Knowledge that Transforms

In this study I propose and test a model that predicts individual analyst forecasts of corporate earnings per share (EPS) using the change in the mean consensus forecast of other analysts since the date of the analyst's current outstanding forecast; the deviation of the analyst's current forecast from the consensus forecast; and cumulative stock returns since the date of the analyst's current forecast. I find that these three variables explain about 38% of the variability in analyst forecast revisions. While there is evidence of a relation between changes in earnings expectations and price changes, virtually all of the explanatory power of my model arises from other analyst forecasts. Section 2 describes the data bases used and the sample selection process. Section 3 presents the model and method for predicting individual analyst forecasts. Section 4 reports the bias and accuracy of the predicted forecasts. Conclusions are in section 5.

The purpose of this paper is to investigate whether financial analysts with superior earnings forecasting ability can be distinguished on the basis of ex post forecast accuracy. I explore the question by estimating and comparing average accuracy across individuals, and by considering whether the observed distribution of analyst forecast accuracies differs from the distribution expected if their relative performances each year were purely random. Overall, I do not find systematic differences in forecast accuracy across individuals. Financial press coverage suggests there are superior financial analysts. For example, Institutional Investor's annual All American Research Team includes analysts rated by money managers as superior on a variety of criteria, including earnings forecasting, ability to pick stocks, and the quality of written reports. Clearly, financial analyst services other than forecast accuracy are valued by their clients. I focus on only one activity, earnings forecasting, for two reasons. First, forecast data are available, quantitative, and can be evaluated against observable earnings outcomes. Services such as insightful, well-written research reports are harder to evaluate quantitatively. Second, academic use of analyst forecasts as earnings expectations data in capital markets empir-

Auditing, Audit risk, Accounting errors, Strategic audit risk

Taxation, Tax complexity, Tax-reporting positions

Ramesh Chandra, Shane Moriarity, G. Lee Willinger, A Reexamination of the Power of Alternative Return-Generating Models and the Effect of Accounting for Cross-Sectional Dependencies in Event Studies, Journal of Accounting Research, Vol. 28, No. 2 (Autumn, 1990), pp. 398-408

Laureen A. Maines, The Effect of Forecast Redundancy on Judgments of a Consensus Forecast's Expected Accuracy, Journal of Accounting Research, Vol. 28, Studies on Judgment Issues in Accounting and Auditing (1990), pp. 29-47

Michael Gibbins, Alan Richardson, John Waterhouse, The Management of Corporate Financial Disclosure: Opportunism, Ritualism, Policies, and Processes, Journal of Accounting Research, Vol. 28, No. 1 (Spring, 1990), pp. 121-143

Agency model, Signaling, Auditing, Audit fee

Debt covenants, Financial distress, Covenant default, Shareholders

Understanding the role of accounting in intrafirm resource allocation problems' requires understanding its substitutes. If accounting is viewed as information production arising in response to inefficiencies caused by information voids,2 then substitutes for accounting are alternative ways to reduce these inefficiencies. The purpose of this paper is to explore the power of commitment to reduce inefficiencies in a repeated intrafirm resource allocation problem. In this way, we hope to take a step toward understanding commitment as a substitute for accounting. We build a model with inefficiencies due to information voids by assuming an owner of an investment project, who knows only a distri-