We examine whether firms with greater financial statement complexity are more likely to meet or beat analysts’ earnings expectations. We proxy for financial statement complexity using the firm's industry and year adjusted accounting policy disclosure length. Firms with more complex financial statements are more likely to just beat expectations than just miss expectations. Firms with complex financial statements appear to use expectations management to beat expectations, but do not use earnings management. Corroborating these findings, we find analysts rely more on management guidance for more complex firms. Firms with complex financial statements are also more likely to have analysts exclude items from actual “street earnings,” but tests suggest this strategy is not specifically used by complex firms to beat expectations. The effect we document is specific to analyst forecasts and not to other alternative benchmarks.

We specify measures of accounting consistency both across time and across firms based on the textual similarity of accounting policy footnotes disclosed in 10-K filings. We first examine how these measures relate to earnings quality. Accounting consistency over time is positively associated with a number of earnings quality proxies, including earnings persistence, predictability, accrual quality, and absolute discretionary accruals. We also find that lower consistency relative to other firms in the industry is associated with larger absolute accrual model residuals. Finally, we examine the information processing effects of accounting consistency. We find that greater accounting consistency in the time-series and the cross-section is associated with lower information asymmetry, as proxied by bid-ask spread and illiquidity. Greater cross-sectional consistency is also associated with greater analyst coverage, more accurate analyst forecasts, decreased dispersion in analyst forecasts, and stronger stock return synchronicity. Data Availability: The accounting consistency measures developed in this study are available upon request. All other data are available from the sources cited in the text.

We examine firm decisions to provide listings of sell-side analyst coverage on corporate investor relations (IR) websites. These listings are related to three major areas of financial research—voluntary disclosure, investor relations, and analysts. Our hand-collected data permit cross-sectional and time-series analyses. Firms are more likely to have such listings when analysts are more important information intermediaries and when firms are directly involved in managing their IR websites. For firms with listings, the probability of an analyst being included on the listing is increasing in firm awareness of and familiarity with the analyst, how active and favorable the analyst is, and the analyst’s reputation. Additional analysis indicates similar results across self-hosting versus third-party hosting IR websites, with a notable exception that self-hosting firms exhibit a stronger preference for analysts who issue more favorable research about the firm. Decisions to add or drop analysts from listings reinforce the main results. Data Availability: Data are available from public sources identified in the text.

The within-year walkdown of analysts' earnings forecasts has largely been attributed to analysts' incentives to curry favor with managers. We appeal to cognitive psychology literature on motivated reasoning and propose that forecasting difficulty interacts with such incentives to yield the observed walkdown. Higher forecasting difficulty generates a wider range of outcomes from which analysts can justify optimistically biased forecasts. In regression analyses, we find that the interaction between analysts' incentives for optimism and difficulty exhibits the strongest effect on earnings walkdowns. We also examine revenue forecasts as a benchmark of lower forecasting difficulty and find that revenue walkdowns are relatively diminutive. However, when analysts forecast losses, revenue forecasts are more critical and exhibit markedly steeper walkdowns. Our results suggest that analyst forecast walkdowns are better characterized by an interactive effect between analysts' strategic incentives for optimism and forecasting difficulty. JEL Classifications: G17; M41. Data Availability: Data are available from public sources identified in the text.

We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts’ earnings forecasts and find the algorithm’s predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm’s predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm’s use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs.