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«Leif Atle Beisland University of Agder Dissertation submitted to the Department of Accounting, Auditing and Law at the Norwegian School of Economics ...»

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samples. While the traditional industries report an R2 of 60%, the non-traditional industries report an R2 of only 45%. As for the incremental explanatory power, the non-traditional industries have a higher incremental R2 for the book value of equity than the traditional industries. The value relevance common to book value and earnings is far higher for the traditional industries. Both sub-samples have a low incremental value relevance of net earnings. The same pattern exists when the individual years are aggregated into five-year periods. As we test for a difference in the mean adjusted R2 between the traditional and nontraditional industries we find strong support for an overall difference in value relevance in favour of the traditional industries (p=0.006).8 The return regressions do not at all support this difference between the two industry categories. The mean explanatory power is 11% for the traditional industries and 13% for the non-traditional industries.9 The traditional industries report more significant regression coefficients than the non-traditional sector, caused partly by fewer observations (particularly in some early years) for the non-traditional industry category. The five-year pooled regressions do not change the impression that there is no substantial difference in the explanatory power between the two samples when using a return specification. As for the incremental explanatory power of earnings and the change in earnings, the results appear relatively similar between the two sectors.

75.4%. A similar figure is reported by Lev and Zarowin [1999]. Gjerde, Knivsflå, and Sættem [2007] report an average explanatory power for their Norwegian sample in the years 1980-2004 of 46.7%.

When applying a standard t-test for differences in means.

For Return model specifications an average explanatory power of 11.2% is high compared to many other studies. Lev and Zarowin [1999] report an average R2 of 7.4% in the years 1978 to 1996. Gjerde et al. [2007] report an average R2 of 5.4% in the years 1980 to 2004. Plenborg [1998] reports an average of 13.7% for a Danish sample between 1985 and 1991. Alford, Jones, Leftwich, and Zmijewski [1993] report a low R2 for Sweden in 1984 to 1990 (2.7%, which is the lowest of all countries in their sample). In our study there are 6 times as many observations as in their study and we report an average R2 of 7.3% in that same period (see Panel A of Table 3).

–  –  –

Table description Table 3 describes the value relevance of accounting information for a sample of Swedish firms in the time-period 1979 to

2004. It summarizes the number of observations (N), regression coefficients (a1, a2, b1 and b2), total explanatory power (R2PRI and R2RET) as well as the incremental and common explanatory power (R2BVS, R2EPS, R2∆EPS, R2COM) for the total sample (Panel A), the traditional industries sample (Panel B) and the non-traditional sample (Panel C). Firms are classified into industries following Table 1. Each Panel presents data for individual years, the mean for all years, pooled results for 5-year periods and pooled results for the whole 25-year period. The highlighted years refer to the “IT-bubble” years. Data is

analyzed using both a Price and a Return model, defined as:

–  –  –

where Pit is the share price of firm i in period t, BVS is the book value per share, EPS is the net earnings per share, R is the dividend-adjusted return, Earn is earnings and ∆Earn is the yearly change in earnings. Both Earn and ∆Earn are scaled by the market value of equity at t-1. The incremental value relevance is estimated in a similar way for Price and Return models. For the Price model, we first estimate R2PRI (the total explanatory power), R21 ( a regression of Pit on BVSit) and R22 (a regression

of Pit on EPSit). The incremental value relevance (R2BVS and R2EPS) and the common value relevance in then calculated as:

–  –  –

The annual means for the non-traditional industries are computed for the period 1985-2004 due to few observations in the first years of the sample period. Boldface denotes significance at a 10% level, two-sided test. R2 is set equal to zero if negative. In such cases, the incremental explanatory power is set equal to zero as well.

t-test for difference in the mean adjusted R2 between traditional and non-traditional industries:

0.006 for the Price model specification (p-value, difference in favour of the traditional industries)

0.563 for the Return model specification (p-value, difference in favour of the non-traditional industries)

–  –  –

The results reported in Table 4 change the picture somewhat. For the price model specification the mean annual R2 increases, to 58%, for the total sample (Panel A). Thus an adjustment for negative earnings, as suggested by e.g. Hayn [1995], Collins, Maydew and Weiss [1997], Francis, Schipper and Vincent [2003], enables accounting information to explain security prices better. But whereas the traditional industries experience hardly any change in explanatory power (an increase from 60% to 61%) the change is substantial for the non-traditional industries (an increase from 45% to 56%). Indeed, the difference in explanatory power between the two industry categories is now statistically insignificant (p=0.369). However, we note a substantial instability over time in the association between the accounting information and market values for the non-traditional industries relative to that of the traditional industries.





A control for negative earnings also benefits the value relevance as measured by the return models. However, here the increase is similar for both sub-samples as the traditional industries experience an increase from 11% to 15% and non-traditional industries change from 13% to 18%. There is no statistically significant difference between the two industry categories (p=0.396), but it seems like the value relevance is not higher in traditional industries.

The model specifications with a dummy for negative earnings appear better than the previous ones with more statistically significant coefficients and higher explanatory power. The properties of positive and negative earnings are apparently different and adjusting our models to accommodate such differences improves the results. There is reason to believe that both

–  –  –

Table description Table 4 describes the value relevance of accounting information for a sample of Swedish firms in the time-period 1979 to

2004. It summarizes the number of observations (N), regression coefficients (a1, a2, a3, b1, b2 and b3) and the total explanatory power (R2PRI and R2RET) for the total sample (Panel A), the traditional industries sample (Panel B) and the non-traditional sample (Panel C). Firms are classified into industries following Table 1. Each Panel presents data for individual years, the mean for all years, pooled results for 5-year periods and pooled results for the whole 25-year period. The highlighted years

refer to the “IT-bubble” years. Data is analyzed using both a Price and a Return model, defined as (cf. Francis et al., 2003):

–  –  –

where Pit is the share price of firm i in period t, BVS is the book value per share, EPS is the net earnings per share, R is the dividend-adjusted return, Earn is earnings and ∆Earn is the yearly change in earnings. Both Earn and ∆Earn are scaled by the market value of equity at t-1.

The annual means for the non-traditional industries are computed for the period 1985-2004 due to few observations in the first years of the sample period. Boldface denotes significance at a 10% level, two-sided test. R2 is set equal to zero if negative.

t-test for difference in the mean adjusted R2 between traditional and non-traditional industries:

0.369 for the Price model specification (p-value, difference in favour of the traditional industries)

0.396 for the Return model specification (p-value, difference in favour of the non-traditional industries)

–  –  –

properties of accounting earnings by dividing reported earnings into a transitory and a sustainable component. It is expected that the accounting earnings reported by firms in general, and in non-traditional industries in particular, contain a transitory component. The value relevance of accounting information can be negatively influenced by this transitory component of earnings. Sustainable earnings for the individual firm (SEit) are estimated by multiplying the beginning of the period total assets with its mean net profit scaled by total assets for the past five years (minimum three years). We denote this period T.

–  –  –

This measure is used to estimate transitory earnings. Reported earnings is decomposed into a sustainable (hereafter SEPS) and a residual transitory component. Untabulated results show that substituting reported earnings with SEPS actually decrease the value relevance for both the price and return model specifications. Hence the transitory component of earnings is relevant to investors (but again, untabulated results show that it is less relevant than SEPS).

Table 5 reports the value relevance when earnings are decomposed into its sustainable and transitory components. In price regressions the overall explanatory power increases to 58% (from 55% for the standard model shown in Table 3). There is hardly any change for the value relevance of accounting information in the traditional industries (up from 60% to 61%), but a substantial increase for the non-traditional industries (up from 45% to 53%). Although the explanatory power is not as high as for the negative earnings model (results shown in Table 4), it is obvious that a considerable part of earnings is transitory and that the market values

–  –  –

Table description Table 5 describes the value relevance of accounting information for a sample of Swedish firms in the time-period 1979 to

2004. It summarizes the number of observations (N), regression coefficients (a1, a2, a3, b1, b2, b3 and b4) and the total explanatory power (R2TOR) for the total sample (Panel A), the traditional industries sample (Panel B) and the non-traditional sample (Panel C). Firms are classified into industries following Table 1. Each Panel presents data for individual years, the mean for all years, pooled results for 5-year periods and pooled results for the whole 25-year period. The highlighted years

refer to the “IT-bubble” years. Data is analyzed using both a Price and a Return model, defined as:

–  –  –

where Pit is the share price of firm i in period t, BVS is the book value per share, EPS is the net earnings per share, R is the dividend-adjusted return, Earn is earnings and ∆Earn is the yearly change in earnings. Both Earn and ∆Earn are scaled by the

market value of equity at t-1. A sustainable component of reported earnings is estimated using the model:

–  –  –

In the Price model specification sustainable earnings are divided by the number of outstanding shares at time t, and in the Return model specification they are divided by the market value of equity at t-1. The difference between reported and sustainable earnings is the transitory component of earnings ((EPS – SEPS) and (Earn – SE)).

The annual means for the non-traditional industries are computed for the period 1985-2004 due to few observations in the first years of the sample period. Boldface denotes significance at a 10% level, two-sided test. R2 is set equal to zero if negative.

t-test for difference in the mean adjusted R2 between traditional and non-traditional industries:

0.086 for the Price model specification (p-value, difference in favour of traditional industries)

0.784 for the Return model specification (p-value, difference in favour of non-traditional industries)

–  –  –

increase in value relevance and the largest increase occurs for the traditional industries.

However, decomposing earnings into a sustainable and a transitory component does not disturb the previous finding that non-traditional industries display slightly higher value relevance than traditional industries.

Next we examine whether a measure of sustainable earnings has information beyond that of a simple dummy that merely adjusts for differences between profits and losses. It can be expected that many of the transitory components our model detects are in fact losses. Table 6 shows results from the price model in which reported earnings is decomposed into its sustainable and transitory components and a dummy is used for negative earnings.10 Again we find an incremental positive effect of adding an additional explanatory variable. The overall explanatory power increases to 59%. For the full sample as well as the two sub-samples all four explanatory variables are statistically significant. More important, the explanatory power is substantially higher for both the traditional and non-traditional industry categories than what it is for the total sample. The incremental effect of controlling for nonlinearities in positive earnings is 2% in traditional industries and 7% in non-traditional industries. The explanatory power in the non-traditional industries increases to such an extent that there is no difference between traditional and non-traditional industries as both industry categories display an R2 of 63% (p=0.941). The transitory elements of earnings are thus not just losses but also variations in the level of profits. We emphasize that the results do not refute past findings that the value relevance is lower in non-traditional industries when using reported accounting information, but we broaden the perspective by showing that the differences disappear if the different properties of sustainable and transitory earnings are considered.

We have also re-run the return regression using this methodology. However, as the results are practically identical to the ones reported in Table 4, we do not tabulate the results. Mean explanatory power is identical to Table 4 for the traditional sector, while it increases by one percentage point for the non-traditional industries.



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