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Motivated by significant global developments affecting the sell-side industry, in particular a shift toward passive investments and growing regulation, this study examines whether financial analyst coverage declined over the past decade and if any loss of analyst coverage is associated with a change in forecast accuracy.

After investigating, and confirming, a general decline in analyst following, the authors calculate the loss of analyst coverage relative to the firm-specific maximum between 2009 and 2013. In multivariate analyses, the authors then examine whether this loss of coverage differs across geographic region, firm size and capital market development, and whether it is associated with consensus analyst accuracy.

Results indicate that between 2011 and 2021, firm-specific analyst coverage globally declined 17.8%, while the decline in the EU was an even greater, 28.5%. Within the EU, results are most pronounced for small-cap firms. As a consequence of the loss of coverage, the authors observe a global decline in forecast accuracy, with EU small-cap firms and firms domiciled in EU non-developed capital markets faring the worst.

This study is the first to document a concerning global decline in analyst coverage over the past decade. The study results provide broad-based empirical support for anecdotal reports that smaller firms in the EU and those in EU non-developed capital markets bear the brunt of consequences stemming from changes in the sell-side analyst industry.

The purpose of this study is to examine whether financial markets value a firm’s specific corporate environmental performance (CEP), i.e. its energy efficiency. This study also investigates the mechanism through which energy efficiency is associated with firm value.

For the empirical study, a sample of 324 US-listed non-financial firms during the period 2006–2019 was accessed from Thomson Reuters Refinitiv. Using baseline ordinary least squares regression models, this study first estimates the association between energy efficiency and firm value. It then tests the role of analyst coverage (the number of sell-side financial analysts following the firm) in ascertaining the value relevance of energy efficiency. To ensure the robustness of the results, alternative estimations including endogeneity and sample bias correctness tests were performed.

The study shows that energy efficiency is associated with firm value, and the role of analyst coverage as an external corporate governance mechanism is positive and significant on the value relevance of energy efficiency. Furthermore, this study documents that the relationship is shaped by sustainability-related internal and external risks, indicating that financial analysts’ role becomes more imperative when firms are subject to high scrutiny.

This study contributes to the literature by examining the intersections of energy efficiency, analyst coverage and firm value. It attempts to demonstrate how and why CEP and financial performance are linked. In the context of growing environmental concerns, the pressure of climate change and achievement of net-zero carbon emissions, this study provides valuable insights into the financial market wherein firms’ environmentally responsible behaviours are value-enhancing, and governance mechanisms are impactful. This study suggests that financial analysts can serve as an effective external corporate governance mechanism.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

The authors provide a general framework of behavior under asymmetric information and develop indices of diligence, objectivity and quality by an analyst and analyst firm about a studied firm, and relate them to the accuracy of its forecasts. The authors test the associations of these indices with time.

The test of Public Information versus Non-Public Information Models provides the index of diligence, which equals one minus the p-value of the Hausman Specification Test of Ordinary Least Squares (OLS) versus Two Stage Least Squares (2SLS). The test of Objectivity versus Non-Objectivity Models provides the index of objectivity, which equals the p-value of the Wald Test of zero coefficients versus non-zero coefficients in 2SLS regression of the earnings forecast residual. The exponent of the negative of the standard deviation of the residuals of the analyst forecast regression equation provides the index of analytical quality. Each index asymptotically equals the Bayesian ex post probability, by the analyst and analyst firm about the studied firm, of the relevant behavior.

The authors find that ex post accuracy is a statistically and economically significant increasing function of the product of the indices of diligence, objectivity and quality by the analyst and analyst firm about the studied firm, which asymptotically equals the Bayesian ex post joint probability of diligence, objectivity and quality. The authors find that diligence, objectivity, quality and accuracy did not improve with time.

There has been no previous work done on the systematic and objective characterization and joint analysis of diligence, objectivity and quality of analyst forecasts by an analyst and analyst firm for a studied firm, and their relation with accuracy. This paper puts together the frontiers of various disciplines.

The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts.

The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach.

The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output.

This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014.

This study aims to contribute to the emerging debate about materiality with novel insights about the signaling effects related to the disclosure of environmental, social and governance (ESG) information using the guidelines released by the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB).

An empirical assessment using panel data analysis was built to evaluate the relationship between sustainability reporting standards and analysts’ forecast accuracy.

The analysis revealed that the proliferation of sustainability reports prepared on mandatory or voluntary basis mitigated the signaling effects related to the disclosure of ESG information by companies. Furthermore, the additional analysis conducted considering sustainability reporting quality and ESG performance revealed the existence of mixed effects on analysts’ forecasts accuracy. Therefore, the insights highlighted the need to consider a cautionary approach in evaluating the contribution of ESG data to financial evaluations.

The practical implications consist of identifying criticisms related to disclosing ESG information by listed companies. In detail, the analysis underlines the need to enhance reporting standards’ interoperability to support the development of more accurate analysis by investors and financial experts.

The analysis reveals increasing attention investors pay to socially responsible initiatives, confirming that financial markets consider sustainability reporting as a strategic driver to engage with stakeholders and investors.

This research represents one of the first attempts to explore differences between GRI and SASB using an empirical approach.

This paper offers a way of revivifying classical accounting research in the form of a pragmatist neoclassical programme with a sound epistemological underpinning.

The paper draws on a pragmatist perspective on financial accounting and accounting research springing from John Dewey's theory of inquiry.

Although a pragmatist underpinning does not entail specific methodological prescriptions, it can provide fruitful insights in research design. The paper discusses the structure and content of a research programme drawing on a pragmatist underpinning and sets out proposals for a practical research agenda. Although the agenda is shaped around the topic of identifiable intangibles, much of the paper has substantially wider relevance.

The approach justifies a revival in scholarly research employing classical methods and directed at improving accounting methods and standards.

The approach would promote closer engagement between scholarly accounting and practitioners such as standard-setters, making some contribution to closing the widely acknowledged gap between research and practice.

The paper offers a neoclassical programme of research drawing considerably more extensively on pragmatist philosophy than did theorisation in the classical period.

This study aims to examine the effects of product market competition (PMC) on analysts’ earnings forecast attributes, particularly forecast accuracy and dispersion. The authors also investigate whether investor protection moderates the relationship between PMC and forecast attributes.

The sample covers 49,578 firm-year observations from 38 countries. This study uses an ordinary least squares regression, a Heckman two-stage regression and an instrumental two-stage least squares regression.

This study finds that PMC is associated with higher forecast accuracy and lower dispersion. The results also show that investor protection enhances the effect of PMC on forecast accuracy and dispersion. These findings imply that countries with strong investor protection have a better information environment, as exhibited by the stronger relationship between PMC and analysts’ forecast properties.

The findings highlight the importance of strong governance mechanisms in both the country and industry environments. Policymakers, including government agencies and financial regulators, can leverage these insights to formulate regulations that promote competition, ensure investor protection and facilitate informed investment decisions.

This study advances our understanding of how PMC affects analysts’ earnings forecast attributes. In addition, it pioneers evidence of the moderating role of investor protection in the relationship between PMC and forecast attributes.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

Using a sample of oil and gas firms in the USA, the study examines the relation between the presence of hedging and annual report readability.

The authors use regression analysis to examine the relation between the presence of hedging and annual report readability.

The authors find that annual reports of firms with the use of hedging are less readable (i.e. difficult to read and understand). The authors also find that the primary results are more pronounced for firms with a higher level of business volatility.

The study contributes to the finance literature on the use and value of hedging and to the accounting literature on the determinants of annual report readability. The Securities and Exchange Commission (SEC) has persistently asked companies to improve the readability of their disclosures to stakeholders (SEC, 1998; 2013, 2014). Hence, the study not only identifies a potential determinant (i.e. hedging) that may influence the level of readability but also supports the current regulatory policy by the SEC, which is encouraging companies to improve readability.