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This paper documents the effect of different types of information on the value of financial analysts.

The authors use the pooled OLS regression and the data of nonfinancial firms from France to test our hypotheses. The data covers the period between 1997 and 2019.

The results show that analysts are more likely to cover those firms that incorporated greater proportion of market-wide information in their prices. Consistent with the economies of scale view, the authors argue that analysts specialize in the interpretation market-wide information. By doing so, they are able to cover relatively large number of firms simultaneously. The results also show that the value of analyst coverage (measured as the impact of analyst coverage on firm value, probability of stock price crash and probability of stock price jump) is a function of the extent to which different types of information are incorporated in prices. The authors’ results suggest that the impact of analyst coverage on firm value and on probability of crash is less pronounced in firms that incorporate greater proportion of market-wide information. In case of probability of jump, the results show that the impact of analyst coverage is more pronounced firms that incorporate greater proportion of market-wide information.

The major contribution of this paper is to document the impact of different types of information on the extent of analyst coverage. Furthermore, this paper also uses various measures (the impact of analyst coverage on firm value, probability of stock price crash and probability of stock price jump) to show how different types of information affects the value of analyst coverage.

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 investigate the impact of the development of high-speed rail (HSR) network on earnings management, especially on the trade-off between the usage of accruals-based earnings management (AM) and real earnings management (RM) techniques, and consequently, examines the extent to which the HSR network–earnings management nexus is moderated by governance and religion factors.

Using a sample of Chinese A-listed firms over an 11-year period, this study uses regression techniques as the baseline methodology while controlling for industry and year-fixed effects. The authors also use endogeneity tests (including instrumental variable method, Generalized Methods of Moments estimation and difference-in-difference) and different robustness checks.

The key findings are threefold. First, the HSR network development reduces AM. This suggests that the presence of HSR network is effective in reducing information asymmetry. Second, the use of RM technique increases with the HSR network development. This indicates that managers do not seem to engage in less earnings management with the HSR network development but instead appear to switch from the easy-to-detect AM to the more costly RM approach. Finally, the HSR network and earnings management nexus is moderated by governance and religion factors.

This study provides new evidence on the trade-off between AM and RM by managers and pioneers in examining the impacts of governance and religion factors on the relationship between the HSR network and the trade-off of earnings management techniques.

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.

As cross-cultural mergers and acquisitions (M&A) have learning effects on organisations, assessing their impacts on corporate performance is crucial. This study aims to explore the impact of inter-firm cultural differences on long-term post-M&A stock market performance.

The authors select domestic M&A transactions of Chinese listed companies during 2010–2021 as the sample. Then, the authors use the partial least squares structural equation model (PLS-SEM) to construct the latent variable of cultural differences in four dimensions to explore long-term stock market performance.

Cultural differences first positively and then negatively impact post-M&A performance. Three transmissions mechanisms are identified: investor sentiment, takeover premiums and information disclosure quality. Further analysis reveals that acquirer stock performance improves with higher analyst coverage and non-local shareholders but worsens if there are business affiliations between the acquirer and target firms.

This study can help optimise information disclosure systems in M&A transactions for regulatory authorities and aid investors’ understanding of post-M&A performance changes. Furthermore, it can improve acquirers’ understanding of the risks and opportunities in cross-cultural M&A, thereby facilitating the adaptation of management practices to the im-pacts of cultural differences.

By integrating the theories of resource dependence and transaction costs, this study examines the reversal effect of cultural differences between merging companies on post-M&A performance. The authors use a PLS-SEM to empirically analyse the main effects and reveal three transmission mechanisms.

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.

Unlike other types of corporate disclosure, corporate political disclosure (CPD), which is the disclosure of corporate political contributions and the related governing policies and oversight mechanisms, does not provide completely new information to stakeholders. Some of the information disclosed in CPD is available from other public records (e.g. the Federal Election Committee website or OpenSecrets website). Given this unique feature of CPD, it is interesting to investigate the cost and benefit tradeoff for firms of altering their CPD practice in response to policy and political uncertainty.

This study employs recently developed indexes of aggregate economic policy uncertainty (EPU) and a novel dataset of CPD transparency to examine the impact of EPU on CPD transparency and how the proprietary cost of corporate political activities moderates this association. The sample consists of S&P 500 companies from the 2012 to 2019 period.

The authors document that firms mitigate the heightened information asymmetry associated with higher aggregate EPU by increasing CPD transparency. The positive association between EPU and CPD is less pronounced for firms that are more sensitive to EPU, for firms that more actively manage EPU through corporate political contributions or lobbying activities and for firms that are followed by more analysts. The authors also find that more transparent CPD helps to mitigate the information asymmetry caused by heightened EPU. This study’s results hold when the authors control for other types of voluntary corporate disclosure.

This study contributes to the emerging literature on the determinants of CPD transparency by identifying EPU's positive impact on CPD transparency. This study also provides empirical evidence that the proprietary costs arising from the controversial nature of corporate political activities dampen firms' incentives to provide transparent CPD in response to heightened EPU, and that information on corporate political activities gathered and processed by financial analysts seems to lower the marginal benefit to companies of publicizing CPD on their own website.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

This article examines the link between the structure of the board of directors and target price accuracy using a sample of 51 listed firms on the Tunisian Stock Exchange over the period of 2011–2017.

In this study, the authors used the generalised method of moments (GMM) model to control the endogeneity problem.

As a result, that model can serve as a signal in the forecasting process. The authors' results suggest that target price accuracy is negatively related to board independence, and dual Chief Executive officer (CEO). In addition, CEO compensation tends to exert a negative impact on target price error.

The authors' findings are valuable for common investors because the findings can be useful in enhancing their capital allocation decisions by assigning higher weights to forecasts issued by firms with strong corporate governance systems. The authors' study also has practical implications for managers and policymakers. Specifically, the evidence provided herein suggests that firms with strong corporate governance mechanisms enhance the accuracy of market expectations, alleviate information asymmetry, and limit market surprises, especially in a context characterised by weak investor protection. The authors' results highlight the advantages of strong corporate governance in improving a firm's information environment and, therefore, are useful for the cost–benefit analysis of improving internal governance mechanisms. Additionally, the authors' results may prove useful to investors who can rely on the information provided by analysts for well-governed companies.

The authors' study contributes to the literature in both corporate governance and analysts' forecasts fields. The study provides additional evidence of the benefit of board quality attributes on target price accuracy in an emerging market characterised by high information asymmetry and weak investor protection. The authors' findings exhibit the effectiveness of board attributes in producing better financial information quality in Tunisia. This is useful for investors who may improve their capital allocation decisions by assigning greater weights to target price forecasts of companies with good governance quality, suggesting that good corporate governance is a credible signal of better financial information quality. These results have important implications for capital market regulators and corporate management in encouraging the implementation of good governance practices.

The authors attempted to assess whether corporate governance of listed firms are priced in the Tunisian context characterised by weak governance control and to highlight which mechanism is highly considered by independent financial analysts to build their forecasts.

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.