Elizabeth Chuk

Assessing methods of identifying management forecasts: CIG vs. researcher collected

This paper examines the characteristics of management forecasts available on Thomson First Call’s Company Issued Guidance (CIG) database relative to a sample of forecasts hand-collected through a search of company press releases. Due to the significantly lower cost of using CIG (relative to hand-collecting data), academics have increasingly relied on this database as a source of management forecasts. However, it is important for researchers to consider the properties of this database (such as coverage, accuracy, and breadth) when evaluating whether it is an appropriate data source for their study. Overall, our results suggest systematic differences between forecasts reported on CIG and forecasts gathered from company press releases. We suggest several sample criteria that will remove or mitigate these biases.

Earnings Precision and the Relations between Earnings and Returns

Formal and heuristic valuation models suggest that changes in firm value associated with a revision in expected earnings should be a multiple on the order of typical earnings capitalization factors between 10 and 30. However, empirical estimates of the earnings response coefficient (ERC) have usually been in a range an order of magnitude lower, between 1 and 3 (Kothari 2001). This paper uses a simple Bayesian model to integrate previous theoretical and empirical results and bridge this large gap. In the Bayesian model, low precision implies a low weight on new earnings information. As a result, the observable earnings surprise is much larger than the unobservable earnings revision, which in turn implies a smaller coefficient on earnings surprise than on the revision in expected earnings. Although the model uses statistical measures, the Bayesian concept of precision closely parallels the accounting concept of earnings quality, i.e., the extent to which current earnings is informative about expected future earnings. Precision summarizes the effects of multiple determinants of earnings quality.Consistent with the model, two proxies for precision, forecast dispersion and absolute magnitude of earnings surprise, explain a broad empirical range of coefficients on earnings surprise ranging from near 0 up to 30. We reconcile the surprisingly large proportion of observations falling in the upper end of the range with the surprisingly low estimated ERCs typically reported in previous research. Finally, we demonstrate in the appendix how the precision proxies can be used to design tests that focus on the majority of observations with larger surprise coefficients and thereby allow researchers to detect the effect of determinants of surprise coefficients that would otherwise be empirically undetectable.

What Have We Learned About Earnings Management? Integrating Discontinuity Evidence

Earnings distributions commonly exhibit statistically significant discontinuities at prominent performance benchmarks. Discontinuities at zero earnings are widely interpreted as evidence of earnings management to avoid a loss, and discontinuities at zero earnings change or zero earnings surprise as evidence of management to avoid an earnings decrease or negative earnings surprise, respectively. In contrast, two recent papers by Durtschi and Easton (2005, 2009, hereafter DE) assert that discontinuities are instead explained by some combination of prior researchers choice(s) of sample selection and scaling as well as a systematic relation between the sign of earnings and market prices. Resolution of the conflicting interpretations of discontinuities is important because 1) it affects how investors, regulators, and scholars view earnings management and 2) it demonstrates the importance of a close linkage between theory and research design choices. We evaluate the three alternative explanations proposed by DE. We point out that DE provide no evidence that their explanations create discontinuities, but only evidence showing that their modified research designs eliminate discontinuities. We also demonstrate why the research designs used by DE eliminate evidence of discontinuities when alternative designs using the same data identify highly significant discontinuities. Finally, we outline the key characteristics of the extensive body of evidence documenting discontinuities in earnings distributions that are consistent with the theory that earnings are managed but are generally inconsistent with artifactual theories of discontinuities.

Standards or Incentives: What Determines Financial Reporting Transparency for Defined Benefit Pension Assets?

We examine whether regulation intended to improve disclosure can itself lead to higher disclosure quality in the absence of a change in preparer incentives. We exploit a setting involving a sequence of two similar regulatory changes, which have one key difference – while both regulatory changes mandate improvements to disclosure (specifically, on pension asset allocation), only one removes preparer incentives to disclose opaquely (by eliminating a key reporting assumption – the expected rate of return on pension assets or ERR, which can be more effectively manipulated if asset allocation remains opaque). We construct two difference-in-difference (DD) research designs to examine the disclosure consequences of each of these changes mandating more transparent disclosures on pension assets ((i) a 2008 rule change under US GAAP and (ii) a 2011 rule change under IFRS), and examine the difference in disclosure outcomes between these two changes. We find that the IFRS disclosure standard, which also removes preparer incentives to obfuscate asset allocation, is effective at improving pension asset transparency as intended by the standard, whereas the US standard, which solely mandates better disclosure while leaving unchanged preparer incentives to disclose or obfuscate, is not as effective at improving pension asset transparency. Using a setting in which firms have a well-documented incentive for lower financial reporting quality (i.e., incentives to manipulate the ERR so as to lower pension expense and boost reported income), our findings reinforce the view in the “standards versus incentives” literature that accounting quality cannot be improved effectively with higher-quality standards alone; preparers’ incentives must work in concert with higher-quality standards to pull firms towards more transparent reporting.