Abstract
We investigate the ability of past operating cash flows (Model 1) and past earnings (Model 2) to generate predictions of operating cash flows from 1990 to 2004. We employ actual cash flow numbers reported in accordance with Statement of Financial Accounting Standards (SFAS) No. 95 in our primary analysis rather than using an algorithm to approximate operating cash flows (i.e., Kim and Kross J Acc Res 43:753–780, 2005; Dechow et al. J Acc Econ 25:133–168, 1998, among others). We derive out of sample predictions of operating cash flows both cross-sectionally similar to the approach of Kim and Kross (2005) and on a firm-specific time-series basis consistent with Dechow et al. (1998). Our predictive findings suggest: (1) cash-flow based models (Model 1) provide significantly more accurate predictions of operating cash flows than earnings-based models (Model 2); (2) time-series models significantly outperform cross-sectional models; (3) larger firms exhibit significantly more accurate cash-flow predictions than smaller firms; (4) firms with relatively shorter operating cycles exhibit significantly more accurate cash-flow predictions that firms with longer operating cycles consistent with Dechow (J Acc Econ 18:3–42, 1994); (5) we find no evidence of increased predictive power for either the cash-based or earnings-based prediction models across 1990–2004; (6) we also provide supplementary analyses to assess the impact on predictive performance when descriptive goodness-of-fit criteria are used instead of out-of-sample forecasts to assess predictive performance, and (7) we re-estimate the CFO prediction models using algorithmic CFO data instead of actual data.
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Notes
These include change in accounts receivable, change in accounts payable, change in inventory, amortization, depreciation, and other accruals.
Estimation of the same model cross-sectionally in 2003 for 1,174 sample firms reveals a b1 parameter value of .830.
Similar to Kim and Kross (2005), we also estimate a combined model that uses past values of CFO and net earnings jointly. Since the predictive performance of the combined model was inferior to Model 1 based on cash flows, we have suppressed providing its MAPEs in subsequent tables.
Pooled Theil’s U statistics for Model 1 were .414 versus .627 for Model 2. The former were significantly smaller (p = .001) than the latter consistent with the MAPE findings.
Specifically, \( {\text{Operating}}\,{\text{cycle}} = \frac{{{({\text{AR}}_{{\text{t}}} + {\text{AR}}_{{{\text{t}} - 1}} )} \mathord{\left/ {\vphantom {{({\text{AR}}_{{\text{t}}} + {\text{AR}}_{{{\text{t}} - 1}} )} 2}} \right. \kern-\nulldelimiterspace} 2}} {{{{\text{Sales}}} \mathord{\left/ {\vphantom {{{\text{Sales}}} {360}}} \right. \kern-\nulldelimiterspace} {360}}} + \frac{{{{\text{(Inv}}_{{\text{t}}} + {\text{Inv}}_{{{\text{t}} - 1}} {\text{)}}} \mathord{\left/ {\vphantom {{{\text{(Inv}}_{{\text{t}}} + {\text{Inv}}_{{{\text{t}} - 1}} {\text{)}}} 2}} \right. \kern-\nulldelimiterspace} 2}} {{{{\text{COGS}}} \mathord{\left/ {\vphantom {{{\text{COGS}}} {360}}} \right. \kern-\nulldelimiterspace} {360}}} \)
When we conduct similar analyses using the reduced subsample of firms with estimated CFO data, pooled adjusted r2 were significantly greater (p = .001) for Model 1 (0.65) versus Model 2 (0.59) when the models were estimated cross-sectionally. However, pooled adjusted r2 were greater for Model 2 (0.15) versus Model 1 (0.11) when the models were estimated on a time-series basis. While this finding is inconsistent with our out-of-period predictive results, it is consistent with the descriptive-predictive paradox discussed by Watts and Leftwich (1977) where in-sample goodness-of-fit results may not translate into out-of-period predictive performance.
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Lorek, K.S., Willinger, G.L. New evidence pertaining to the prediction of operating cash flows. Rev Quant Finan Acc 32, 1–15 (2009). https://doi.org/10.1007/s11156-007-0076-1
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DOI: https://doi.org/10.1007/s11156-007-0076-1
Keywords
- Predictive ability
- Operating cash flows
- Cross-sectional prediction models
- Time-series
- Prediction models
- Operating cycles