Earnings announcements, Disclosures, Timeliness, Stock price reaction

[This research investigates the degree to which the superiority of analysts' earnings forecasts (relative to a univariate time-series model) is associated with certain firm characteristics. The analysts' information advantage is characterized as being related to private information-gathering incentives, and to the amount of information disseminated about the firm. The objective is to determine whether analyst forecast superiority is related to firm characteristics not examined in previous research. Specifically, the investigation relates the analyst advantage over a time-series model to past earnings variability and the extent of coverage in The Wall Street Journal. Statistical controls were employed for the market value of the firm's common stock, the firm's number of lines of business, and the time lapse between the end of the previous fiscal quarter and the release of the earnings forecast. The methods of data analysis consist of estimating OLS regressions, heteroscedasticity-consistent estimators, and bootstrapping techniques. The results indicate, first, that the analyst advantage in forecast accuracy over a time-series model is materially related to the historical variability in the earnings time series. Second, no positive relation is evident in our data between the analyst advantage and firm size, a result that is at variance with some previous research. Third, the analyst advantage is positively related to the amount of coverage in The Wall Street Journal Index, which is consistent with the intuitive notion of prior research that analysts' forecasts improve as more information becomes available. Finally, an attempt was made to ensure that the results were not caused by violations of classical regression assumptions. This was accomplished by explicitly correcting for a nonconstant variance, and by allowing for cross-correlation using bootstrapping. The asymptotic results are very similar to the bootstrapping results, but neither adjustment has altered the primary findings using OLS.]

This study presents evidence on whether analysts' earnings forecasts anticipate management's discretionary accruals choices. If analysts anticipate discretionary accruals, earnings forecast errors are composed of at least two parts: cash-flow and discretionary-accruals forecast errors. Management's bonus-maximizing incentives allow for identification of circumstances in which discretionary-accruals forecast errors are expected to offset cash-flow forecast errors and circumstances in which they are expected to exacerbate cash-flow errors. Controlling for the unexpected cash-flow variability, the empirical results are consistent with these predictions.

Financial statements summarize a firm's fiscal position using only a limited number of accounts. Readers often interpret financial statements in conjunction with other information, some of which may be aggregated in a different way (or not at all). This paper exploits properties of the double‐entry accounting system to provide a systematic approach to reconciling diverse financial data. The key is the ability to represent the double‐entry system by network flows and, thereby, access well‐recognized network optimization techniques. Two specific uses are investigated: the reconciliation of audit evidence with management‐prepared financial statements, and the creation of transaction‐level financial ratios.

In this paper, we embed the double entry accounting structure in a simple belief revision (estimation) problem. We ask the following question: Presented with a set of financial statements (and priors), what is the reader's “best guess” of the underlying transactions that generated these statements? Two properties of accounting information facilitate a particularly simple closed form solution to this estimation problem. First, accounting information is the outcome of a linear aggregation process. Second, the aggregation rule is double entry.

In this paper, we embed the double entry accounting structure in a simple belief revision (estimation) problem. We ask the following question: Presented with a set of financial statements (and priors), what is the reader's “best guess” of the underlying transactions that generated these statements? Two properties of accounting information facilitate a particularly simple closed form solution to this estimation problem. First, accounting information is the outcome of a linear aggregation process. Second, the aggregation rule is double entry.