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The purpose of this paper is to focus on the moderating effect of CEO's accounting-based attributes on the relationship between chief executive officer (CEO) overconfidence and forecast accuracy in European companies.

Data from a sample of 347 European firms listed on Stoxx Europe 600 index from 2005 to 2018 are used to test the moderation model using moderation regression analysis.

Evidence reveals that CEO overconfidence is negatively associated with forecast accuracy. Further, CEO's accounting-based attributes significantly moderates the impact of CEO overconfidence on forecast quality.

This study is unique in providing European evidence for the moderating effect of CEO's accounting-based attributes on the relationship between CEO overconfidence and forecast quality. This paper is also relevant as it addresses the interaction between two sciences (psychology) to explain the forecast accuracy (accounting).

The purpose of this study is to present an extension to the research area dealing with the Tunisia initial public offering (IPO) associated earnings management forecasts, by an examination of the corporate governance mechanisms and earnings forecast accuracy relating impacts.

The authors use a multiple regression technique (FGLS) to estimate the effect of corporate governance structures and audit quality on earnings forecast accuracy. A sample of 33 IPO companies (165 firm-year observations) collected over the period ranging between 2011 and 2015 was applied.

The finding of this study reveals that the companies displaying a respectable audit committee size have a significant level of earnings forecast accuracy. Similarly, the accuracy level associated with IPO earnings forecasts is positively influenced by the use of the brand-name auditor.

This study is based on a small sample from a single jurisdiction and limited time period. In fact, the findings examine how financial statements are measured and reported and assess additional regulation to protect investors and understand as well as manage earnings forecast accuracy in IPO prospectuses.

The findings of the study provide some implications for regulators, financial analysts, investors and users of financial statements, particularly who are investigating in potentially IPO firms. This study has an implication for market regulators who suggest that a requirement to publish very detailed forecast information would improve market efficiency by reducing the forecast error.

Previous studies on this subject carried out in other countries with a regulatory framework differ from that of Tunisia, which obligatorily obliges the publication of the forecasts in the prospectus of IPO and capital increase. This is one of the most important studies that simultaneously tests the impacts of corporate governance and audit quality on earnings forecast accuracy in an emerging market, and the results of this study may give strength to Tunisian as well as other developing countries.

The purpose of this study is to examine the effect of International Financial Reporting Standards (IFRS) on earnings quality in a continental European context (i.e. France) more than a decade after their mandatory adoption. Furthermore, the authors investigate whether the IFRS effect depends on firm-specific incentives.

The authors construct an aggregated measure that considers the main qualitative information characteristics: reliability and relevance. They identify accruals quality, earnings smoothing and the degree of conditional conservatism as attributes of reliability and use earnings persistence, predictability, value relevance and timeliness to measure earnings relevance. To test the hypotheses, the authors use a sample of French listed companies. The analyses are based on ordinary least squares (OLS) fixed effects, the Newey–West estimator and the difference-in-difference approach. The authors also use cluster analysis to identify firms with high incentives for earnings quality.

The results reveal a decrease in earnings quality that persisted for a decade after IFRS adoption. This decrease is mainly due to a decline in earnings relevance, suggesting that the fair value principle worsened earnings volatility. However, the results show that there is an improvement in earnings reliability after IFRS adoption, suggesting that the international standards were able to constrain managerial opportunism. Additionally, the findings reveal that firm-specific incentives can enhance the positive effect of IFRS, but the incentives are not able to substitute for such effect.

The IFRS effect depends on firm-specific incentives.

The authors prove that firm-specific incentives are important to accentuate the positive effect of IFRS on earnings reliability and to mitigate the impact of IFRS on earnings relevance.

This paper makes several contributions to the literature. First, it addresses the relative lack of attention to the main qualitative characteristics in measuring earnings quality, that is, earnings reliability and earning relevance, and uses an aggregate earnings quality measure. Second, this paper uses a cluster analysis to highlight the role of firm-specific incentives in shaping the effect of IFRS on earnings quality.

Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.

The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.

The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.

This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.

This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

The objective of this research is to examine empirically the effects of digital maturity (DM) on the firm's financial performance as measured by return on assets (ROA), return on equity (ROE) and Tobin's Q.

The authors use a panel data sample of 92 observations collected from 23 listed firms on Sweden's stock exchange over four years, 2015–2018. The authors hand collect DM from the digital leader's reports and collect financial data from DataStream. Using both static and dynamic panel (generalized method of moments (GMM) estimation) regression models to perform endogeneity problem, the authors explore the impact of the DM index on ROA, ROE and Q of Tobin.

The results show that DM has a negative effect on ROA and ROE but a positive effect on Q of Tobin. This negative relationship can be explained, by the fact that information technology (IT) investment and the DM could take years to be materialized and to be captured by performance indicators. Company investment in IT will increase and basically the ROA will be negatively affected because the higher value of IT assets is not amortized. Nevertheless, in the long term, company can maximize its performance. The positive effect on Q of Tobin captures the long-run effect of digital transformation.

This research can be helpful for firms in their process of digital transformation to succeed with the change, create value and to understand the challenges they have to face. In the short term, firms undertaking digital transformation will face some financial difficulties which affect negatively their ROA and ROE, but in the long term they can maximize their performance (captured by Tobin’s Q) and improve their market value.

In previous research, the impact of digital transformation on performance has been measured in terms of revenue growth, profit margins and in terms of earnings before interest and taxes (EBIT). Even if the authors have sufficient evidence of the positive effect of digital transformation on organizational performance, there is no support of the positive effect on financial performance. So, the authors try to fill this gap. This research has also the merit of examining this relationship empirically through a dynamic panel data estimation two-step system GMM, while the majority of previous studies are qualitative in nature based on interviews and questionnaires or simple correlations.

The paper analyzes downside and upside risk spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

By using VAR-ADCC models and conditional value at risk (CoVaR) techniques, downside and upside risk spillovers between stock markets of G7 countries and China are analyzed before and during the COVID-19 pandemic.

The results suggested existence of a significant and asymmetrical two-way risk transmission between majority of pair markets, but the degree of asymmetry differs according to the use of the entire cumulative distributions or distribution tails. Downside and upside risk spillovers are significantly larger before the COVID-19 pandemic in all cases except between CAC 40/DAX and S&P/SSE pairs.

The paper used CoVaR and delta-CoVaR to investigate the downside and upside spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.