CAPÍTULO V: Intervención
5.8 Cronograma
The first step of the study analyses the predictive ability of current cash flow and accruals with respect to future firm performance as measured by cash flow and accounting earnings. Table 2 summarises the empirical findings from these regressions. In the total sample, cash flow seems to be a significant predictor24 of future cash flows. As for next year’s cash flow, both the level and the first difference of current cash flow turn out to be significant predictors. However, accruals seem to be unrelated to future cash flow. In fact, this conclusion holds with respect to next year’s cash flow as well as the mean of the next three cash flows. The mean of the next three cash flows appears to be easier to forecast than next year’s cash flow, as the explanatory power is 2.5 times higher in regression (1b) than in (1a)25. Overall, these results indicate that there is positive auto correlation for cash flows; a high cash flow in one year is
24
The presented t-values are computed using White-adjusted standard deviations. The White estimator for variance controls for possible heteroskedasticity in the regression analyses. Coefficients are termed “significant” if they are significant on a 5 % level, using two sided tests.
25
Note that the sample size differs between the regressions. As a robustness check, all regressions are re-run on an identical sample, compare section 6.1.
typically followed by a high cash flow the next year as well, a result consistent with prior research (Barth et al., 2001; Dechow et al., 1998). Even though a high cash flow in the normal case is followed by another high cash flow, a high increase in cash flow seems to have a negative impact on future cash flows, at least next year’s cash flow. This finding indicates that the cash flows to a certain extent mean revert. The indication that companies performing badly for some time tend to perform better in the future, and vice versa, is a phenomenon frequently discussed in capital market-based accounting research (see for instance Ball & Brown, 1968; Basu, 1997; Hayn, 1995; Sloan, 1996).
Table 2: Step 1 - Predictive Ability of Cash Flow and Accruals Total Sample
Dependent variable:
Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
CF 0.46 6.48 0.47 5.82 0.48 6.27 0.49 5.81 ∆CF -0.23 -3.03 -0.06 -0.71 -0.10 -1.70 0.05 0.68 ACC 0.08 0.93 -0.13 -1.08 0.48 5.23 0.41 4.45 ∆ACC -0.11 -1.42 0.18 1.79 -0.06 -0.89 0.10 1.17 Constant 0.09 10.15 0.08 11.39 0.01 1.48 0.00 -0.80 Adj. R2 0.12 0.29 0.16 0.16 n 1105 693 1105 693 Mean VIF 2.81 4.64 2.81 4.64 Positive Earnings Dependent variable:
Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
CF 0.80 5.96 0.66 5.67 0.64 5.81 0.48 4.76 ∆CF -0.57 -5.41 -0.21 -1.82 -0.32 -3.69 -0.12 -1.61 ACC 0.34 2.08 0.06 0.38 0.60 5.15 0.44 3.81 ∆ACC -0.34 -3.02 0.14 0.95 -0.24 -2.71 -0.10 -0.99 Constant 0.07 6.78 0.07 8.98 0.02 1.84 0.01 2.19 Adj. R2 0.20 0.36 0.16 0.14 n 776 524 776 524 Negative Earnings Dependent variable:
Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
CF 0.37 2.60 0.33 1.65 0.34 1.80 0.42 1.55 ∆CF -0.02 -0.34 -0.04 -0.38 0.04 0.66 0.14 1.10 ACC 0.01 0.09 -0.29 -1.87 0.37 3.04 0.25 1.38 ∆ACC 0.05 0.75 0.14 1.23 0.08 0.86 0.22 1.42 Constant 0.08 5.02 0.06 3.37 -0.03 -1.63 -0.05 -2.56 Adj. R2 0.05 0.16 0.09 0.04 n 327 167 327 167
CFt+1 meanCFt+1,2,3 EARNt+1 meanEARNt+1,2,3
CFt+1 meanCFt+1,2,3 EARNt+1 meanEARNt+1,2,3
CFt+1 meanCFt+1,2,3 EARNt+1 meanEARNt+1,2,3
Table description
Table 2 describes the predictive ability of earnings split into cash flow and accruals for a sample of Norwegian firms in the period 1992 to 2004. It summarises the regression coefficients (Coeff.), White-adjusted t-values (t- value), total explanatory power (adj. R2) and number of observations (n) for the total sample as well as for the
positive and negative earnings sub-samples. Mean variance inflation factor (VIF) is displayed for the total sample. Data are analysed using the following regression specifications:
(1a) CFi,t+1=β0+β1CFi,t+β2∆CFi,t+β3ACCi,t+β4∆ACCi,t+εi,t
(1b) meanCFi,t+1,2,3=β0+β1CFi,t+β2∆CFi,t+β3ACCi,t+β4∆ACCi,t+εi,twhere 3 CF CF CF meanCF i,t1 i,t 2 i,t3 3 , 2 , 1 t , i + + + + + + =
(1c) EARNi,t+1=β0+β1CFi,t+β2∆CFi,t+β3ACCi,t+β4∆ACCi,t+εi,t
(1d) meanEARNi,t+1,2,3=β0+β1CFi,t+β2∆CFi,t+β3ACCi,t+β4∆ACCi,t+εi,t where 3 EARN EARN EARN meanEARN i,t1 i,t 2 i,t 3 3 , 2 , 1 t , i + + + + + + =
where CFi,t is cash flow from operations for company i in year t, ACC is total accruals and EARN is earnings before extraordinary items. ∆ denotes yearly change in the variables. The accounting variables are scaled by the market value of equity at 30 December in year t-1. Coefficients marked in boldface denote a statistical significance at a 5 % level, two sided test.
Contrary to Barth et al. (2001) and Dechow et al. (1998), table 2 suggests that there is no significant relation between future cash flows and current accruals in the total sample. Neither accruals nor the change in accruals shows significant coefficients in the regressions. It does not matter whether the dependent variable is next year’s cash flow or the average of the next three cash flows. The explanatory power of the two specifications is 12 % and 29 %, respectively. When only cash flow and its first difference are used as explanatory variables, the adjusted R is respectively 12 % and 28 % (untabulated). These results contradict the 2
assertion that accruals make current earnings a better predictor of future cash flows than current cash flow. When the sample is split according to the sign of earnings, accruals are significantly related to next year’s cash flow for the positive earnings sample. Accruals remain unable to predict the mean of the next three cash flows. Consistent with prior research (for instance Hayn, 1995), today’s cash flow and accruals have a low association with future cash flow when earnings are negative. Actually, none of the regression coefficients are significant when the mean of the three next cash flows is analysed. Explanatory power is also
dramatically lower when negative earnings are considered than when positive earnings are investigated. Note that cash flow may be both positive and negative when the sample is split – the split is governed by the sign of the earnings.
Table 2 also displays the findings from regressions of future earnings on today’s cash flow and accruals. The results for the total sample clearly indicate that accruals are a relevant earnings predictor. In fact, both cash flow and accruals are significantly associated with future firm performance when cash flow is replaced by earnings as the dependent variable. Though cash flows are positively related to future earnings, large levels of accruals are typically associated with lower future earnings (note that total accruals typically are negative). Still, the change in accruals is statistically unrelated to future earnings. This is also the case for the change in cash flow. The explanatory power is the same in regression (1c) and (1d). In contrast to the cash flow regressions, it does not appear to be easier to forecast the mean of the next three earnings than next year’s earnings. This finding may be attributed to the higher variation in cash flow than in earnings (see table 1). Accruals contribute to levelling out earnings but not cash flow. Note that untabulated results show that the explanatory power of the earnings regressions falls dramatically if either cash flow or accruals are omitted as an explanatory variable. For instance, the adjusted R is only 2 % when next year’s earnings are 2 regressed on either cash flow or accruals.
As with cash flow predictions, earnings predictability for future earnings is sign dependent (see table 2). All explanatory variables are significant when earnings are positive and next year’s earnings are to be forecasted. When the mean of the next three earnings is predicted, only the level of cash flow and accruals is significant. In the case of negative earnings, the mean of the next three earnings seems practically unpredictable. Only the level of accruals is
significantly related to next year’s earnings when earnings are negative. The explanatory power from the earnings prediction regressions is lower when earnings are negative than when they are positive, but the explanatory power is less sign dependent than in the cash flow predictions.
Note that multicollinearity could have been a challenge in these specifications since four related measures are used as explanatory variables. Fortunately, it is not. The variance inflation factor (VIF) is computed for each regression, and they are each significantly below the cut-off threshold of 10 proposed by for instance Hair et al. (2006). As a result, multicollinearity is not considered a problem in the regression analyses.
Overall, the findings of step 1 indicate that both cash flow and accruals are significantly related to future firm performance. However, the results are dependent on the sign of earnings and whether cash flow or earnings is forecasted. The most unambiguous results are found for cash flow levels. Present cash flow is related to future cash flow and earnings in the total sample and when earnings are positive. It does not matter whether it is next year’s performance or the mean of the next three years that is analysed. Accruals are related to future earnings as long as earnings are not negative. However, accruals are generally not related to future cash flow. Except for short term predictions in the positive earnings sample, the change variables are insignificant. In the negative earnings sample, all explanatory variables are typically insignificant. Thus, cash flow and accruals seem to show little association with future firm performance when earnings are negative.