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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.

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.

This paper aims to examine the extent to which sell-side analysts efficiently incorporate firms’ corporate social responsibility (CSR) activities into their earnings forecasts. In addition, this paper also investigate the CSR information efficiency of analysts vis-à-vis that of investors.

This paper measures CSR activities by using CSR strength and CSR concern scores from the Morgan Stanley Capital International Environmental, Social and Governance database. This paper uses analysts’ earnings forecast errors and dispersion as proxies for their information efficiency. To compare the CSR information efficiency of analysts to that of investors, this paper uses the Vt/Pt ratio, which is the equity value estimates inferred from analysts’ earnings forecasts (a proxy for analysts’ CSR information efficiency) to the stock price of the focal company (a proxy for investors’ CSR information efficiency).

The regression analysis indicates that analysts’ earnings forecasts are optimistically biased and more dispersed for firms with positive CSR activities. The paper also finds that analysts’ forecasts are more optimistically biased than investors in interpreting CSR activities.

The lack of standardized protocols in CSR reporting and activities has raised the risk of mispricing by analysts, threatening the stability of sustainable investments. This paper suggests that regulators and standard-setters should establish a uniform framework governing firms’ CSR activities, along with their reporting and measurement, to ensure more consistent and reliable evaluations of CSR practices.

Analysts’ mispricing of CSR activities may distort sustainable investing, as it can overly focus on the positive impacts of stakeholder theory, overlooking agency theory’s warnings about managerial self-interest. Investors need to assess CSR efforts with a dual perspective, acknowledging their societal value but also examining their alignment with shareholder interests.

To the best of the authors’ knowledge, this research is the first to assess the efficiency of analysts versus investors in processing CSR information amidst growing sustainable investment interests. Furthermore, building on Dhaliwal et al. (2012), which found that voluntary CSR disclosures correlate with more accurate analyst forecasts, this research provides fresh perspectives on the evolving nature of how analysts assimilate CSR information over time.

Findings in the literature indicate leading financial analysts attract high levels of market attention and provide more accurate earnings forecasts prior to becoming all-star analysts. Furthermore, these analysts significantly impact the investment decisions of other market participants and thus the market price of assets. Therefore, this study examines the information role of leading financial analysts and identifies two significant conclusions. First, the positive outcomes of these analyst leaders are more informative and attract more followers. Second, informational herding by followers of these analysts is not as naïve as suggested in previous studies, as followers who smartly use information from analyst leaders tend to perform better. We also find that analysts who practice smart learning by studying and selectively employing analyst-leader decisions achieve better career outcomes.

This paper aims to analyze the economic-financial performance (EFP) and value creation (VC) in the Brazilian construction industry.

Based on the theories of strategy and finance, a quantitative-qualitative, descriptive and explanatory and applied study was carried out, contrasting the performance of the Direcional company and the civil construction industry – both listed on the Brazilian Stock Exchange and the Over-the-Counter Market (B3)

The analysis of the EFP in the Brazilian construction industry shows that EZTEC, Helbor, Trisul and Direcional were the companies with the best EFP in the period. The analysis of the Economic Value Added (EVA®, henceforth EVA), as a VC metric and basis for assessing the relative technical efficiency score by Data Envelopment Analysis (DEA®, henceforth DEA), revealed that the companies Direcional, EZTEC, MRV and CR2 were considered efficient throughout the period covered. The multicriteria methodology for empirical testing of the EFP and VC allowed not only contrasts Direcional's results with the other companies of the construction industry but also offered a complementary tool for comparative analysis of enterprises of different sizes, structures and realities.

Regardless of any contextual limitations, from a theoretical point of view, the research not only helps fill the research gap aforementioned but also expands knowledge on the topic and demonstrates how this multi-criteria methodology (integrating DEA and EVA) can be used to assess EFP and VC in addition to traditional tools. However, this new approach evaluates, at the same time, corporate and sectorial effectiveness by contrasting the efficiency and efficacy (simultaneously) in the generation of performance and value of a company in relation to the industry.

Significant implications for managerial practice could be noted by offering a tool to improve company performance and creating a competitive benchmarking process for analysts, investors, managers, financing agencies, shareholders, policymakers and business owners, as well as organizations and sectors in similar situations – who need to assess the EFP and VC holistically and improve their decision-making processes.

The uniqueness and innovation of this research come from the original multi-criteria methodology developed, applied and validated for analysis of EFP and VC. This methodology was operationalized through DEA applied to the companies' EVA, making it possible to compare corporate results and those of the whole industry in a balanced way – an unexplored issue in the literature, especially in emerging economies, opening several avenues for future research.

Este artículo tiene como objetivo analizar el desempeño económico-financiero (DEF) y la creación de valor (CV) en la industria de la construcción brasileña.

Con base en las teorías de estrategia y finanzas, se realizó un estudio cuanti-cualitativo, descriptivo, explicativo y aplicado, contrastando el desempeño de la empresa Direcional y de la industria de la construcción civil, ambas cotizadas en la Bolsa y Mercado Extrabursátil Brasileña (B3).

El análisis de la DEF en la industria de la construcción brasileña muestra que EZTEC, Helbor, Trisul y la Direcional fueron las empresas con el mejor desempeño en el período. El análisis del Valor Económico Agregado (en adelante EVA), como métrica de CV y base para evaluar el puntaje de eficiencia técnica relativa mediante Análisis Envolvente de Datos (en adelante DEA), reveló que las empresas Direcional, EZTEC, MRV y la CR2 se consideraron eficientes durante todo el período cubierto. La metodología multicriterio para pruebas empíricas de la DEF y CV permitió no sólo contrastar los resultados de la Direcional con los de otras empresas del sector de la construcción, sino que también ofreció una herramienta complementaria para el análisis comparativo de empresas de diferentes tamaños, estructuras y realidades.

La singularidad y la innovación de esta investigación provienen de la metodología original multicriterio desarrollada, aplicada y validada para el análisis de DEF y CV. Esta metodología fue operacionalizada a través de DEA aplicado al EVA de las empresas, permitiendo comparar los resultados corporativos y los de toda la industria de manera equilibrada – un tema inexplorado en la literatura, especialmente en las economías emergentes, abriendo varias vías para futuras investigaciones.

Independientemente de las limitaciones contextuales, desde un punto de vista teórico, la investigación no solo ayuda a llenar el vacío mencionado anteriormente, sino que también amplía el conocimiento sobre el tema y demuestra cómo esta metodología multicriterio (integrando DEA y EVA) puede utilizarse para evaluar el DEF y CV además de las herramientas tradicionales. Sin embargo, este nuevo enfoque evalúa, al mismo tiempo, la efectividad corporativa y sectorial contrastando la eficiencia y eficacia (simultáneamente) en la generación de desempeño y valor de una empresa en relación con la industria.

Se podrían observar implicaciones significativas para la práctica gerencial al ofrecer una herramienta para mejorar el desempeño de la empresa y crear un proceso de evaluación comparativa competitivo para analistas, inversionistas, gerentes, agencias financieras, accionistas, formuladores de políticas y propietarios de negocios, así como organizaciones y sectores en situaciones similares, que necesitan evaluar el DEF y el CV de manera integral y mejorar sus procesos de toma de decisiones.