This study aims to map the evolution of earnings persistence research, identify its intellectual, conceptual, and social structures, and examine its relationships with earnings quality, corporate governance, audit quality, and sustainability while proposing future research directions. A bibliometric approach was employed using the Scopus database. Following the PRISMA procedure, 482 records were identified, and 291 articles published between 2006 and 2026 were retained after applying timespan, document type, subject category, language, and journal filters. The dataset was analyzed using Bibliometrix/Biblioshiny through performance analysis, scientific mapping, and structured content analysis, including co word analysis, co citation, bibliographic coupling, and collaboration network analysis. The findings reveal increasing publication trends following IFRS adoption, with earnings quality remaining the dominant theme alongside growing attention to ESG, sustainability, and corporate governance. However, cross country collaboration and digital reporting remain underexplored. This study provides a comprehensive bibliometric overview and proposes future research directions for earnings persistence.

Attempts have been made in this research to forecast returns of exchange rates of foreign countries in relation to Naira using the SVM, Neural Network (NN), and Random Forest (RF) forecasting models. The Value-at-Risk and Expected Shortfall results demonstrate that exchange rate risks intensified significantly during the post-crisis period. USD/NGN exhibited the highest post-crisis tail risk under the Neural Network model, with VaR₉₉ and ES₉₉ values reaching 1.2211 and 1.3003 respectively, indicating extreme downside exposure and elevated currency market fragility. Similarly, EUR/NGN and CAD/NGN recorded heightened post-crisis risk levels, reflecting increased investor uncertainty and inflationary exchange rate pressures. By contrast, the RF model generated more moderate and economically plausible risk estimates, suggesting stronger robustness and stability in volatile emerging market environments. Graphical analyses corroborate these findings, showing that Neural Network forecasts produced explosive and exponential depreciation trajectories in the post-pandemic era, while RF forecasts exhibited smoother and more gradual adjustment paths consistent with managed exchange rate dynamics. The study found consistently higher post-crisis VaR and ES values across models signal rising tail risks, which imply potential for large currency swings. Such volatility could exacerbate macroeconomic fragility, increase the cost of external debt servicing, and drive inflationary pressures through more expensive imports. Across all models, the RF system consistently delivered superior predictive accuracy, forecast stability, and tail-risk moderation particularly during the crisis and post-crisis periods. In contrast, the NN model produced exponential post-crisis forecast trajectories especially for USD/NGN and EUR/NGN; highly sensitive to structural breaks and may exaggerate persistent volatility in crisis-prone economies. Though informative, such outputs tended to overshoot plausible devaluation trends for the Naira, likely due to exaggerated extrapolation of recent market behaviors. SVM forecasts showed modest error levels and smoother, more plausible trends, offering less extreme but significant signals of future currency devaluation. This reinforces the importance of hybrid modeling approaches and the integration of non-linear machine learning tools in forecasting into central banking operations, exchange rate surveillance frameworks, and investor risk assessment strategies under turbulent economic conditions to enhance resilience against future external shocks and currency market disruptions.

This paper examines the relationship between earnings management and earnings quality in two countries (93 companies from Tehran Stock Exchange and 92 companies from Saudi Arabia Stock Exchange) for the period (2013-2022 Tehran) and (2014-2023 Saudi Arabia). The data were collected as a year -firm and analyzed using multiple regression. The earnings quality was measured through three separate attributes (earnings predictability, earnings smoothness and relevance of earnings). To achieve the research goals, three hypotheses were developed and, in each hypothesis, the moderating role of one of the earnings quality indicators for each category of non-stressed and distressed companies were studied. The results of the research showed that in all cases of measuring the earnings quality, the earnings management in distressed and non-distressed firms are efficient. Similarly, earnings quality of earnings predictability type in the Tehran Stock Exchange and the earnings quality of relevance type in the Saudi Stock Exchange and the distressed firms, the earnings quality of relevance type in the Tehran Stock Exchange and the earnings quality of earnings smoothness type in the Saudi Stock Exchange can explain future profitability. Moreover, for the first time, the emphasis on the relationship between the attributes of the earnings quality and the type of earnings management and future profitability is introduced globally, especially in Saudi Arabia and Iran. By using international data, the comparison between the approach of Saudi Arabia and the approach of Iran will be done.

This article has explained the influence of selected financial indicators on banking growth by taking into consideration SBI, HDFC and HSBC. Thus, monthly log data has been considered over a period from 2005 to 2024. The study has considered the Cobb-Douglas production function as a model specification to examine the above issue. It has been found that IDR is an important financial indicator to justify the banking growth in relation to CAR, NPAs, PPE, RO Adv., ROA, ROE and ROI of SBI, HDFC and HSBC.

This paper examines the relationship between digital payment infrastructure (DPI), education spending, and government performance to produce sustainable development outcomes in a Sub-Saharan Africa (SSA). On panel data of Kenya, Nigeria, South Africa, Rwanda and Ghana over 2010-2022, the results of analysis use two-stage least squares (2SLS), fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and quantile regression methods to overcome the endogeneity, non-stationarity, and distributional heterogeneity. Findings indicate that DPI has a strong, positive and significant effect on the Sustainable Development Index among all estimators and quantiles, which support financial digitization as a structural cause of multidimensional development. The effectiveness of governance improves development based on the short-run dynamics and a distribution-specific effect, whereas government spending on education is always in the negative; this is due to the inefficiency, leakages in governance and long gestation lags and not necessarily the ineffectiveness of education. The internet penetration has negative conditional impacts, which explains the need to focus on digital finance rather than on an overall connection. The results highlight the fact that the outcomes of developing countries are not only determined by the distribution of resources but also the quality of institutions, their effectiveness in implementation, and the strategic targets of digitalization. The policy suggestions focus on digital financial inclusion, governance enhancement, education quality reforms, and integrated development plans.