Value relevance tests

Table 3.5 shows the results of the multivariate return regression analysis that tests for DVAs’

value relevance. In Model 1, earnings (including DVAs) are not significantly associated with returns. Splitting earnings in DVAs and the remaining components of earnings (Model 2), I find that the coefficient on DVA is significantly negative. This is consistent with DVAs not being value relevant for investors in the sample, in line with findings of Cedergren et al.

(2015). While this stands in contrast to the findings of Chung et al. (2012), their sample differs from mine in several aspects, for example, their sample period goes only until 2010.

In Model 3, I test whether the proportion of corresponding assets measured at fair value mediates the value relevance of DVAs. As the coefficient on the variable that tests for this association (DVA*FVA) is insignificant, I conclude that this is not the case. Additionally, an F-Test for the coefficients on DVA and DVA*FVA shows that the coefficients are not jointly significant (p-value: 0.8200). In conclusion, while Fontes et al. (2014) demonstrate that a higher proportion of fair value assets is generally able to mediate the information asymmetry from DVAs among investors, I do not find that a higher proportion of fair value assets similarly improves investors’ perception of DVAs concerning DVAs’ value relevance.

Among other reasons, one explanation could be the different samples between their and my study. Especially, Fontes et al. 2014 conduct their tests for a sample of European banks, not US banks.

Concerning my main tests, I find that the coefficient on DVA*FVA1 in Model 4 is significantly positive. Additionally, an F-test shows joint significance for DVA and DVA*FVA1 (p-value: 0.0000). This finding suggests an increasing DVA value relevance in the presence of a higher proportion of corresponding level 1 fair value assets. The finding is consistent with investors being able to better assess the wealth transfers between shareholders and debtholders due to changes in credit risk (and therewith: DVAs), when the corresponding assets provide reliable information on the change in credit risk. In contrast, the coefficient on DVA*FVA23 is negative. An F-test shows joint significance of the coefficients on DVA and DVA*FVA23 (p-value: 0.0885). This finding suggests that a higher proportion of level 2 and 3 fair values do not increase DVAs’ value relevance. The finding is consistent with the notion that investors do not price the wealth transfer between shareholders and debtholders due to a change in credit risk (as reflected by DVAs), when the corresponding assets related to the change in credit risk are recognized on the balance sheet but not measured reliably. In Model

5, the coefficients on FVA12 and FVA3 are insignificant and not jointly significant with DVA.

This implies that the found association between DVAs’ value relevance and fair values’

reliability from Model 4 does not hold when classifying level 2 assets as reliable. In the light of findings from Hitz (2007) and Song et al. (2010), this is somewhat surprising. A potential explanation could lie in the relatively low proportions of level 3 fair value assets that firms hold in my sample. The descriptive statistics from Table 3.3 show that the average proportion of held level 3 fair value assets (1.62%) is notably smaller than the proportions of level 1 (4.48%) and level 2 fair value assets (19.53%). This finding could be indicative of a mutual understanding of investors and managers that level 3 fair value measures are not reliable and thus managers waive from using level 3 measures due to expected negative effects. As the amounts of firms’ level 3 fair value assets are consequently low, they are relatively less likely to reflect the changes in credit risk that underlie DVAs.

Throughout the tests, the coefficient on DVA*UIA is significantly negative. This is consistent with the finding of Cedergren et al. (2015) that DVAs are more value relevant when the level of related unrecognized intangible assets is low. However, in contrast to their findings, I do not find value relevance for earnings throughout the models. Also, unlike them, I do not find DVAs to be value relevant in Model 2, the model which most closely follows Cedergren et al. 2015. The differences are potentially due to the fact that both studies’

samples are relatively small as adoption of the fair value option for liabilities is not widespread (Guthrie et al. 2011). Therefore, the small differences in variable measures and in the sample selection between their study and my study might lead to the differing results. To highlight one difference in variable measures: Cedergren et al. (2015) include DVAs in their sample that are only recognized in regulatory reports (FRY9C reports) but not in annual and quarterly reports (10-Q and 10-K filings). In annual and quarterly reports, firms need to disclose only “significant” DVAs (FASB 2007). Therefore, a number of firms disclose DVAs only in regulatory reports as anecdotal evidence in the course of my data collection suggests.

Contacting investor relations services of such firms, I receive confirmation that this is indeed a matter of materiality. I do not include such DVAs in my sample as they are possibly not compliant with US GAAP regulation and “not audited or reviewed by external auditors”

(Cedergren et al. 2015). However, the higher relevance of DVAs that the findings of Cedergren et al. (2015) reflect in comparison to my findings could potentially be driven by their inclusion of such DVAs. This would imply that investors, on average, price DVAs even

when they are not recognized in annual and quarterly reports, consistent with the findings of Barth et al. 2008.

In sum, my findings add to the findings of Cedergren et al. 2015. Their study shows that investors do not price DVAs when the sources of the change in credit risk are clouded by a large amount of unrecognized intangible assets. My findings additionally show that even

E_excl_DVA -0.0057 -0.0689 -0.0857 -0.0030

(-0.09) (-0.65) (-1.25) (-0.05)

Table 3.5 (continued)

This table displays coefficient estimates from an OLS model. The underlying regression model is (Model 5):

𝑅_𝑖𝑡 = 𝛽₀+ 𝛽₁𝐸_𝑒𝑥𝑐𝑙_𝐷𝑉𝐴_𝑖𝑡 + 𝛽₂𝐷𝑉𝐴_𝑖𝑡+ 𝛽₃𝐷𝑉𝐴_𝑖𝑡 ∗ 𝐹𝑉𝐴12_𝑖𝑡 + 𝛽₄𝐷𝑉𝐴_𝑖𝑡∗ 𝐹𝑉𝐴3_𝑖𝑡+ 𝛽₅𝐹𝑉𝐴12_𝑖𝑡 + 𝛽₆𝐹𝑉𝐴3_𝑖𝑡 + 𝛽₇𝐷𝑉𝐴_𝑖𝑡∗ 𝑈𝐼𝐴_𝑖𝑡+ 𝛽₈𝑈𝐼𝐴_𝑖𝑡 + 𝛽₉𝑂𝐶𝐼_𝑖𝑡+ 𝛽₁₀𝐿𝐸𝑉_𝑖𝑡+ 𝛽₁₁𝑆𝐼𝑍𝐸_𝑖𝑡+ 𝜀_𝑖𝑡 For all variable definitions, see Table 1. E, E_excl_DVA, DVA, OCI, and UIA are scaled by lagged market value of equity. The regression models have standard errors that are heteroscedasticity robust and clustered at firm and quarter-year level. t-values are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.