Banks’ efforts to issue green bonds for climate mitigation can be hampered by governmental conduct through sovereign credit ratings (Adrian et al., 2024). The research problem concerns emerging market banks’ issuance challenges resulting from the nexus between sovereign credit ratings and bank credit ratings. The purpose of the research is to establish associations between the ratings and their impact on banks’ attempts to issue green bonds. The methodology is quantitative. Credit ratings for 57 banks that issued green bonds and sovereign credit ratings are collected from banks, S&P Global, Fitch, and Moody’s websites, respectively. Descriptive statistics, estimations, and regression methods are used to test hypotheses. Findings confirm hypotheses that 1) the majority of banks’ (88 percent) credit ratings mirror or are lower than the corresponding sovereign credit rating; 2) if sovereign ratings are non-investment, local banks’ credit ratings are also non-investment. A population estimate between 76 percent and 100 percent, a Pearson correlation coefficient of +0.91, and p = 0.026 < 0.05 support the hypotheses. In conclusion, the positive correlation indicates that a downgrade leads to increased financing costs, affecting banks’ green bond issuances (Li et al., 2020). The study’s relevance lies in drawing governments’ attention to safeguarding against sovereign rating deteriorations to enhance banks’ capacity to issue green bonds.
Sharia-compliant stocks have expanded beyond Muslim-majority markets and are increasingly sought by global investors who prioritize investment in ethical and faith-based financial instruments. Despite their similarity with conventional stocks, empirical and conceptual discussions on the determinants of Sharia stock performance, particularly in sector-specific contexts, remain limited. The current study proposes a conceptual framework for assessing Sharia-compliant stock investment decisions, specifically in Indonesia’s telecommunications industry. Using a literature-based approach, our framework integrates macroeconomic indicators, information and communication technologies (ICT) development, and firm-specific financial variables to identify relevant factors for Sharia stock return analysis. The targeted telecommunication sector was selected due to its close relationship with digital infrastructure and ICT development and is included in the permissible investment sector by Sharia law. Our study presents a preliminary conceptual model that can guide future empirical testing. At the same time presenting different perspective regarding Sharia-compliant stock studies such as Zuliansyah et al. (2023) and Handyansyah and Sukarno (2023). Furthermore, the proposed framework contributes to the literature by organizing potential determinants of Sharia stock performance within Indonesia’s telecommunication sector. Future studies can refer to our framework to validate it and assess its predictive capacity, model reliability, and practical applicability.
The pharmaceutical industry in Jordan faces increasing challenges related to global environmental issues, such as pollution, resource depletion, and climate change. This highlights the need for effective risk management to ensure sustainable environmental development within this vital sector. This study aims to explore the impact of risk management on achieving environmental sustainability in the Jordanian pharmaceutical industry, focusing on the role of various risk management components in improving environmental performance and reducing negative environmental impacts. The study employed a descriptive-analytical approach. Data were collected from a sample of 139 employees holding risk management positions in Jordanian pharmaceutical companies. The data were analyzed using Statistical Package for the Social Sciences version 26 to establish statistical relationships between the variables. The results showed a clear application of risk management practices and a strong correlation between its components (risk identification, assessment, treatment, and review) and environmental sustainability factors such as resource efficiency, pollution reduction, and climate change mitigation. Risk treatment emerged as the most influential factor. The study recommended strengthening environmental monitoring systems and adopting modern sensing technologies. The study provides a practical framework to support decision-makers in adopting modern strategies that promote environmental compliance and achieve sustainable development.
This study examines the operational workflow and governance structure of Islamic donation-based crowdfunding, focusing on its alignment with Shariah principles and ethical financial practices. Islamic donation-based crowdfunding provides a faith-based mechanism for charitable contributions through digital platforms. Employing a qualitative document analysis approach, this research identifies four key stages in the end-to-end campaign process: project application, fundraising, post-campaign reporting, and fund disbursement. The findings show that campaign initiators begin by submitting proposals for platform approval, followed by public fundraising. Upon project completion, initiators are required to report on fund utilisation and outcomes. A notable feature of most platforms is that funds are disbursed directly to beneficiaries or service providers, rather than to project owners, reinforcing the principles of trust (amanah) and public benefit (maslahah) in Islamic finance. However, the study also finds inconsistencies in verification procedures and post-campaign accountability across platforms. It aligns with concerns raised by previous studies regarding the need for enhanced governance mechanisms. These variations highlight the need for a more standardized governance structure. In conclusion, the study highlights the need for a unified, Shariah-compliant framework to enhance transparency, build donor confidence, and uphold the objectives of Islamic Law (maqasid al-Shariah), particularly justice (‘adl), social responsibility (mas’uliyyah), and social welfare.
The ability to accurately forecast default risk for a personal loan is a critical factor for managing credit risks. In this context, developing an adequate risk assessment methodology for borrowers is especially important for emerging markets with diverse borrower features and behavior. Hence, the following paper aims to investigate the potential for applying machine learning approaches to improving default risk predictions for individual loans in Vietnam over 2012–2022. For the empirical analysis, the authors used a sample of 6,034 observations of retail loans to compare the performance of logistic regression (LR), k-nearest neighbors (KNN), artificial neural networks (ANNs), and random forest (RF). The methodological framework involves data preprocessing, balancing classes, optimization of parameters, and validation through the use of accuracy, precision, sensitivity, specificity, and F1 scores. As a result, RF demonstrates superior prediction performance with an accuracy of 97.56 percent, precision of 0.988, sensitivity of 0.987, specificity of 0.992, and F1 score of 0.987. Important predictors include months since last delinquency, credit history length, maximum credit limit, and open accounts count.
The study examines the economic efficiency of central bank digital currencies (CBDCs) and the risks associated with them. This study uses empirical data to determine whether CBDCs improve the efficiency of financial transactions and to identify the risk differences. Using staggered difference-in-differences (sDiD) panel regression, we indirectly measure retail CBDC impact via transaction costs and wholesale CBDC pilot impact via interbank interest rate spreads. Robustness checks use gross domestic product (GDP) weighted models. Overall, we found that the introduction of a retail CBDC significantly reduces transaction costs by 0.79–0.80 percent (p < 0.001). Wholesale CBDC pilot programs show weak significance (p < 0.10), with a slight increase in spreads of 0.21 percent, likely due to high initial costs and limited scope. In other words, retail CBDCs enhance efficiency, while wholesale pilots have not yet realized expected gains. This study is one of the few cross-country quantitative analyses examining the effectiveness of CBDCs and helps to address the lack of empirical evidence regarding the benefits and risks of CBDCs, as highlighted by Auer et al. (2021) and Bindseil (2020).
The study aims to analyze the factors affecting the probability of bad debt exceeding the threshold at listed joint-stock commercial banks in Vietnam during the period 2012–2024. The study examines the relationship between the probability of bad debt exceeding the threshold (Lyra et al., 2015) and independent variables such as credit growth rate, bank size, business efficiency, liquidity, and macroeconomic variables such as inflation rate, economic growth, and money supply by using a binary logit model. The results show that rapid credit growth, low business efficiency, and poor liquidity are factors that significantly increase the probability of bad debt exceeding the threshold. On the contrary, large size and high profitability have an impact on reducing this probability. The study provides important empirical evidence for bank managers in controlling credit risks and contributing to ensuring the safety of the banking and financial system in Vietnam (Chang et al., 2025). The study contributes to the application of the logit model to predict the possibility of bad debt exceeding the threshold, while clearly identifying key factors to enable banks to enhance early warning and control credit risks more effectively.
This study investigates investor herding dynamics in high-risk markets by examining the Pakistan Stock Exchange (PSX) during periods of extreme terrorism and political instability. Using the state-space model by Hwang and Salmon (2004), we apply Kalman filter estimation to isolate unobservable sentiment-driven herding from fundamental-based behaviour. The data, spanning January 2000 to April 2016, capture a complete cycle of prolonged security shocks. Our findings demonstrate that herding in the PSX is statistically significant, persistent, and predominantly disconnected from short-term market fundamentals. During peaks of terrorism, investors increasingly rely on fundamentals, weakening sentiment-driven herding. Conversely, in stable periods, consensus-driven herding intensifies. Crucially, the lessons extracted from this terrorism-affected market cycle provide a vital behavioural benchmark for understanding investor psychology during contemporary global uncertainties, such as the COVID-19 pandemic and recent geopolitical supply chain disruptions (Rashid et al., 2022). These insights advance behavioural finance literature by contextualizing extreme security conditions and offer actionable regulatory strategies for emerging markets navigating severe exogenous shocks.
Portfolio allocation in rapidly changing markets requires frameworks that can adapt to time-varying expected returns and covariance structures. This study develops and evaluates a dynamic Bayesian Black–Litterman (DBBL) model that extends the traditional Black–Litterman framework through recursive Bayesian updating, dynamic covariance estimation, and LSTM-generated return views. Using 11 U.S.-listed assets (AAPL, MSFT, AMZN, GOOG, TSLA, JNJ, JPM, NVDA, META, XOM, and GLD) and daily data from 2015 to 2025, the DBBL model is compared with Markowitz and static Black–Litterman benchmarks. The results show that DBBL provides a modest improvement in Sharpe Ratio, indicating better risk-adjusted efficiency. However, this gain is accompanied by lower cumulative returns and deeper maximum drawdown relative to the comparator models, highlighting a clear trade-off between adaptive risk management and absolute portfolio performance. These findings suggest that DBBL should be interpreted as a risk-aware adaptive allocation framework rather than a uniformly superior portfolio solution, and they underscore the importance of balancing responsiveness and stability in dynamic portfolio construction.
Based on data from 29 Vietnamese commercial banks over the period 2011–2024, this study investigates the impact of financial technology (fintech) development on bank profitability, with a particular focus on the moderating role of bank size. The article uses a two-step generalized method of moments (GMM) estimator. The findings reveal that the proliferation of fintech companies and the emergence of innovative business models have intensified competitive pressure, thereby exerting a negative impact on the profitability of traditional banks. This result can be explained by declines in some banking services due to market-share losses to emerging fintech companies, as well as by the increased cost of investing in technological infrastructure, which has driven up bank operating expenses. However, larger banks, benefiting from greater scale, reputation, and financial strength, have shown more resilience by investing in digital transformation or forming strategic alliances with fintech firms, mitigating the adverse effects on profitability. In contrast, smaller banks, with more limited resources and technological capabilities, experience a more pronounced negative impact, highlighting a significant disparity in adaptive capacity amid the era of financial digitalization. The results imply that banks need to accept the trade-off of short-term profits for technology investment costs, aiming for a long-term, stable increase in earnings in the future.
Standard asset pricing models often fail to capture acute tail risks and asymmetric volatility in financial markets, particularly in weaker economies. This study investigates whether the volatility risk premium (VRP) can predict fat-tail risks and asymmetric tendencies in emerging and developed markets. Employing conditional value-at-risk (CoVaR), VaR-regression, Baba, Engle, Kraft, and Kroner generalized autoregressive conditional heteroskedasticity (BEKK-GARCH), and dynamic conditional correlation (DCC) frameworks, realized volatility was bifurcated into good (positive) and bad (negative) components. Findings reveal that bad volatility drives systemic and individual risks at nearly twice the rate of good volatility. Emerging markets exhibit persistent, integrated GARCH (IGARCH)-like volatility, whereas developed markets remain mean-reverting. Models incorporating VRP significantly outperform GARCH-type models in out-of-sample forecasts, showing a 25–30% predictive improvement for emerging markets versus 19–20% for developed markets. By identifying impending tail risks missed by historical data, the VRP and asymmetric volatility elements are essential for enhancing macro-stability policies and portfolio risk management in structurally precarious, highly sensitive markets.
This article examines the impact of ‘reasons for’ and ‘reasons against’ factors on the adoption of robo-advisors (RAs) in the Saudi financial services sector. The limited empirical evidence on behaviour drivers and resistance factors that affect the adoption of digital financial innovations (Mishra, Bansal, & Maurya, 2023) is to be addressed within the framework of the behavioural reasoning theory (BRT) (Claudy et al., 2015). The data of 1366 Saudi customers have been analysed using a multi-stage stratified sampling approach. Confirmatory factor analysis (CFA), structural equation modelling (SEM), and artificial neural networks (ANN) have been used for analysis. Our analysis reveals that factors related to ‘reasons for’ and ‘reasons against’ affect RAs adoption. The results indicate that compatibility (COMP) and openness to change (OC) have a significant impact on attitude (ATT) and behavioural intention (BEI), whereas relative advantage (REA) has a direct impact on behavioural intention. Additionally, openness to change and compatibility affect adoption motivations and attitudes toward RAs. The findings highlight the need to analyse both pro-adoption and anti-adoption aspects in marketing strategy. The research has valuable implications for financial service providers and policymakers who are interested in boosting the uptake of digital financial services.
This study examines the spillover effects of global Bitcoin (BTC), price volatility on Vietnamese stock and gold markets using daily data from 2018 to 2025 (Akpan, 2024; Umoru et al., 2025). Building on a baseline framework of ordinary least squares (OLS), fixed-effects, and instrumental variable regressions with lagged Bitcoin returns, we analyze how global Bitcoin price movements co-move with the Ho Chi Minh Stock Exchange index (VNINDEX), Hanoi Stock Exchange (HNX), and VND-denominated gold returns after controlling for USD/VND exchange rate, United States (US) 10-year treasury yields, volatility index (VIX), and turnover volatility. To capture event-driven dynamics, we further incorporate dummy variables and interaction terms around major crypto-specific events (such as regulatory announcements and large market corrections), allowing us to test whether spillovers intensify during stress periods (Umoru et al., 2025). The results indicated that although preliminary estimates suggest positive comovements, these effects weaken after robustness checks, and no significant or stable spillover from Bitcoin to Vietnamese stock or gold markets is detected. These findings challenge the view of gold as a safe haven in the Vietnamese context and point to limited diversification benefits when combining Bitcoin, stocks, and gold in domestic portfolios. Overall, the study contributes to the emerging literature on cryptocurrency spillovers in frontier markets and provides actionable insights for investors and regulators concerned with systemic risk and portfolio risk management (Chutipat et al., 2023).
The papers in this issue reinforce a clear direction in contemporary corporate governance and risk research, marking a shift toward a dynamic governance style in which operational risk management systems interact with disclosure regimes, regulation, and market discipline.
This study investigates the influence of behavioral biases, specifically overconfidence, herding behavior, and regret aversion, on investment decisions among treasury dealers in Islamic banks in Indonesia, and examines the moderating role of Islamic ethical values in these relationships. Drawing on behavioral finance theories, particularly prospect theory, which explains how cognitive biases distort decision-making under uncertainty (Kahneman & Tversky, 1979), this study adopts a quantitative explanatory approach. Data were collected through a structured survey of 67 treasury dealers and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that overconfidence and herding behavior have no significant effect on investment decisions, while regret aversion has a significant negative impact, indicating that fear of making wrong choices reduces rationality and decision quality. Although Islamic ethical values do not significantly moderate the effects of behavioral biases, they have a positive and direct influence on investment decisions, reinforcing ethical judgment and compliance with Shariah principles. These findings contribute to behavioral finance by showing that Islamic ethical values function as an internal moral framework that enhances rationality and reduces behavioral distortions in Islamic financial decision-making.
The rapid digital transformation of business operations presents a significant challenge for corporate governance, particularly in the complex and opaque domain of tax strategy. This systematic review follows PRISMA guidelines to critically analyze the role of corporate governance mechanisms in developing tax avoidance and tax risk in the context of the current phase of rapid digital transformation. The research problem stems from fragmented evidence on governance-tax relationships as digital technologies fundamentally alter corporate information environments and decision-making processes. The purpose is to synthesize evidence on how governance mechanisms, including board characteristics, ownership structure, executive compensation, and external monitoring, influence tax outcomes in digitally transforming organizations. The methodology employed a systematic search of Scopus and Web of Science, yielding 96 peer-reviewed articles following Tranfield et al.’s (2003) guidelines. Findings reveal that while board independence and expertise moderate tax aggressiveness, governance effectiveness is increasingly mediated by digital technologies. Consistent with Jensen and Meckling’s (1976) agency theory framework, significant theoretical gaps persist regarding digital transformation’s impact. The review concludes that future research must integrate technological factors and alternative theoretical perspectives. This synthesis guides regulators, practitioners, and academics navigating the complex intersection of governance, tax behavior, and digital transformation.
This paper examines how stock prices of Italian football clubs react to unexpected match outcomes, focusing on Juventus, Lazio, and Roma over the 2013–2014 to 2018–2019 football seasons. According to market efficiency theory, price adjustments should reflect only the “surprise” component of match results — that is, the deviation between the actual outcome and its ex-ante expectation. Using betting odds to proxy market expectations, we show that surprises exert a significant and immediate influence on stock prices, which is largely incorporated into opening prices on the first trading day after the match. By analysing both open and close prices, our findings indicate that some irregularities appear at market opening but tend to be corrected during the trading day, suggesting partial but not complete market efficiency. We also document asymmetric effects across clubs and explore whether rival-team results influence price adjustments. Overall, the results highlight the central role of expectation formation and surprise in shaping stock market reactions to sporting events.
This research explores the demand for digital enhancements in microfinance services in Bangladesh, focusing on developing a credit scoring model using logistic regression. The study highlights the transformation potential of digitization in improving the accessibility, efficiency, and scalability of microfinance services for the unbanked and underbanked population. Through a comprehensive literature review and primary data collected from borrowers, the research identifies key borrower characteristics influencing loan performance and repayment behavior. Employing a quantitative methodology, the study develops a practical, data-driven credit scoring model that supports digital loan evaluation and issuance in microfinance institutions (MFIs). It further analyzes the challenges and opportunities associated with integrating digital tools into traditional microfinance systems. The findings emphasize the need for digital innovations to foster greater financial inclusion, offering policy recommendations for practitioners, policymakers, and development partners. The study concludes with implications for future research and the practical application of digital credit models in the microfinance sector. We recommend that microfinance providers and policymakers prioritize interoperable digital platforms, automated credit scoring, and climate-responsive product design to meet rising borrower demand for accessible, efficient, and resilient micro-banking services in emerging markets.
The study investigates the impact of Basel III capital regulations on bank lending to small and medium-sized enterprises (SMEs) in South Africa, distinguishing between SME retail and SME corporate. Using an autoregressive distributed lag (ARDL) model on a dataset spanning from January 2013 (2013M1) to December 2024 (2024M12), the findings show that Basel III capital requirements do not significantly constrain SME lending in both segments. In the long run, more profitable banks lend more, bank size supports SME retail lending, and economic confidence boosts credit supply to SMEs, while inflation adversely affects SME retail lending. In the short run, SME lending exhibits persistence, but profitability shocks temporarily reduce credit supply, and SME retail lending adjusts faster to equilibrium than SME corporate lending. The evidence suggests that banks should adopt targeted lending strategies that align with SMEs’ cash flow cycles to maintain consistent credit flow while safeguarding profitability. At the same time, regulators should prioritize SME lending incentives, inflation stability, and counter-cyclical credit mechanisms to ensure a consistent supply of SME funding, particularly during economic downturns.
This study investigates and measures the intangible internal resources of enterprises that meet the VRIN criteria (valuable, rare, inimitable, and non-substitutable) and their effect on the dynamic competitiveness (DC) of Vietnamese firms, grounded in the resource-based view (RBV) (Barney, 1991). A mixed-method approach was employed, utilizing qualitative analysis to develop a theoretical framework and quantitative analysis to test the model on a sample of 200 enterprises in the Vietnamese financial market. Using structural equation modeling (SEM), the research identifies three core dynamic resource factors—relationship quality (RQ), corporate reputation (CR), and orientation competitive (OC)—that significantly influence an enterprise’s competitive performance. These factors contribute approximately 46 percent, 32 percent, and 22 percent, respectively, to the explained variance in DC, highlighting the critical role of relational assets as noted in research regarding market responsiveness (Homburg et al., 2007). The findings suggest that enhancing these capabilities allows firms to capture greater market share and contribute to macroeconomic stability. This study offers strategic insights for managers and policymakers to prioritize these dynamic capabilities to sustain competitive advantages in a rapidly evolving financial market.
