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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.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

In this study, the authors investigate if analysts, as knowledgeable information intermediaries, can correctly identify bank corporate social responsibility (CSR) activities and can reliably transmit that information to investors. Hence, the authors specifically explore if analysts perceive and behave differentially in the presence of genuine bank CSR activities (strengths). The authors also analyze if financial markets differentially assess bank CSR strengths. The authors further explore the viability of focusing on analyst and financial markets to validate genuine bank CSR strengths.

The authors use COMPUSTAT and CRSP for firm and financial data, I/B/E/S for analyst reporting data and MCSI Research KLD for CSR data. The sample consists of 329 distinct banks and 2,525 bank-year observations from 2003 to 2016. The primary CSR score is the total number of CSR strengths less the total number of CSR concerns, across six of the seven dimensions for each firm in each year of the sample (Adjusted CSR Score). In addition, the authors estimate all the analyses with dis-aggregated measures of total CSR strengths and total CSR concerns (Adjusted Total Strength Score).

The authors find that analysts correctly distinguish and construe bank CSR strengths from CSR concerns. Specifically, bank CSR strengths increase analyst following and forecast accuracy, while decreasing analyst forecast dispersion. The authors further find that bank CSR strengths increase bank market returns. These results are reversed for bank CSR concerns. Additionally, the authors demonstrate that this method using knowledgeable intermediaries can help validate bank CSR strengths.

The sample is limited to US banks and financial markets. The regulatory and information environment is likely to be different from global or emerging markets. However, since banks in many countries aspire to emulate the US banks, these results would be a precursor of banking sectors conditions in emerging markets. Additionally, the availability of data limits the sample to a period that ends in 2016. To the extent that the importance of ESG and CSR concerns has increased in the intervening time, the results may not accurately reflect the current state of the market.

This investigation benefits researchers, customers, banking executives, regulators and activist groups. First, the authors show that in addition to customers, analysts and the financial markets appreciate bank CSR strengths. Second, despite sophisticated financial reporting by banks, analysts correctly distinguish and construe bank CSR strengths. Third, the authors demonstrate a method for bank marketing researchers to validate genuine bank CSR activity, as well as provide additional support for customer related bank CSR outcomes. Fourth, the findings highlight the importance for banks to have high-quality CSR reporting. This might be especially helpful to a bank rebuilding its reputation after a CSR failure. Finally, this investigation using US banks could serve as a precursor for future bank CSR research and help develop CSR reporting guidelines for banks in emerging economies.

This investigation benefits researchers, customers, banking executives, regulators and activist groups.

This investigation benefits researchers, customers, banking executives, regulators and activist groups. First, the authors show that in addition to customers, analysts and the financial market appreciates bank CSR strengths. Second, despite sophisticated financial reporting by banks, analysts correctly distinguish and construe bank CSR strengths. Third, the authors demonstrate a method for bank marketing researchers to validate genuine bank CSR activity, as well as provide additional support for customer related bank CSR outcomes. Fourth, the findings highlight the importance for banks to have high-quality CSR reporting. This might be especially helpful to a bank rebuilding its reputation after a CSR failure. Finally, this investigation using US banks could serve as a precursor for future bank CSR research and help develop CSR reporting guidelines for banks in emerging economies.

This study aims to investigate whether the cash flow forecasts (CFF) of analysts can disseminate valuable information to the information environments of companies.

The author uses empirical archival methodology to conduct differences-in-difference analyses.

It is found that information asymmetry decreases in the treatment group following the initiation of CFF during the postperiod, which is consistent with the hypothesis of this paper.

To the best of the author’s knowledge, this study is the first among the cash flow forecast studies to demonstrate the usefulness of CFF in the mitigation of information asymmetry, a friction that is widespread in capital markets.

This article analyzes the hypothesis that analysts issue higher long-term earnings growth (LTG) forecasts following a market-wide investor sentiment.

This study analyzed 193 publicly traded Brazilian firms listed on B3 (Brasil, Bolsa, Balcão), totaling 2,291 observations. To address the potential selection bias resulting from analysts' preference for more liquid firms, this study used the Heckman model in the analysis with samples with only one analyst and the entire sample. The study also applied other robustness tests to ensure the reliability of the findings.

The results suggest that market-wide investor sentiment influences LTG when the firm's stocks are difficult to value. Market optimism did not reflect five-year profit growth after the forecast issue, suggesting lower forecast accuracy during high investor sentiment values.

Volatile-earnings firms have relevant implications in LTG forecasts during bullish moments. According to the study’s evidence, investors' decisions and policymakers' and regulators' rules should consider analysts' expertise as independent information when considering LTG as input for valuation models, even under market optimism.

This paper contributes to the literature on the influence of investor sentiment on analysts' forecasts by incorporating two crucial elements in the discussion: the scenario free from herding behavior, as usually only one analyst issues LGT forecast for Brazilian firms, and the analysis of research hypotheses incorporates the difficulty of pricing a firm given the uncertainty of its earnings as an explanation to bullish forecast.

Financial reports are the basis of economic decisions that affect organizational interests and shareholders. However, there is a severe research gap concerning the factors affecting the quality of financial information (such as audit report readability and tone). Therefore, considering the importance of presenting high-quality financial information, this study aims to investigate the impact of intellectual capital (IC) and its components on the audit report's readability and tone.

The multivariate regression model tests research hypotheses. Then, hypotheses are tested via a sample of 824 observations of the listed companies on the Tehran Stock Exchange (103 companies) from 2014 to 2021, using the multivariate regression model based on pooled data and fixed effects.

Results determine that customer capital (CC) and structural capital (SC) are likely to influence the audit report tone positively. In general, the IC and human capital (HC) negatively impact auditors' tone. More analyses also document that IC and its CC, HC and SC components positively and significantly affect audit report readability based on two readability indices, including FOG and text length. Finally, findings pertaining to the third readability index (Flesch index) reveal that only HC and SC are robust based on this measurement, whereas the IC and CC have a negative and significant impact on the readability of auditors’ reports.

To the best of the authors’ knowledge, this study is the first to address this issue in emerging markets, and it provides helpful insights for users, analysts and legal institutions regarding IC, which significantly affects audit report readability and tone.

Integrated reporting enhances the meaningfulness of non-financial information, but whether this enhancement is progressive or regressive from a sustainability perspective is unknown. This study aims to examine the influence of the Integrated Reporting (<IR>) Framework on the disclosure of financial- and impact-material sustainability-related information in integrated reports.

Using a disclosure index constructed from the Global Reporting Initiative’s G4 Guidelines and UN Sustainable Development Goals, the authors content analysed integrated reports of 40 companies from the International Integrated Reporting Council’s Pilot Programme Business Network published between 2015 and 2017. The content analysis distinguished between financial- and impact-material sustainability-related information.

The extent of sustainability-related disclosures in integrated reports remained more or less constant over the study period. Impact-material disclosures were more prominent than financial material ones. Impact-material disclosures mainly related to environmental aspects, while labour practices-related disclosures were predominantly financially material. The balance between financially- and impact-material sustainability-related disclosures varied based on factors such as industry environmental sensitivity and country-specific characteristics, such as the country’s legal system and development status.

The paper presents a unique disclosure index to distinguish between financially- and impact-material sustainability-related disclosures. Researchers can use this disclosure index to critically examine the nature of sustainability-related disclosure in corporate reports.

This study offers an in-depth understanding of the influence of non-financial reporting frameworks, such as the <IR> Framework that uses a financial materiality perspective, on sustainability reporting. The findings reveal that the practical implementation of the <IR> Framework resulted in sustainability reporting outcomes that deviated from theoretical expectations. Exploring the materiality concept that underscores sustainability-related disclosures by companies using the <IR> Framework is useful for predicting the effects of adopting the Sustainability Disclosure Standards issued by the International Sustainability Standards Board, which also emphasises financial materiality.

Despite an emphasis on financial materiality in the <IR> Framework, companies continue to offer substantial impact-material information, implying the potential for companies to balance both financial and broader societal concerns in their reporting.

While prior research has delved into the practices of regulated integrated reporting, especially in the unique context of South Africa, this study focuses on voluntary adoption, attributing observed practices to intrinsic company motivations. To the best of the authors’ knowledge, it is the first study to explicitly explore the nature of materiality in sustainability-related disclosure. The research also introduces a nuanced understanding of contextual factors influencing sustainability reporting.

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

This paper aims to examine the relationship between the readability of annual reports and corporate performance in Chinese listed firms.

This research examined the annual report readability factors of Chinese listed companies by using a textual analysis method using Python to extract the text from the annual reports, convert it into numerical form to facilitate statistical analysis and then merge the results with data from the Chinese stock market to explain the impact on corporate performance and predict future earnings in the Chinese financial markets from 2008 to 2021.

Study findings indicate that firms with better financial reporting readability are more profitable, incur lower agency costs and have low earnings in the Chinese stock markets when readability is low (i.e. more complexity and length of annual reports). It was also found that when a listed company has a good performance, it prefers to use a short space to explain its operating and financial status. More generally, the means of the report length are short, and accounting terms are used less frequently; in the case of a poor company, the annual report is particularly long and accounting terms are more frequently used. In the context of the COVID-19 crisis, this study served as a proxy measure of returns prior to the announcement of the COVID-19 pandemic. In addition, an instrumental variable approach is used, which helps results to remain robust and control for fixed effects and potential endogeneity problems.

Although this study’s results cannot be generalised globally because of their limited scope, they can still be generalised across non-English speaking countries. Thus, future cross-country research is encouraged to examine the textual analysis of financial reports across those countries.

This study conveys two messages to investors and policymakers within the Chinese market. First, investors ought to pay greater attention to the nonfinancial information contained in annual reports to improve the accuracy of their predictions regarding future firm performance. Second, Chinese policymakers are encouraged to instate a policy for the use of plain English in annual reports to make them more readable by international investors.

This study contributes to the paucity of research that examines English-written annual reports in non-English speaking countries by examining the readability of annual reports in the Chinese market.

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.