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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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earnings disaggregation is also studied in this section. Table 6 indicates that the incremental explanatory power of earnings increases as earnings are disaggregated. However, the effect of including a dummy variable for negative earnings appears to be even more substantial than the earnings disaggregation effect. Incremental explanatory power increases from 5.99% to 9.32% as earnings are disaggregated. Even with aggregated earnings, the incremental explanatory power is still 11.18% when the sign of earnings is taken into account. Table 6 shows that the incremental explanatory power reaches 13.49% as earnings are disaggregated and a dummy variable for the sign of earnings is included. As in the main analysis, there is clear evidence that both disaggregated earnings and the sign of earnings should be incorporated in value relevance studies. There is much to gain by accounting for the sign effect even if earnings are disaggregated, and vice versa.25 Overall, Tables 5 and 6 do not present results that alter previously stated conclusions.

4.4 Excess Return In all analyses so far I have applied the “raw” return for each stock. To control for possibly exogenous (to accounting earnings) market-wide effects on stock returns (e.g., changes in expected return), I re-run all regressions using excess return as the stock return measure (see Dechow, 1994). Excess return is defined as the return of a stock minus the yearly market-wide return. Market wide returns are estimated from OSEBX26 – Oslo Stock Exchange Benchmark Index – at 30 December of each year. It turns out that this change has practically no influence on the adjusted R 2 values previously reported. These results are therefore not tabulated. The conclusions stated in the main section remain unchanged after this robustness check.

I have also run tests in which one control variable is added at a time. The results are qualitatively identical to the reported results.

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earnings are less value-relevant than positive earnings. Negative earnings often seem to have no association with stock returns at all. This study provides evidence that the lack of value relevance for negative earnings, to a certain extent, is a matter of earnings aggregation. When bottom line earnings are used as the explanatory variable in value relevance regressions, negative earnings are far less value-relevant than positive earnings. However, as earnings are disaggregated into major components, the increase in value relevance is much larger for negative than for positive earnings. The gains in explanatory power from disaggregating earnings into components are much more substantial for negative than for positive earnings.

These results are consistent with my hypothesis. Negative bottom line earnings are not expected to persist and will therefore have little value relevance. However, individual earnings components may still be persistent and, thus, will contain information relevant to investors.

The second part of the paper studies whether it is more useful to account for the sign of earnings in value relevance research than to disaggregate earnings into components. The empirical findings show that explanatory power of regression analyses increase dramatically as earnings are disaggregated and the sign of bottom line earnings is taken into account.

Compared to a “standard” Easton and Harris regression of stock returns on aggregate and changes in earnings, disaggregating earnings and accounting for the sign of earnings doubles the explanatory power of the regressions. Explanatory power increases significantly when OB Total – Oslo Stock Exchange’s all shares index – is used to represent market wide returns for the period 1992-1995.

–  –  –

account. Similarly, accounting for the sign of earnings is extremely useful for all earnings aggregation levels. However, if earnings are not disaggregated into a relatively large number of items, the sign effect seems to dominate the disaggregation effect. Overall, the empirical findings suggest that the various earnings items generally have different associations (regression coefficients) with stock returns. Additionally, each earnings item’s coefficient is likely to be dependent on the sign of bottom line earnings.

Several interesting questions arise from studies like this. I will focus particularly on one such question: Why are depreciation and changes in depreciation the only significant explanatory variables in the most disaggregated earnings specification for the negative earnings sample?

In the positive earnings sample, explanatory power is 13.62% for the most disaggregated specification. The equivalent number for the negative earnings sample is 6.50%. In the positive earnings sample, most earnings components have significant coefficients. In the negative earnings sample, only depreciation and depreciation change are statistically significant. The coefficient on depreciation is positive, while the coefficient on the change in depreciation is negative. The finding that, for a given amount of loss, stock returns are higher when the loss can be attributed to depreciation than when it can be attributed to cash flows is in some sense understandable. On the other hand, increases in depreciation have a negative effect on stock returns. Clearly, for negative earnings, depreciation bears information highly associated with stock returns. Why is this? Does depreciation proxy for something else? This issue is left for future research.





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