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Review of Accounting Studies

Erschienen in:

01.12.2010

The usefulness of accounting estimates for predicting cash flows and earnings

verfasst von: Baruch Lev, Siyi Li, Theodore Sougiannis

Erschienen in: Review of Accounting Studies | Ausgabe 4/2010

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Abstract

Accounting estimates and projections potentially improve the relevance of financial information by providing managers a venue to convey to investors forward-looking, inside information. The quality of financial information is, however, compromised by the increasing difficulty of making reliable estimates and forecasts and the frequent managerial misuse of estimates. Given the ever-increasing prevalence of estimates in accounting data, particularly due to the move to fair value accounting, whether these opposing forces result in an improvement in the quality of financial information is among the most fundamental issues in accounting. We examine the contribution of accounting estimates embedded in accruals to the quality of financial information, as reflected by their usefulness in the prediction of enterprise cash flows and earnings. Our out-of-sample prediction tests indicate that accounting estimates beyond those in working capital items (excluding inventory) do not improve the prediction of cash flows. Estimates do, however, improve the prediction of next year’s earnings, though not of subsequent years’ earnings. We conclude that the usefulness of accounting estimates to investors is limited and provide suggestions for improving the usefulness of estimates.

Vorheriger Artikel The new Form 8-K disclosures

Nächster Artikel The use of advertising activities to meet earnings benchmarks: evidence from monthly data

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Indeed, Aboody and Lev (1998) document a positive association between capitalized software development costs and future earnings.

Consider, for example, the 2001 pension footnotes of three financial institutions, Merrill Lynch, Bank of New York, and Charles Schwab, which report the following estimates of the expected returns on pension assets: 6.60, 10.50, and 9.00%, respectively (Zion 2002). The wide range of estimates (6.6–10.5%) of the long term performance of capital markets reflects the inherently large uncertainty (unreliability) of the pension expense estimate.

There are, of course, other uses of financial data, such as in contracting arrangements, which are not aimed at predicting future enterprise performance.

For example, General Electric reports in its revenue recognition footnote that various components of revenues derived from long-term projects are based on the estimated profitability of these projects. GE, however, does not break down total revenues into estimates and “facts.”.

Bowen et al. (1986) and Greenberg et al. (1986) perform similar regression-based, in-sample predictions.

Studies such as Bathke et al. (1989) and Lorek et al. (1993) also perform out-of-sample prediction tests.

Examples of studies in related areas including economic significance tests are Ou and Penman (1989), Stober (1992), Abarbanell and Bushee (1998), and Piotroski (2000).

The accounts receivable change, net of the provision, is an exception, since it is subject to an estimate. But this estimate is included in our second accruals component, EST.

We measure CFO as in Barth et al. (2001), namely net cash flow from operating activities, adjusted for the accrual portion of extraordinary items and discontinued operations.

For robustness, we reran our predictions (reported in Table 2) without capital expenditures, and conclude that none of our inferences changes in the absence of capital expenditures.

Valid statement of cash flows data for the year 1987 are available for a relatively small number of firms not enough to do a meaningful industry-by-industry analysis. Thus, we do not use 1987 data.

We repeated all of our analyses with a sample without any outlier removal, namely where we only require non missing values for the key variables and at least 600 observations in each two-digit SIC over the sample period 1988 through 2004. This sample consists of 65,178 observations and is substantially larger than the sample of 41,124 observations used in the analysis reported below. We find that for many industries the R-squares in the in-sample regressions are higher for the un-truncated data than for the truncated data. The forecast error results are essentially identical to the results from the truncated sample in terms of inferences, but the errors are larger. The portfolio abnormal returns results exhibit similar patterns to the results from truncated data. Overall, the un-truncated data yield very similar results to those of the truncated data reported below.

The reported Theil's U-statistic is the average of the yearly U-statistics. Theil’s U is defined as the square root of ∑(actual-forecast)²/∑(actual)². The U statistic can range from zero to one, with zero implying a perfect forecast. Thus, models generating better predictions should have lower U statistics.

All the absolute forecast errors (MAER) in Table 2 are statistically significant, with p-values of 0.01 or better. The majority of the signed errors (MER) is also significant at p-values of 0.01 or better, and many of the errors are statistically significant at least at p-values of 0.05. The following signed errors are insignificant: Model 1 in forecasting Years 1–2 CFO, Models 1 and 3 in forecasting Years 1–3 CFO, and Models 2, 4 and 5 in forecasting Years 1-3 OI.

Kim and Kross (2005) use balance sheet items to calculate cash from operations, while we use statement of cash flows data. We were able to replicate the out-of-sample prediction results of Kim and Kross using balance sheet items for our sample period. Accordingly, the difference in the results between the two studies is due to the data used. As shown by Collins and Hribar (2002), the cash from operations and accruals derivation from the statement of cash flows are preferable.

Note that despite the very small difference between the MAERs of Models 1 and 3, the mean differences are statistically significant at the 0.05 level or better (see asterisks).

These inferences do not change when we examine median signed and absolute prediction errors (available on request).

The median absolute errors are lower for Model 2 than for Model 1 in all NI and OI panels except in the bottom two panels (for the aggregate next two and three years horizons).

The exception: for the 25% of the sample firms with high accruals, the mean absolute errors of model 5 (CFO plus disaggregated accruals) are significantly lower than those of model 1, for both CFO and FCF in year 1.

For example, at the end of April 2000 all firms whose most recent fiscal year ended no later than December 1999 are ranked by predicted cash flows or earnings and assigned into portfolios.

Most of the return outliers are from the years 1999 and 2003, where the subsequent returns reflect significant market reversals: the burst of the tech bubble in 2000 and the economy’s emergence from recession in 2004. Extrapolation predictions like ours perform poorly in sharp reversal years.

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Titel: The usefulness of accounting estimates for predicting cash flows and earnings
verfasst von: Baruch Lev
Siyi Li
Theodore Sougiannis
Publikationsdatum: 01.12.2010
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 4/2010
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-009-9107-6

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