Reviews the book "Audit Problem: Metalcraft, Incorporated," 6th ed., by Arthur W. Holmes and Dale L. Kiefer.

This article discusses various types of time series processes for accounting earnings, based on results of previous studies. Basically, evidence regarding the time series properties is important since it provides some guidance as to the process generating accounting signals. Three accounting processes are used and discussed here: the mean reverting process, autoregression, and moving average process. According to the author, a pure mean reverting process can be used for generating accounting signals if: events affecting the firm had no future period impact or events had a future period impact and the accounting system recognized the future period effects in the current periods earnings. With the statistical properties of the three processes as a foundation, the author examines findings of several previous studies. The purpose of the time series analysis in this study is to distinguish between these two competing explanations of the observed moving average properties of the series of earnings signals.

The article focuses on a new design for the intermediate accounting course. One who sets out to review financial accounting textbooks will likely conclude that accounting educators are in virtually unanimous agreement on the manner, in which accounting information should be organized, classified and sequenced for delivery to students. Not only do all major intermediate accounting texts adopt virtually identical plans of organization, but also that plan is the same as that used in most introductory texts. The plan, well known to accounting educators, is that after initial chapters devoted to general aspects of the balance sheet and income statement, the text proceeds to have chapters on each of the major balance sheet and income statement accounts, that is, the chapter titles read like the financial statements themselves: cash, accounts receivable, inventories, fixed assets, current liabilities and so on. The similarity of organization in introductory and intermediate texts gives students the impression that intermediate accounting is just principles a little deeper.

This article discusses the nature of the assessment activity for audit decision making with citing an overview of some relevant research and some supporting evidence on the topic. The purpose of this article is to give additional exposure to a method of assessing prior probability distributions that appears to be particularly congruent with the auditor's environment. The process of quantifying qualitative evidence appears in the literature under a variety of terms, including "eliciting Bayesian priors" and "probability encoding." In this article, the process is referred to as "assessing prior probabilities or subjective probability distributions (SPDs)." According to the author, a satisfactory assessment of the prior probability function would result when the auditor, after training, believes that the resulting distribution is a good summary of his or her qualitative evidence. In a recently conducted study, all of the auditors had some difficulty in deciding where the first and third quartiles of the SPD ought to be in using the direct assessment method. There is some evidence that training in assessing SPDs may be an effective means of overcoming a common problem, that is, a possible tendency to underestimate dispersion. Results of that study indicate that even limited training may have an effect and that at least some auditors are receptive to the study and use of subjective probability distributions and Bayes method.

Reviews the books "Management Accounting: Text and Cases" 5th ed. and "Management Accounting Principles," 3rd ed., by Robert N. Anthony and James S. Reece.

Reviews the book "More Debits Than Credits: The Burnt Investor's Guide to Financial Statements," by Abraham J. Briloff.

Reviews the book "Certified Public Accounting: A Sociologist View of a Profession in Change," by Paul D. Montagna.

Introduction, 245. — Model I — the framework, 250. — Model I — rivalry and speed of innovation, 253. — Model II — the framework. 255. — Model II — rivalry and speed of innovation, 256. — Summary and conclusions, 258. — Appendix, 259.

In this paper some effects of nonstationary parameters upon inferences and decisions in portfolio analysis are investigated. A Bayesian inferential model with nonstationary parameters is presented and is applied to the problem of portfolio choice. For this model, nonstationarity 1) implies greater uncertainty about future returns; 2) implies that in forecasting future returns, recent returns should receive more weight than not-so-recent returns; 3) restricts the amount of information that can be obtained about future values of the parameters of interest; 4) shifts investment among risky securities and from risky securities to risk-free securities; and 5) yields optimal portfolios with smaller expected returns than corresponding optimal portfolios in the stationary case.