Knowledge that Transforms

The time series behavior of earnings is an important area for empirical research because of its implications for related research in several areas of and finance. Although many other examples could be provided, three accounting issues immediately come to mind: (1) income smoothing, (2) the relative forecast ability of alternative income measurements, and (3) interim reporting. The hypothesis that management uses discretionary practices to smooth income was first posited by Gordon [21] and later tested by Gordon, Horwitz, and Meyers [22] and by Copeland [14] among others. As stated by Gordon, smoothing involves minimizing the deviations of reported income from some standard, where the standard is defined in terms of normal income. Normal income has never been precisely defined at the conceptual level, but in many cases it appears to have been used in the sense of the expected value of the process at a given point in time. A variety of models could be used, and in fact have been used, to assess the normal or expected value of income for a given period. Each model makes specific assumptions about the process generating income numbers. Any inferences drawn from empirical evidence regarding the existence of income smoothing (or the lack of it) are dependent upon the validity of the assumptions made about the underlying earnings process. Moreover, as shown later in the paper, for certain processes attempts to smooth income can have exactly the opposite effect. Yet the models used in the smoothing literature represent only a narrow range of the possible alternatives, little justification (either a priori or empirical) has been offered in their behalf, nor has there been any direct, rigorous investigation of the underlying nature of the earnings process itself.

One purpose of an accounting system, or any information system, is to provide a set of signals designed to communicate descriptions of certain past phenomena believed to be decision relevant in the future.' However, it is difficult to evaluate a given or proposed information system because of its complexity. This complexity arises in part because of the interrelated problems of which phenomena to describe and how best to describe them. Since complexity hinders information system evaluation, decomposition of the total interrelated system into a number of less complex subsystems for evaluation purposes is often desirable. Unfortunately, most decomposition will introduce errors into the analysis. But if we can predict the resultant errors, decomposition can still provide a useful surrogate evaluation method. The purpose of this paper is to explore the decomposition approach to information system evaluation, at the conceptual level, relying heavily on Feltham's recently proposed model for predicting the value of information in the single decision case.2

Financial statement consolidation, Aggregation, Informational approach

Liquidity, Financial Ratios, Liquidity-flow index

Business combination, Intangible assets, APB Opinion No. 16, Pooling of interest

Financial statement consolidation, Aggregation, Informational approach, Information loss

Segment reporting, Disclosures, Mandatory Disclosure, Regulatory timeline

Probabilistic depreciation is a method of determining the proper depreciation charge in each year of an asset's service life, when the service life is a random variable with known distribution. The paper discusses how the service life distribution is modified as more information is obtained about the actual lifetime of the asset. The problem of determining the proper amount to be charged each year to depreciation while at the same time maintaining the proper balance in the accumulated depreciation account is considered. The analysis is done both for a single asset case and for group depreciation. A final section discusses the use of Bayesian analysis for estimating the particular form of the service life distribution while the assets are in service.