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Are analysts’ earnings forecasts more accurate when accompanied by cash flow forecasts?

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Abstract

We examine whether analysts’ earnings forecasts are more accurate when they also issue cash flow forecasts. We find that (i) analysts’ earnings forecasts issued together with cash flow forecasts are more accurate than those not accompanied by cash flow forecasts, and (ii) analysts’ earnings forecasts reflect a better understanding of the implications of current earnings for future earnings when they are accompanied by cash flow forecasts. These results are consistent with analysts adopting a more structured and disciplined approach to forecasting earnings when they also issue cash flow forecasts. Finally, we find that more accurate cash flow forecasts decrease the likelihood of analysts being fired, suggesting that cash flow forecast accuracy is relevant to analysts’ career outcomes.

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Notes

  1. Hereafter, we use operating cash flows and cash flows interchangeably to refer to cash flows from operations.

  2. Loh and Mian (2006) provide evidence consistent with individual analysts using their earnings forecasts to produce stock recommendations, with more accurate forecasters providing more profitable recommendations.

  3. See Schipper (1991), Brown (1993) and Ramnath et al. (2008) for literature reviews on research related to analyst’ forecasts and stock recommendations.

  4. In a recent working paper, Givoly et al. (2008) examine the properties of analysts’ cash flow forecasts and raise concerns about this assumption. They find that in a regression of cash flow forecasts on earnings forecasts, depreciation expense, working capital accruals, and other accrual adjustments, the coefficients on the earnings forecasts and depreciation expense approach 1.0, whereas the coefficients on working capital accruals and other accrual adjustments, although positive, are very small (0.06). They thus conclude that analysts’ cash flow forecasts appear to be simple, mechanical adjustments to their own earnings forecasts. This is not consistent with both DeFond and Hung’s (2003) conclusion and our own readings of analyst research reports.

  5. As discussed in Lundholm and Sloan (2007), forecasting a full set of financial statements allows analysts to fully consider the firms’ interacted set of operating, investing, and financing activities. For example, the forecast of interest expense on the income statement is dependent of the amount of debt forecasted on the balance sheet. The amount of forecasted debt is dependent on the forecast of the firm’s net operating assets and capital structure. In turn, the forecast of net operating assets depends on the forecast of sales growth. This structured approach to forecasting imposes discipline on the earnings forecasts and facilitates analysts’ understanding of the firms’ business and earnings processes.

  6. The accounting literature indicates that the cash flow and accrual components of earnings exhibit different time-series properties (Sloan 1996; Xie 2001; Call et al. 2008).

  7. DeFond and Hung (2003) find that analysts are more likely to issue cash flow forecasts for firms with low quality earnings (e.g., higher earnings volatility and greater heterogeneity of accounting choice). Assuming analysts place more emphasis on forecasting cash flows than on forecasting earnings, this could result in a negative association between the presence of cash flow forecasts and earnings forecast accuracy, inconsistent with our prediction. However, there is no empirical evidence that supports the above assumption. Moreover, in their long-run stock returns analysis, DeFond and Hung (2003) find that investors place greater weight on earnings compared to cash flows for all firms (i.e., regardless of the presence of analysts’ cash flow forecasts), suggesting that earnings information continues to be of primary importance even for firms with cash flow forecasts.

  8. Elgers et al. (2003) and Bradshaw et al. (2001) find evidence consistent with analysts’ overreaction to the implication of current accruals for future earnings. However, in their analyses they examine analysts’ reaction to accrual information without controlling for cash flows. In contrast, Ahmed et al. (2006) and Yu (2007) jointly consider the implications of accruals and cash flows for future earnings and find analysts underreact to the implications of both accruals and cash flows for future earnings.

  9. The only exception to this sampling requirement is our test using analyst-specific regressions: since we examine the difference in earnings forecast accuracy for the same analyst who gives cash flow forecasts for some firms but not others, we include firms that have no cash flow forecasts in our sample. Our results are robust to restricting this sample to only those firm-years with at least one cash flow forecast. Please see Sect. 4.2 for more details.

  10. Our sample differs from the sample employed in Pae et al. (2007) in that we do not restrict our sample of I/B/E/S analyst-firm observations to firms that are also on Compustat. Restricting the sample to firms on Compustat results in a much smaller sample of primarily larger firms, impacting both the inference and generalizability of the research findings.

  11. We identify analysts who simultaneously issue cash flow forecasts and earnings forecasts and analysts who only issue earnings forecasts from the I/B/E/S database. Our communication with I/B/E/S reveals that I/B/E/S does not impose any restrictions, except for standard quality assurances, on the types of analyst forecasts reported in its database (e.g., revenue, cash flow and earnings forecasts). That is, I/B/E/S will make available in its database all forms of forecasts provided by the analysts. Hence, we rely on I/B/E/S when determining whether an analyst issued a cash flow forecast for a particular firm. To the extent that analysts who only issue earnings forecasts to I/B/E/S also privately forecast cash flows, this will bias against finding results consistent with our predictions.

  12. Unlike Pae et al. (2007), who use range-adjusted forecast errors, we use mean-adjusted forecast errors for our relative earnings forecast accuracy measure. Prior studies that use range-adjusted forecast errors (e.g., Clement and Tse 2003, 2005; Brown and Hugon 2007) mainly do so to compare regression coefficients across two models—one that models earnings forecast accuracy and one that models market reactions to analysts’ forecasts. Therefore, we mean-adjust because we do not model market reactions and because it facilitates comparison with other prior studies that also use mean-adjusted forecast errors (e.g., Clement 1999; Jacob et al. 1999; Chen and Matsumoto 2006).

  13. Unlike Pae et al. (2007), who use the Heckman (1979) two-stage self-selection model to control for the endogeneity of analysts’ decision to issue cash flow forecasts, we include analyst-firm and analyst specific variables that have been identified in prior research to be associated with the issuance of cash flow forecasts directly into Eq. 2. We do this for two reasons. First, the same variables (e.g., lagged earnings forecasts accuracy, forecast frequency, and brokerage size) that affect analysts’ decision to issue cash flows forecasts also affect earnings forecast accuracy. Therefore, it is difficult to impose the exclusion restriction in the Heckman (1979) two-stage approach which requires some variables to be included in the choice model (i.e., cash flows forecasts issuance model) that do not appear in the treatment model (i.e., earnings forecast accuracy model). If the exclusion restriction is not imposed, the resulting estimates in the treatment model are likely to be inefficient, leading to overstated standard errors (Wooldridge 2006). Note that Pae et al. (2007) do not account for this exclusion restriction as all the variables that appear in their choice model also appear in their treatment model. Second, Francis and Lennox (2008) suggest that in the presence of inappropriate model specifications, Heckman selection models can provide inferences that are extremely fragile and have severe multicollinearity problems. They show that virtually any possible inference is achievable in the second stage estimation with minor changes to model specification in either or both the choice and treatment models. As such, we do not rely on the Heckman procedure but employ two alternative empirical specifications to examine the relation between the issuance of cash flows forecasts and earnings forecast accuracy to mitigate the concerns over endogeneity of analysts’ decision to issue cash flows forecast. These alternative specifications are discussed in Sects. 4.2 and 4.3.

  14. The results are unchanged when DTOP10 is mean-adjusted.

  15. One concern is that the issuance of cash flow forecasts simply identifies firms that analysts care more about and on which they expend more forecasting efforts. Clement et al. (2003) measure analyst effort using earnings forecast frequency, as analysts who work harder analyzing a firm generate more forecasts for that firm. Barth et al. (2001) use the number of firms followed by the analyst to proxy for analysts’ effort, as the fewer firms an analyst follows, the greater the effort spent on each firms. Our inclusion of MFREQ and MNCOS as control variables mitigates the concern of omitted correlated variables associated with analysts’ efforts.

  16. Note that we mean adjust forecast errors with respect to all analysts issuing earnings forecasts for firm j in year t. However, in our regression analysis some analysts drop out due to missing observations on required independent variables, especially LMAFE. For this reason, while the average mean-adjusted value for each variable is zero (by definition), it is not necessarily the case that the average mean-adjusted values reported in Panel B of Table 1 are zero. When we re-estimate our Eq. 2 using mean-adjustment with respect only to observations that have all the required data, the coefficient on CFF is still significantly negative and our inferences are unchanged.

  17. We also partition the sample based on (i) total assets, (ii) total sales, and (iii) market value of equity. We obtain similar results across all sample partitions.

  18. Recall that for each analyst, MAFE is measured as the absolute earnings forecast error relative to the mean absolute forecast error of all analysts following the same firm in the same year (regardless of whether the analysts issue cash flow forecasts). Hence, this 0.6% improvement in forecast accuracy is not a simple comparison of the forecast errors of earnings forecasts issued with cash flow forecasts and those issued in isolation (unlike our univariate comparison of differential forecast accuracy in Panels C and D of Table 1). Rather, this analysis suggests that relative to earnings forecasts issued in isolation, earnings forecasts accompanied by cash flow forecasts are 0.6% more accurate than the mean earnings forecasts issued for the firm. Therefore, this coefficient provides a lower bound estimate of the difference in earnings forecast accuracy between earnings forecasts issued with cash flow forecasts and those issued in isolation. While the MAFE measure may mute the economic significance of our reported findings, we believe its role in controlling for firm-year specific factors affecting earnings forecasting difficulty is of vital importance to the validity of our analyst level tests reported in Tables 2 through 5.

  19. We examine whether multicollineraity among variables might influence our regression results reported in Table 2. Kennedy (1992) suggests that a Variance Inflation Factor (VIF) greater than 10 is indicative of problematic collinearity. The VIFs are less than two for all variables in the regressions reported in Table 2. The absolute correlations between CFF and all independent variables do not exceed 0.15 while the absolute correlations among the other independent variables are all lower than 0.6. Hence, multicollinearity is unlikely to affect our results.

  20. Note that we estimate this regression separately for each analyst, so even though the variables are still measured at the analyst-firm-year level, analyst subscripts are unnecessary in this model.

  21. The above test is conducted on a sample in which some firms do not have cash flow forecasts. This might raise the question of whether we adequately control for firm-characteristics that affect the issuance of cash flow forecasts. We test the robustness of the results by further restricting the above sample to only firm-years with at least one cash flow forecast, resulting in 5,174 unique analyst regressions. Our results are robust to this design choice. Specifically, the coefficient (p-value) on CFF is 0.051 (<0.001). The other coefficients are very similar to those reported in Table 3.

  22. The results reported in Table 3 further distinguish this study from Ertimur et al. (2008), who hypothesize that analysts issue non-earnings (e.g., revenue) forecasts in order to signal their superior ability. If analysts issue cash flow forecasts to signal their innate forecasting ability, we would not expect to find that an individual analyst’s earnings forecast accuracy varies across firms depending on whether a cash flow forecast is issued for the firm because, in this analyst specific analysis, the analysts’ ability is held constant across all firms being covered. However, consistent with our hypothesis that analysts’ earnings forecast accuracy improves when the analysts forecast the full set of financial statements and issue cash flow forecasts, the coefficient on CFF is significantly positive. This result is unlikely to be explained by Ertimur et al. (2008) signaling hypothesis.

  23. In an untabulated analysis, we find that in the year when cash flow forecasts are initiated, cash flow forecast accuracy is significantly higher for persistent versus non-persistent cash flow forecasters. While we do not examine the determinants of persistent and non-persistent cash flow forecasters, this result suggests that one of the potential reasons why analysts stop forecasting cash flows for some firms after only one year is lower cash flow forecast accuracy.

  24. Since we find earnings forecast accuracy to be positively associated with the issuance of cash flow forecasts, and because cash flow is a component of earnings, we expect to observe empirically that cash flow forecast accuracy is positively associated with earnings forecast accuracy. We focus on a restricted sample consisting of only earnings forecasts issued with cash flow forecasts and replace the indicator variable for cash flow forecast issuance (CFF) in Eq. 2 with a measure of mean-adjusted cash flow forecast accuracy. In untabulated analysis, we find a positive coefficient on mean-adjusted cash flow forecast accuracy indicating that cash flow forecast accuracy is indeed positively associated with earnings forecast accuracy. This relation continues to hold even after we replace the contemporaneous cash flow forecast accuracy measure with lagged mean-adjusted cash flow forecast accuracy in an effort to mitigate any mechanical relation between current period cash flow and earnings forecast accuracy.

  25. Equation 6 is estimated at the firm-year level while Eqs. 79 are estimated at the analyst-firm-year level. When we estimate Eq. 6 at the analyst-firm-year level, we obtain very similar results.

  26. For Eqs. 69, Compustat data item 18, earnings before extraordinary items and discontinued operations, is used to measure actual earnings rather than actual earnings from I/B/E/S. We do this to be consistent with the use of Compustat data items to obtain accruals (ACC) and cash flows (CFO), which are not widely available on I/B/E/S. Compustat data item 18 is consistent with the description from I/B/E/S that analysts typically forecast earnings after discontinued operations, extraordinary charges, and other non-operating items. As a comparison, the mean (median) of Compustat data item 18 is 0.32 (0.47) while that for the I/B/E/S actual earnings is 0.30 (0.32). Furthermore, the correlation between Compustat data item 18 and I/B/E/S actual earnings is 0.72. We offer further robustness checks on this design choice and detail our investigation in Sect. 7.2.

  27. In Eq. 9, our focus is on whether analysts’ underreaction to both cash flows and accruals is mitigated when they issue cash flow forecasts compared to when they do not issue cash flows forecasts. We do not predict or examine whether the underreaction to accruals is mitigated to a greater or lesser extent than is the underreaction to cash flows.

  28. In untabulated analysis, we find the difference in the persistence of cash flows (CFF) and accruals (ACC) is significant at the 1% level.

  29. Raedy et al. (2006) propose a rational economic explanation for analysts’ underreaction to earnings information. They argue that analysts have an asymmetric loss function. Specifically, that analysts’ reputation suffers more (less) when subsequent information causes a revision in investor expectations in the opposite (same) direction as the analyst’s prior earnings forecast revisions. By underreacting, analysts can avoid a revision in an opposite direction and avoid the associated greater reputation damage. Raedy et al. (2006) also predict a positive relation between analysts’ underreaction to earnings information and the uncertainty regarding future earnings information. This suggests the underweighting of accrual and cash flow persistence decreases with the accuracy (as well as the presence) of cash flow forecasts. To examine this proposition, we isolate only the analysts who issued a cash flow forecast, and we replace the indicator variable for cash flow forecast issuance (CFF) in Eq. 9 with a measure of cash flow forecast accuracy. Consistent with the prediction of a positive relation between underreaction and information uncertainty, we find (in an untabulated analysis) the underweighting of cash flows and accrual is mitigated to a greater extent when analysts issue more accurate cash flow forecasts.

  30. We find approximately 15% of analysts on the I/B/E/S database are affiliated with at least two brokerage houses in any given year. The reason of this multiple affiliation is unclear. Mikhail et al. (1999) report the same phenomenon using Zack’s Investment Research database (see page 187 of their paper). Therefore, in our empirical analysis of career outcomes, we only include analysts with only one affiliation in year t. While this leads to a smaller sample size, the resulting definition of analysts being fired is clearer and more objective.

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Acknowledgements

We thank Russ Lundholm (Editor) and Phil Shane (Referee) for valuable insights. We have also benefitted from the discussion of Reuven Lehavy at the 2008 RAST Conference. We appreciate the helpful comments of Michael Clement, Pieter Elgers, Max Hewitt, Qianyun Huang, Ping-Sheng Koh, Bernadine Low, Ray Pfeiffer, Dave Piercey, Steve Rock, Richard Sloan, Siew Hong Teoh and workshop participants at the University of Colorado at Boulder, the University of Massachusetts Amherst, and participants at the 2008 AAA Annual meeting and 2008 RAST Conference. We also thank Julia Yu for research assistance.

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Call, A.C., Chen, S. & Tong, Y.H. Are analysts’ earnings forecasts more accurate when accompanied by cash flow forecasts?. Rev Account Stud 14, 358–391 (2009). https://doi.org/10.1007/s11142-009-9086-7

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