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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.

The aim of this paper is to document the effect of analyst coverage on the ex ante probability of stock price crash and the ex ante probability stock price jump.

This paper uses the data of non-financial firms from France to test the arguments presented in this paper during the period between 1997 and 2019. The paper also uses flexible quadrants copulas to compute the ex ante probabilities of crashes and jumps.

The results show that the extent of analyst coverage is positively associated with the ex ante probability of crash and negatively associated with the ex ante probability of jump. The results remain qualitatively the same after several sensitivity checks. The results also show that the relationship between the extent of analyst coverage and the probability of cash and the probability of jump holds when ex post probability of stock price crash and stock price jump is used.

Unlike most of the earlier papers on this topic, this paper uses the ex ante probability of crash and jump. This proxy is better suited than the ones used in the prior literature because it is a forward-looking measure.

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.

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.

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.

Institutional investors are major shareholders in publicly traded firms and play crucial roles in the financial and governance aspects of these firms. Despite their importance, little is known about their role in internal auditing. This study aims to fill this gap by investigating the relationship between institutional investors’ ownership and investment in the internal audit function (IAF).

The study uses ordinary least squares regressions with two-way cluster-robust standard errors (firm and year) to estimate the relationship between institutional investors’ ownership and investment in IAF for Malaysian listed firms between 2009 and 2020.

The findings show that companies with higher levels of institutional ownership invest more in IAF, suggesting that institutional investors can effectively monitor managers due to their large holdings. Moreover, both transient and dedicated institutional investors are more likely to invest in IAF.

The results highlight the importance of institutional investors as a significant determinant of investment in IAF, which can aid regulators and managers in understanding the institutional investors’ role in governing and optimizing the efficient use of a firm’s resources. The findings also provide insight into institutional investors’ behavior regarding monitoring systems, which may inspire regulators and policymakers to consider increasing institutional investors’ participation to enhance governance structures.

Sustainable development (SD) is widely acknowledged as the center around which all development efforts should revolve. Banking is a crucial component of SD, and the adoption of sustainable banking practices by various banking institutions is a powerful catalyst for its achievement. This paper aims to investigate the level of adoption of environmental, social and governance (ESG) indicators in India and the extent to which financial institutions use these strategies. In addition, the banks have been classified according to their sustainable banking performance and showing a relationship between ESG and sustainability.

An ESG framework has been developed for the Indian banking system that focuses on the behavior of banks. The evaluation of literature helps to identify the gaps in particular frameworks for analyzing sustainable banking practices in developing nations because of the variation in economic criteria between developed and developing countries. An attempt to construct a common framework for measuring the banking sector’s sustainable efforts has been done in the past. Specifically in India, where the social and environmental dimensions of sustainability are of equal importance to governance indicators, these studies fall short of providing relevant indicators. Multiple financial reports, nonfinancial reports, corporate social responsibility reports and business responsibility reports of this sector were analyzed using content analysis techniques against ESG indicators for sustainability attainment.

The result of this study shows that both the sectors are disclosing their environmental indicators more as compared to other dimensions. While the analysis says that private companies are going better than public companies in terms of disclosing their ESG indicators. As compared to the international banking sector, adoption of Global Reporting Initiatives standards, United Nations Environment Programme Financial Initiatives (UNEP FI), Green Credit Policy and Equator Principles (EP) is near to the ground in India. IDFC bank is the only entity that started implementing EP practices and Yes bank also is doing a wonderful implementation of the green policies and is the signatory to UNEP FI.

The current state of sustainable banking in India is reflected in the implementation of the proposed framework. To better integrate sustainability problems into banking, this study provides helpful information for banks and other stakeholders. In addition, this study corrects the lack of research in the Indian context on sustainable banking.

To the best of the authors’ knowledge by far, this is one of the prime studies to inspect the degree of ESG disclosure by the Indian banking sector in their sustainability report.

The purpose of this study is to examine herd behaviour under different market conditions, examine the potential impact of the firm size and stock characteristics on this relationship, and explore how herding affects market prices in the German market.

The authors apply a method that does not rely on theoretical models, thus eliminating the biases inherent in their application. This technique is based on the assumption that macro herding manifests itself in the synchronicity (comovement) of stock returns.

The study’s findings show that herding is more pronounced in down markets and is more pronounced when market returns reach extreme levels. Additionally, the authors have found that there is stronger herding among large companies compared to small companies, and that stock characteristics considered have no effect on the degree of macro herding. Results also suggest that the contemporaneous market-wide information drives macro herding and that macro herding facilitates the incorporation of market-wide information into prices.

The study’s results strongly support the idea of directional asymmetry, which holds that stocks react quickly to negative macroeconomic news while small stocks react slowly to positive macroeconomic news. Additionally, the study’s results suggest that the contemporaneous market-wide information drives macro herding and that macro herding facilitates the rapid incorporation of market-wide information into prices.

To the best of the researchers’ knowledge, this is the first study that examines macro herding for a major financial market using a herding measure based on the co-movement of returns that does not rely on theoretical models.