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The purpose of this paper is to investigate the relationship between earnings management and chief executive officers’ (CEOs) compensation. Owing to the fact that earnings management does not have only opportunistic effects, but signaling effects, this study focuses on accruals quality to examine earnings management incentives. Thus, accruals quality is described against future cash flow. The empirical evidences suggest that a positive relationship between discretionary accruals and future cash flow provides predictive elements for earnings management, whereas a negative relationship between discretionary accruals and future cash implies to opportunistic elements for earnings management. Should there is no significant relationship between discretionary accruals and future cash flow, there will be no earnings management, and such a result suggests that incentives and managers’ performance in these firms differ.

The statistical population of this research consists of all listed companies on the Tehran Stock Exchange during 2009–2016. Panel data method is applied in order to estimate the research model.

Findings of the study show that there is no significant relationship between discretionary accruals and future cash flow in pharmaceutical and food industries, thus they have neither predictive nor opportunist earnings management, while the results evidence a negative significant relationship between discretionary accruals and future cash flow in machineries, automobile, mineral and chemical industries. Furthermore, it can be alleged that there is no significant difference between CEOs’ compensation in firms with opportunistic earnings management (OEM) and other types of earnings management. It shows that firms do not have appropriate plans for CEOs’ compensation. Moreover, the relationship between earnings management and stock return has been investigated in this study. We document that stock return is influenced by accruals quality and its components. In other words, stock return significantly differs in firms with OEM and firms without any kind of earnings management.

The authors’ findings provide contributions; for managers, it is noticeable that stock markets have sufficient comprehension about financial statements and the undertaken procedures on them, resulting in a higher return base on fair information. For investors and regulators, using the findings, may have deeper understanding to distinguish between industries that are recognized as opportunistic and non-opportunistic, which, in turn, results in better decision and regulation.

Previous studies have been mostly investigated OEM, while the current study examines both signaling and opportunistic aspects of earnings management.

Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whether the market rewards meeting or beating analyst earnings expectations (MBE).

The authors use an event study methodology to capture market reactions to MBE.

The authors document a stock return premium for beating analyst forecasts by a wide margin. However, there is no stock return premium for firms that meet or just beat analyst forecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts.

The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market's over-skepticism of earnings management being a plausible mechanism for this phenomenon.

The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers' earnings management.

The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

The purpose of this paper is to examine the comparative abilities of current period cash flows and earnings (and its components) to predict one‐year‐ahead cash flow from operations in Egypt.

The study uses the cash flow prediction models developed by Barth, Cram, and Nelson to examine the predictive abilities of earnings and cash flows for future cash flows. The first set of prediction models uses cross‐sectional regression to compare the predictive abilities of cash flows and aggregate earnings for one‐year‐ahead cash flow from operations. The second set of prediction models tests whether disaggregating earnings into cash flows and the major components of accruals enhances the predictive ability of earnings for one‐year‐ahead cash flow from operations.

The findings of the study reveal that aggregate earnings have superior predictive ability than cash flows for future cash flows. Also, the results reveal that disaggregating accruals into major components – changes in accounts receivable and payable, and in inventory, depreciation, amortization, and other accruals – significantly enhances predictive ability of earnings.

The study provides empirical evidence on the superiority of earnings in predicting future cash flows. The findings of the study should be considered in explaining the results of value relevance research Egypt. However, owing to relatively small sample size, given the thinness of the Egyptian capital market, these findings should be interpreted with caution.

The paper contributes to the limited body of research on the superiority of earnings and cash flows in predicting future cash flows by examining the predictive abilities of earnings and cash flows for future cash flows in Egypt as one of many emerging markets.

This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings.

The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables.

Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results.

This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

The purpose of this paper is to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The study uses the list of CRISIL NSE Index (CNX) Nifty Shariah Index companies as its sample for a period of 10 years for conducting the analysis. The study utilizes the cash flow prediction models to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows.

The study report that contrary to Financial Accounting Standard Board assertion, current cash flows have superior predictive ability of next period cash flows than current aggregate earnings in case of Shariah-compliant companies in India. The results further show that there are no gains from decomposing earnings into accruals and cash flows in predicting future cash flows. There is no increase in explanatory power (measured by adjusted R2) when aggregate earnings are disaggregated into accruals and cash flows to predict next period cash flows.

The empirical findings of the study will enable the Shariah compliant investors to understand the role of current earnings (and its components) and cash flows in predicting next period cash flows in case of Shariah-compliant companies in India.

To the best of author’s knowledge, this is the first study which examines the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The purpose of this paper is to summarize important information for managers, including research evidence related to the prevalence, legality, ethicality and other aspects of earnings management, to explain potential negative consequences of earnings management, and to offer suggestions for improved financial statement disclosure to enhance the transparency of financial reporting. We especially focus on income smoothing.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This study examines (1) the extent of key audit matters (KAMs) reported by auditors is related to accounting estimates, (2) whether measurement uncertainty and management bias affect auditors to do so and (3) whether the use of accounting estimates, given the measurement uncertainty and management bias reported in KAMs adversely affects the decision usefulness of accounting information.

Data on key audit matters, accounting estimates, measurement uncertainty, management bias, etc. were collected from the auditor's reports of 351 sample Chinese listed firms. It employs regression analyses to assess the hypotheses on issues affecting the report of these key audit matters and the impacts on the decision usefulness of accounting information.

Fair value and impairment loss estimations make up of 2.6 and 44.1% of the 606 KAMs identified, respectively. Measurement uncertainty is positively, while management bias is negatively, affecting auditors report KAMs related to accounting estimates. The use of accounting estimates in firms where their auditors reported the KAMs related to accounting estimates does not enhance the value and predictive relevance of reported earnings. The assurance works on, and reporting of, KAMs served as a “red flag” about the accounting estimates.

The use of accounting estimates does not always lead to enhanced decision-useful accounting information. Auditors, in their stewardship role, shall ensure that the measurement uncertainty issue is appropriately identified, addressed and verified. In addition, they shall provide an effective check-and-balance to the accounting discretion managers have in providing decision-useful information from opportunistic reporting.

This study examines the proposition that while the use of estimates can enhance the decision usefulness of accounting information, it can also induce measurement uncertainty and management bias into financial reporting.

The purpose of this paper is to determine the properties of analysts’ cash flows from operations (CFO) forecast generated for Australian listed firms as a productive activity, within the wider processes of financial disclosure in Australia.

Two categories of criteria are adopted: first, basic predictive statistical performance relative to a benchmark model and earnings forecasts; and second, relevance for equity pricing, as indicated by the market reaction to cash flow or forecast error reactions. The final sample comprised 2,138 observations between 2001 and 2016 and several regression models are estimated to determine the relative performance and market reaction.

Analysts’ consensus cash flow forecasts demonstrate poor predictive performance relative to earnings forecasts. Cash flow forecasts are typically naïve extensions of earnings forecasts. Furthermore, cash flow forecasts appear to be of minimal use for equity market participants in complementing earnings forecasts’ role in informing firms’ equity pricing.

While analysts’ earnings forecasts are useful for making predictions, the role of analysts’ cash flow forecasts in capital market functional efficiency appears quite limited.

This study is one of few that examines comparative usefulness of analysts’ earnings and cash flow forecasts and their predictive power using the Australian setting. Additionally, it enriches the sparse international literature on such forecasts.

This study aims to examine several hypotheses, in conjunction with fundamental accounting concepts, to explain variations in the explanatory power of earnings for returns.

The authors explore three factors for their impact on the explanatory power of earnings. First, the accounting period preceding the earnings report is characterized by distinct intratemporal subperiod behavior. Recognizing this intratemporal nonstationarity is hypothesized to increase the explanatory power of earnings. Second, disaggregation of earnings into operating components is hypothesized to increase the explanatory power of earnings. Moreover, joint consideration of these first two factors is investigated. Third, the authors hypothesize that recognizing fundamental accounting concepts such as timeliness, predictive value, objectivity and verifiability offer key insights into the explanatory power of earnings.

The authors explore a sample of firms with management forecasts, which yields natural intratemporal subperiods – preforecast, forecast and realization periods – to generate hypotheses rooted in fundamental accounting concepts. The empirical evidence shows that recognition of nonstationary intratemporal behavior and earnings disaggregation yields a significant increase in the explanatory power of earning for returns. These findings are linked to fundamental concepts of accounting information.

This study is unique as it examines the joint role of nonstationarity and disaggregation in assessing the information conveyed in earnings. Importantly, results on these factors are linked to fundamental accounting concepts of timeliness, predictive value, objectivity and verifiability, along with their inherent trade-offs.