Reviews the book `Australian Company Financial Reporting: 1990,' by J.B. Ryan, B.H. Andrew, M.J. Gaffikin and T. Heazlewood.

Reviews the book `TQC for Accounting: A New Role in Companywide Improvement,' by Takashi Kanatsu.

Reviews the book `Performance Measurement for World Class Manufacturing: A Model for American Companies,' by Brian H. Maskel.

Relates belief functions to the structure of audit risk and provides formulas for audit risk under certain assumptions. Review of literature; Structure of audit evidence; Control factors and accounting systems; Plausibility interpretation of audit risk; Limitations in the application of the models; Derivation of audit risk formulas.

Reviews the book "Handbook of International Accounting," by Frederick D.S. Choi.

Reviews the book `Private Sector Performance Measures and Their Applicability to Government Operations,' by David J. Harr and James T. Godfrey.

Reviews the book `Common Cents: The ABC Performance Breakthrough,' by Peter B.B. Turney.

[This article relates belief functions to the structure of audit risk and provides formulas for audit risk under certain simplifying assumptions. These formulas give plausibilities of error in the belief-function sense. We believe that belief-function plausibility represents auditors' intuitive understanding of audit risk better than ordinary probability. The plausibility of a statement, within belief-function theory, measures the extent to which we lack evidence against the statement. High plausibility for error indicates only a lack of assurance, not positive evidence that there is error. Before collecting, analyzing, and aggregating the evidence, an auditor may lack any assurance that a financial statement is correct, and in this case will attribute very high plausibility to material misstatement. This high plausibility does not necessarily indicate any evidence that the statement is materially misstated, and hence, it is inappropriate to interpret it as a probability of material misstatement. The SAS No. 47 formula for audit risk is based on a very simple structure for audit evidence. The formulas we derive in this article are based on a slightly more complex but still simplified structure, together with other simplifying assumptions. We assume a tree-type structure for the evidence, assume that all evidence is affirmative and that each variable in the tree is binary. All these assumptions can be relaxed. As they are relaxed, however, the formulas become more complex and less informative, and it then becomes more useful to think in terms of computer algorithms rather than in terms of formulas (Shafer et al. 1988). In general, the structure of audit evidence corresponds to a network of variables. We derive formulas only for the case in which each item of evidence bears either on all the audit objectives of an account or on all the accounts in the financial statement, as in figure 1, so that the network is a tree. Usually, however, there will be some evidence that bears on some but not all objectives for an account, on some but not all accounts, or on objectives at different levels; in this case, the network will not be a tree. We assume that all evidence is affirmative because this is the situation treated by the SAS No. 47 formula and because belief-function formulas become significantly more complex when affirmative and negative evidence is combined. This complexity is due primarily to the renormalization involved in Dempster's rule for combining belief functions. The variables in the network or tree represent various audit objectives, accounts, and the financial statement as a whole. We assume these variables are binary. For example, we assume that an account either is or is not materially misstated. This assumption is clearly too restrictive for most audit practice. Often, for example, an auditor must consider immaterial errors in individual accounts that could produce a material error in the financial statement when they are aggregated. We derive formulas for plausibility of material misstatement at three levels: the financial statement level, the account level, and the audit objective level. The formula at the audit objective level resembles the SAS No. 47 formula, but the formulas at the other two levels are significantly different. Because our model does distinguish evidence gathered at the three different levels, audits based on our formulas are sometimes significantly more efficient than audits based on the SAS No. 47 model or on the simpler Bayesian models.]

[This study provides evidence that market segmentation affects the strength of the association between financial disclosure and net interest cost for new issues of municipal bonds. Previous studies (see, e.g., Amershi and Ramamurtie 1990; Cook 1982; Hendershott and Kidwell 1978; Kidwell et al. 1983) show that the geographic segmentation of primary markets for municipal bonds along regional and national lines is characterized by different sources and costs of information. Because little information is available from alternative sources for the less marketable bonds of smaller issuers which use regional markets, financial reporting variables are hypothesized to be associated more strongly with interest costs for these issues than for those of larger municipalities, whose bonds are typically issued in the national bond market. Cook (1982) argues that the costs of obtaining information about small issuers from alternative sources are relatively high. For these issuers, who also do not issue bonds frequently, regional underwriters may be the only informational intermediaries (other than rating agencies if the bonds are rated) between the issuers and investors (Kidwell et al. 1983). Thus, financial and other entity-specific information contained in the offering statement are expected to be weighted more heavily in pricing these bonds than for bonds of larger issuers. Although this differential-information hypothesis is analogous to Atiase's (1985) argument regarding differential predisclosure of information, our study assumes differential availability of information across market segments, and size alone is not likely to be an adequate proxy for segmentation. Our main analysis uses the nature of the underwriting syndicate, whether it is managed by a national or regional underwriter, to proxy for the relevant market segment. Previous studies of municipal accounting (see, e.g., Ingram and Copeland 1982; Wallace 1981; Wilson and Howard 1984) have provided evidence of an association between bond measures and accounting and auditing variables. However, the results were mixed and inconsistent, which may be attributable to differences in the entities examined (cities, counties, and school districts), the time periods examined, or definitions of variables. Also, differences in the mix of bonds issued in each segment may have contributed to the inconsistent results. By examining the relationship between accounting and auditing variables and bond interest costs separately for each segment of the primary bond market, our study provides useful evidence on the potential effect of different information environments on this relationship. Regression results based on a sample of 119 new municipal bond issues, partitioned on a measure of segmentation (regional vs. national underwriter), are consistent with the hypothesis that the association between the quality and quantity of financial disclosure and interest costs is stronger for municipalities that issue bonds in local or regional markets than for those that issue bonds in the national market. Our results suggest that future research in this area should consider the potential effects of market segmentation when testing the association between accounting or auditing variables and bond interest costs.]

Reviews two books. "Auditing Symposium X: Proceedings of the 1990 Deloitte & Touche," edited by Rajendra P. Srivastav; "University of Kansas Symposium on Auditing Problems," edited by Rajendra P. Srivastava.