7.1 The value relevance hypothesis
The results of the value relevance regression (table 5) indicate a slightly negative association of -0.09 between CAR and GWIL. The sign of GWIL is negative, as expected, but the coefficient is not statistically significant at any conventional level. Hence, the results provide no support for the value relevance hypothesis (H1). Goodwill impairment losses are therefore on average non-value relevant for investors on the Swedish equity market. Consequently, the result also suggests that most investors on the Swedish equity market find the goodwill asset to be non-value relevant.
Two of the control variables are statistically significant, BETA at the 10% level and MTB at the 1% level. The remaining control variables are however not statistically significant. The R-squared of the value relevance regression indicates that 13.36% of the variance in CAR is explained by the regression variables. Hence, a majority of the variance in CAR is explained by factors outside the model.
Table 5. The value relevance regression (equation 13).
Variable Coefficient P-value T statistics
GWIL -0.0927856 0.472 -0.72 ∆ROE -0.020934 0.567 -0.57 LTG 0.0122911 0.955 0.06 BETA -0.043027* 0.091 -1.70 SIZE 0.0068524 0.110 1.61 MTB 0.0083971*** 0.002 3.22 MOM 0.0372921 0.182 1.34 Intercept -0.09998* 0.084 -1.74 n 188 R2 0.1336 F value 2.39**
Notes: *** significant at the 1% level, ** significant at the 5% level and * significant at
the 10% level. Robust standard errors are used for the value relevance regression. ∆ROE, LTG and BETA are winsorized to the 2.50 and 97.50 percentiles.
7.2 The leverage hypothesis
The leverage regression (table 6) results in a slightly positive GWIL coefficient of 0.003. The coefficient is, however, not statistically significant at any conventional level, and investors therefore consider goodwill impairment losses in firms with low financial leverage as non-value relevant.
The interaction term (FLEV x GWIL) coefficient, measuring the difference in average market reaction caused by a goodwill impairment loss for the two financial leverage groups, is negative as expected. The interaction term coefficient of -0.945 is also statistically significant at the 1% level. From an economic perspective, it is also worth noting the magnitude of the interaction term coefficient. Ceteris paribus, in firms with high financial leverage, a goodwill impairment loss in relation to total assets (GWIL) of one percentage point causes on average a 0.945 percentage point more negative CAR compared to a GWIL of equal size in firms with low financial leverage. The interaction term coefficient must therefore be considered economically significant.
As the interaction term coefficient is negative and statistically significant, and the GWIL coefficient (the low leverage group) is close to zero and not statistically significant, the result supports the leverage hypothesis (H2) at the 1% significance level. Hence, investors on the Swedish equity market react more negatively to goodwill impairment losses in firms with high financial leverage compared to goodwill impairment losses in firms with low financial leverage.
A similar result is also found when two separate multivariate OLS regressions are carried out, one for each leverage group (see appendix 3). The results indicate that goodwill impairment losses are non-value relevant for investors in firms with low financial leverage. On the contrary, goodwill impairment losses are value relevant for investors in firms with high financial leverage. To conclude, financial leverage impacts the value relevance of goodwill impairment losses, and consequently investors only find the goodwill asset valuable in firms with high financial leverage. A part from the interaction term, the control variable MTB is statistically significant at the 5% level. The other control variables are however not statistically significant. The R-squared of the leverage regression indicates that the regression variables explain 27.11% of the variance in CAR. Hence, a majority of the variance in CAR is explained by factors outside the model.
Table 6. The leverage regression (equation 14).
Variable Coefficient P-value T statistics
FLEV x GWIL -0.9452515*** 0.006 -2.73 GWIL 0.0035836 0.970 0.04 ∆ROE -0.0404847 0.283 -1.08 LTG -0.1020476 0.627 -0.49 BETA -0.0374534 0.104 -1.63 SIZE 0.0048003 0.223 1.22 MTB 0.0085712** 0.013 2.51 MOM 0.014448 0.480 0.71 FLEV 0.0087285 0.381 0.88 Intercept -0.0693742 0.179 -1.35 n 188 R2 0.2711 F value 2.18**
Notes: *** significant at the 1% level, ** significant at the 5% level and * significant at
the 10% level. Robust standard errors are used for the leverage regression.∆ROE, LTG
and BETA are winsorized to the 2.50 and 97.50 percentiles.
7.2.1 Robustness test
A robustness test is carried out to test the results of the leverage hypothesis (H2) further and to verify the underlying debt covenant theory of the hypothesis. Information on debt covenants is collected for each of the 188 goodwill impairment loss observations. 50 of the 188 goodwill impairment loss observations are recognized in firms with debt covenants at the year-end of the goodwill impairment loss (see appendix 4). These 50 goodwill impairment losses constitute a subsample that is used for the robustness test.
The robustness test regression (table 7) results in a GWIL coefficient of -0.42 that is statistically significant at the 10% level. The magnitude of the GWIL coefficient can also be considered economically significant as a goodwill impairment loss in relation to total assets of one percentage point on average causes a negative CAR of 0.42 percentage point. Investors on the Swedish equity market therefore find goodwill impairment losses value relevant in firms with debt covenants. The result of the
robustness test therefore confirms that debt covenants play a crucial part in determining value relevance.
Among the other regression variables, LTG and MOM are statistically significant at the 5% level, while the other control variables are not statistically significant. The R- squared of 43.75% indicates that the regression variables explain almost half of the variance in CAR.
Table 7. The robustness test regression (equation 13).
Variable Coefficient P-value T statistics
GWIL -0.4272611* 0.075 -1.83 ∆ROE -0.0529101 0.222 -1.24 LTG 1.174768** 0.027 2.29 BETA -0.0348518 0.477 -0.72 SIZE -0.000254 0.975 -0.03 MTB 0.0050325 0.470 0.73 MOM 0.1637679** 0.011 2.66 Intercept -0.038279 0.760 -0.31 n 50 R2 0.4375 F value 4.67***
Notes: *** significant at the 1% level, ** significant at the 5% level and * significant at
the 10% level. ∆ROE, LTG and BETA are winsorized to the 2.50 and 97.50 percentiles.