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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 examines the ability of fundamental summary measure Pr to predict earnings change for the subsequent year, the association of Pr and stock returns, and the relationship between Pr and risk factors beta and size. Pr is a probability index generated by logistic model and financial statement data. Beta effect is minimized by grouping firms into beta portfolios while size is controlled through incorporating size as an independent variable in the regression models. Evidence from the study indicates that Pr has a strong ability to predict future earnings change and has a positive and significant association with adjusted market returns, after controlling for beta. Pr's association with adjusted market returns is mitigated when beta and size are controlled simultaneously.

The Securities Exchange Commission mandated eXtensible Business Reporting Language (XBRL) filing data provide immediate availability and easy accessibility for both academics and practitioners. To be useful, this data should provide information for decisions, specifically, investment decisions. The purpose of this study is to examine whether the XBRL database can be used with models, developed in previous studies, predicting the directional movement of earnings. The study does not attempt to examine the validity of these models, but only the ability to use the data in the analysis of financial statements based on these models.

The study analyzes New York Stock Exchange companies’ XBRL data using a two-step logistic regression model. The model is then used to arrive at the directional movement of earnings between current and subsequent quarters. Additional models are created by dividing the sample into industry membership.

The results classified companies as realizing an increase or a decrease in earnings. The final model indicated a significant ability to predict earnings changes, on average about 65 per cent of the time, for the entire model, and 71 per cent, for the industry-based models (higher than those of previous studies based on COMPUSTAT). The investment strategy created average quarterly return between 2.8 and 10.7 per cent.

The originality of this study is in the way it examines the quality of XBRL data, by examining whether findings from prior research which relied on traditional databases (such as COMPUSTAT) still hold using XBRL data. The use of XBRL allows not only easier and less-costly access to the data but also the ability to adjust the models almost immediately as current information is posted, thus providing a much more relevant tool for investors, especially small investors.

This paper seeks to investigate whether disaggregated bank earnings better predict next period earnings than contemporaneous aggregated earnings.

Fairfield et al.'s (1996) regression approach is used for predicting next period's return of equity (ROE) and stock prices using disaggregated earnings data.

The results show that the mean adjusted R‐square significantly increases with the progressive disaggregation of earnings. The results also demonstrate that disaggregated components are better able to predict next period earnings and stock prices than aggregated earnings.

The findings support the US Financial Accounting Standard Board's contention that disaggregated information may be more useful than aggregated information for investment, credit, and financing decisions.

Investors and analysts should use disaggregated income statement information in predicting next period earnings and stock prices for the banking industry.

The main contribution of this paper is to demonstrate how fully disaggregated earnings explain ROE, stock prices, and analysts forecast error in the banking industry.

The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.

Archival in nature using a combination of regression analysis and binomial tests.

The binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.

The results are generalizable to US firms and may not extend to non-US firms.

The TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.

Awareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.

Although a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

Financial analysts act as crucial conduits of information between firms and stakeholders. However, comparatively little is known about how these information intermediaries evaluate the believability and importance of corporate disclosures. We argue that a firm’s level of managerial discretion, or latitude of executive action, acts as a cue for financial analysts, which helps them interpret and respond to voluntary management earnings forecasts. Our study provides strong, robust evidence that financial analysts find management forecasts significantly less believable in low-discretion than in high-discretion environments, and therefore tend to be much less responsive to these forecasts. We also show that managerial discretion is especially impactful on analysts’ responses in those circumstances where analysts are typically most uncertain about how to interpret management forecasts.

The purpose of this study is to determine if an earnings forecasting model based on factors hypothesised to result in differential profits across firms (industries) reduces model error relative to the model developed by Ou (1990). Initial research attempting to forecast earnings found that the random walk model, where current year's earnings are the prediction for next year, provides the best forecast of annual earnings (Ball and Watts 1972; Foster 1973; Beaver, Kettler, and Scholes 1970; Albrecht, Lookabill, and McKeown 1977; Brealey 1969). Ou (1990) developed an earnings forecasting model using financial statement information beyond prior years' earnings as the explanatory variables that outperformed the random walk model in predicting annual earnings.

The purpose of this paper is to examine the impact of corporate governance on the capital market participants’ abilities to forecast future performance, as measured by the properties of analysts’ earnings forecasts in Asian stock markets.

This paper hypothesizes that higher corporate governance is associated with lower forecast errors, lower forecast dispersion and lower forecast revision volatility.

These predictions are supported with a sample of companies across eleven Asian economies over 2004-2012. The results of this paper suggest that corporate governance plays a significant role in the predictability of firm’s future performance and, therefore, improves the financial environment in Asian stock markets. Furthermore, the impact of corporate governance on analysts’ forecast properties is more pronounced in countries with strong investor protection.

The authors acknowledge the following limitations of this paper. First, the results of this paper may be subject to omitted-variable bias and endogeneity issue. The authors have used control variables in the regressions to reduce the omitted variable bias. The authors have run lead-lag regressions to address causality issue. Second, CLSA corporate governance scores are collected for largest companies in each jurisdiction. Therefore, the sample is biased towards the largest companies in those jurisdictions and may not be representative of the average firm in the Asia.

The results of this paper speak to the benefit of having strong corporate governance in terms of reducing the information asymmetry between investors and corporate management.

The purpose of this paper is to give a comprehensive review and synthesis of automated textual analysis of corporate disclosure to show how the accuracy of disclosure tone has been incremented with the evolution of developed automated methods that have been used to calculate tone in prior studies.

This study have conducted the survey on “automated textual analysis of corporate disclosure and its impact” by searching at Google Scholar and Scopus research database after the year 2000 to prepare the list of papers. After classifying the prior literature into a dictionary-based and machine learning-based approach, this study have again sub-classified those papers according to two other dimensions, namely, information sources of disclosure and the impact of tone on the market.

This study found literature on how value relevance of tone is varied with the use of different automated methods and using different information sources. This study also found literature on the impact of such tone on market. These are contributing to help investor’s decision-making and earnings and returns prediction by researchers. The literature survey shows that the research gap lies in the development of methodologies toward the calculation of tone more accurately. This study also mention how different information sources and methodologies can influence the change in disclosure tone for the same firm, which, in turn, may change market performance. The research gap also lies in finding the determinants of disclosure tone with large scale data.

After reviewing some papers based on automated textual analysis of corporate disclosure, this study shows how the accuracy of the result is incrementing according to the evolution of automated methodology. Apart from the methodological research gaps, this study also identify some other research gaps related to determinants (corporate governance, firm-level, macroeconomic factors, etc.) and transparency or credibility of disclosure which could stimulate new research agendas in the areas of automated textual analysis of corporate disclosure.

This study aims to provide insights into US and Australian analysts' views regarding the relative importance of disclosing the direct method (DM) or indirect method (IM) statement of cash flows and forecasting firm performance.

Evidence is collected from responses to 104 surveys and 52 interviews completed by US and Australian analysts from 2017 to 2022. The survey and interview questions are developed with reference to the literature.

US and Australian analysts believe that the DM format provides incremental benefits compared to the IM for (1) confirming the reliability of earnings; (2) improving earnings confidence; (3) more accurate ex ante forecasts of operating cash flow and earnings; and (4) identifying opportunistic accruals manipulation. Analysts view that DM disclosure can lower firm-level cost of equity, although US interviewees more uniformly expect lower costs of equity under DM disclosure when firms yield low earnings quality. DM disclosure is also more important during unstable economic periods, as proxied by COVID-19.

Limited research currently exists regarding disclosure of the DM or IM and its impact on analysts' forecasting accuracy, earnings quality, economic uncertainty and cost of equity. Previous research has relied on archival research to examine differences between the DM and IM methods and are limited by data availability. Our findings are particularly relevant to the US market with few US firms reporting the DM format.