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This paper aims to examine the effect of audit quality of consolidated financial statements on the accuracy of analysts’ earnings forecasts from the viewpoint of users of financial statements.

This paper investigates the effect of dependence on the work of other auditors on error in analysts’ earnings forecasts based on samples from 2011 to 2012 (the period since implementation of the International Financial Reporting Standards in Korea). In addition, this paper examines the effects of use of Big 4 auditors, use of auditors with industry expertise and the proportion of overseas subsidiaries in relation to all subsidiaries on the association between dependence on the work of other auditors and error in analysts’ earnings forecasts.

This paper finds a positive relation between dependence on the work of other auditors and error in analysts’ earnings forecasts, suggesting that more dependence on the work of other auditors decreases the quality of the audit of consolidated financial statements; thus, to the extent that low-quality audits decrease reporting reliability, analysts’ forecasts are less likely to be accurate. This paper also finds that the positive relationship between dependence on the work of other auditors and error in analysts’ earnings forecasts is weakened when the principal auditor is a Big 4 auditor or one with industry expertise, because such auditors provide higher-quality audit services. However, the positive relationship between dependence on the work of other auditors and error in analysts’ earnings forecasts is further strengthened in cases where the proportion of overseas subsidiaries to all subsidiaries is higher. These results suggest that the complexity of the consolidation process increases as the proportion of overseas subsidiaries increases.

The findings are useful in analyzing the effects of adoption of the New ISA, implemented in 2014, which does not allow the division of audit responsibilities between principal auditors and other auditors. This paper also provides insights for regulators and practitioners to improve the auditor appointment system in the future.

It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases.

Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes.

Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias.

This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

In capital markets research, analysts’ consensus forecasts are widely used as a proxy for unobservable market earnings expectation. However, they measure the market earnings expectation with error that may vary cross-sectionally, as the market does not consistently rely on analysts’ consensus forecasts to form earnings expectation (Walther, 1997). Based on this notion, this paper aims to relate the prediction of future stock returns to the cross-sectional variation of the error in measuring market earnings expectation embedded in analysts’ consensus forecasts.

This study uses empirical analyses based on stock returns and annual analysts’ consensus forecasts.

Based on the analytical work by Abarbanell et al. (1995), this study reports that when the measurement error in annual analysts’ consensus forecasts is the smallest, forward earnings-to-price ratio (constructed with annual analysts’ consensus forecasts) best explains future stock returns, and the forward earnings-to-price ratio-based investment strategy is the most profitable.

Findings of this study are useful to capital markets research that relies on the market earnings expectation and to practitioners seeking more profitable investment strategies.

The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors.

Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors.

Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus.

These refinements were not presented in prior literature and can be useful to financial analysts and investors.

The purpose of this study is to examine whether and how analysts’ foreign ancestral origins would have an effect on analysts’ earning forecasts in particular and ultimately on firms’ information environment in general.

By inferring analysts’ ancestral countries based on their surnames, this study empirically examines whether analysts’ ancestral countries affect their earnings forecast errors.

Using novel data on analysts’ foreign ancestral origins from more than 110 countries, this study finds that relative to analysts with common American surnames, analysts with common foreign surnames tend to have higher earnings forecast errors. The positive relation between analyst foreign surnames and earnings forecast errors is more likely to be observed for African-American analysts and analysts whose ancestry countries are geographically apart from the USA. In contrast, this study finds that when analysts’ foreign countries of ancestry are aligned with that of the CEOs, analysts exhibit lower earnings forecast errors relative to analysts with common American surnames. More importantly, the results show that firms followed by more analysts with foreign surnames tend to exhibit higher earnings forecast errors.

Taken together, findings of this study are consistent with the conjecture that geographical, social and ethnical proximity between managers and analysts affect firms’ information environment. Therefore, this study contributes to the determinants of analysts’ earnings forecast errors and adds to the literature on firms’ information environment.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

The purpose of this paper is to examine whether corporate social media information helps improve analysts’ forecast accuracy.

This study uses hand-collected information on S&P 500 firms’ official Facebook pages and uses posts and reactions to such posts to measure corporate Facebook information. Multivariate regression models are estimated to test the relationship between analysts’ forecast accuracy and corporate Facebook information.

The results indicate that analysts forecast accuracy is unresponsive to posts. However, analyst forecast errors are decreasing in reactions to posts. These findings are robust to the inclusion of control variables, firm and time fixed effects, and alternative specifications of forecast errors and different pre-forecast time windows.

This study has some limitations. It focuses only on the S&P 500 firms, which are large and generally provide better information to the market. The sample period coincides with the early period of the corporate Facebook culture. However, more recent data sets are likely to provide stronger results.

The findings of this study provide support for “information generation” role of social media and show that reactions to corporate Facebook posts are the new and unique information generated from corporate social media activities, which help information intermediaries in improving their forecasting accuracy.

This study makes an important contribution to the literature by separating the information dissemination role of social media from information generation role and establishes the first evidence on how corporate social media information affects forecast accuracy of financial analysts.

The purpose of this paper is to revisit the question of whether analysts anticipate accruals’ predicted reversals (or persistence) of future earnings. Prior evidence documents that analysts who provide information to investors are over optimistic about firms with high working capital (WC) accruals. The authors propose that empirical models using WC accruals alone may be incomplete and hence not entirely appropriate to assess the level of analysts’ understanding of accruals. The authors argue that analysts’ optimism about WC accruals might not be due to their lack of sophistication, but rather driven by incomplete accrual information embedded in forecast accuracy tests.

The authors use non-financial US firms for the period between 1976 and 2013. The authors define earnings forecast errors as the analysts’ consensus earnings forecasts minus the actual earnings provided by IBES deflated by share price from CRSP. The authors carry out forecast error regressions on individual accrual components by decomposing total accruals into categories. The authors perform the tests across 12 months starting from the initial analysts’ forecasts, which are generally issued in the first month after the prior period earnings announcement date. The final sample contains 48,142 firm–year observations per month.

The empirical tests show no correlation between analysts’ forecast errors and revised total accruals. The findings are robust to different samples, periods, model specifications, decile ranked accruals, high accruals, absolute forecast errors, controlling for cash flows (CF) and high accounting conservatism. The findings imply that if analysts are to achieve more accurate forecasts, they should be considering all rather than some accrual components. The authors interpret this evidence as an indication of analysts’ relative sophistication with respect to accruals.

The authors recognise that analysts’ correct anticipation of accruals’ persistence does not mean that their earnings forecasts are entirely free of bias. Analysts can make forecast errors for various reasons including strategic biases. For instance, the tests show pessimistic forecast errors with respect to CF, which is in line with similar findings in prior research (Drake and Myers, 2011). Hence, the authors suggest that future research examine this correlation in greater depth as CF components are with the highest level of persistence, and hence should be predicted most accurately.

The results imply that the argument about analysts’ lack of sophistication with respect to accruals’ persistence is not warranted. The results imply that forecasts appear to contribute to market efficiency. Another implication is that analysts seem to utilise all relevant accrual information in their forecasts, hence traditional accrual definition should be revised in future studies. Key inferences of the paper imply that the growing use of analysts’ reports by institutional investors and money managers in their decision-making processes is justified despite the debate in the prior literature on the role and the reputation of analysts as surrogates of market expectations.

The research sheds a new light on the question whether sell-side security analysts are able to anticipate the persistence of accruals in future earnings.

Financial analysts are frequently viewed as information intermediaries who process and interpret firms' financial reports for other market participants. Much recent research, however, has cast doubts on analysts' ability to fully utilize the information in firms' financial reports. Using an alternative approach, this study aims to provide evidence on how sophisticated analysts are at using information in firms' financial reports.

The paper estimates different measures of firms' operational efficiency, all of which are derived from financial statement data, and compares the strength of the association between these measures and analysts' absolute forecast errors. It then compares a sophisticated frontier‐based measure of firms' operational efficiency that evaluates firms' performance relative to their competitors with three more traditional efficiency measures; specifically the return on asset (ROA) ratio, industry‐adjusted ROA, and the return on equity ratio.

The results indicate that the more sophisticated frontier‐based measure is more strongly negatively associated with analysts' absolute forecast errors than the other three measures. The results thus suggest that analysts are capable of undertaking a sophisticated analysis of the information in firms' financial reports, at least as it pertains to operational efficiency.

To the extent that analysts serve as a key group of users of financial information, these results are likely to be of interest to accounting policy makers.

This study aims to determine whether financial statement users suffered a significant loss of information when, in November 2007, the SEC dropped the requirement for foreign private issuers using International Financial Reporting Standards (“IFRS firms”) to reconcile their financial statements to US generally accepted accounting principles (GAAP).

The study investigates whether analyst forecast errors and forecast dispersion increased for IFRS firms to a greater extent than for US GAAP firms after the Securities and Exchange Commission (SEC) dropped the reconciliation requirement. Using a treatment group comprised of IFRS firms and a matched sample of US GAAP firms, this study uses regression analyses to compare forecast errors and dispersion for the last fiscal year the reconciliation was available and the first fiscal year during which the reconciliation was unavailable to analysts.

The study finds evidence that forecast errors for IFRS firms exhibited no systematic change after the reconciliation was no longer available for analysts covering those firms. Thus, it does not appear that dropping the reconciliation requirement was associated with a change in forecast accuracy. However, the study does find evidence of increased dispersion in the IFRS firms’ forecasts relative to their US GAAP counterparts after the reconciliation requirement was dropped.

These findings have implications for evaluating the Securities and Exchange Commission’s 2007 decision to eliminate the reconciliation for IFRS firms. Specifically, the Securities and Exchange Commission’s decision does not appear to have significantly altered analysts’ information environments.

This paper contributes to the understanding of how a group of sophisticated financial statement users adapt to different sets of accounting standards.