004 American Accounting Association Conference as well as anonymous reviewers for their contributions.

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Authors and Affiliations

  1. E. J. Ourso College of Business, Louisiana State University, 3109B CEBA, Baton Rouge, LA, 70803, USA

    C. S. Agnes Cheng

  2. Leeds School of Business, University of Colorado at Boulder, 419 UCB, Boulder, CO, 80309-0419, USA

    Dana Hollie

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  1. C. S. Agnes Cheng
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  2. Dana Hollie
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Correspondence to Dana Hollie.

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Data used in this study are available from public sources identified in the paper.

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Cheng, C.S.A., Hollie, D. Do core and non-core cash flows from operations persist differentially in predicting future cash flows?. Rev Quant Finan Acc 31, 29–53 (2008). https://doi.org/10.1007/s11156-007-0062-7

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