Ariful Hoque

Purpose
This study investigates the impact of Environmental, Social, Governance (ESG) overall score and its pillars on firm risk and the mediating role of earnings management.

Design/methodology/approach
The research applies Generalised Method of Moments (GMM) regression to address endogeneity in a panel of Australian-listed firms from 2014 to 2023.

Findings
The findings reveal that higher ESG scores are associated with lower firm risk, with governance and social pillars exerting the most substantial immediate effects. In contrast, the environmental pillar demonstrates a delayed risk-reducing impact, reflecting long-term benefits rather than short-term volatility reduction. Moreover, the study identifies earnings management as a significant mediator that partially offsets ESG's stabilising effects, highlighting that firms with strong ESG practices are less likely to engage in accrual-based earnings management, thus reducing risk.

Practical implications
These findings have critical implications for investors, regulators, and policymakers. They underscore the importance of pillar-level ESG evaluation, long-term orientation in environmental assessments, and integrating financial transparency into ESG frameworks.

Originality/value
This study contributes to the extant knowledge of ESG overall and the individual pillar effect on firm risk in Australian companies, highlighting the mediating role of earnings management (EM). By identifying earnings management as a partial mediating mechanism, the study extends agency and stakeholder theories beyond direct ESG–firm risk association through the lens of financial reporting behaviour. This integrated framework bridges sustainability and earnings management literatures, offering a more comprehensive theoretical understanding of how ESG performance is related to firm risk.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.
We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39%, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.
Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

This study examines the relationship between greenwashing and firm risk among listed Australian firms from 2014 to 2023. We construct a firm-level greenwashing score as the residual based on regressions of composite ESG on Scope 1–2 CO2 emissions; positive residuals indicate overstated sustainability relative to emissions. Using realized volatility as a measure of firm risk and applying the Generalized Method of Moments (GMM) regression framework, we uncover three key findings. First, contemporaneous greenwashing significantly lowers volatility, which is consistent with legitimacy and signalling theory, as overstated ESG credentials create a temporary perception of stability. Second, the risk-reducing effect is strongest with a one-period lag, likely reflecting delayed ESG and emissions reporting cycles and investor reaction times. Third, by the two-period lag, the effect reduces in magnitude, suggesting that markets eventually recognize the misalignment between ESG claims and environmental performance. Robustness checks with the E-pillar confirm these dynamics. Additional tests excluding the COVID-19 period (2020 and 2021) reveal that the risk-mitigating effects of greenwashing are even stronger during normal market conditions, implying that pandemic-related volatility may have muted the signalling power of ESG narratives. While firm fundamentals (e.g., book-to-market) explain part of risk variation, greenwashing-driven effects are economically meaningful yet short-lived. The findings underscore that greenwashing offers only temporary risk mitigation; as transparency improves and regulatory enforcement strengthens, firms relying on inflated ESG narratives face diminishing benefits and potential long-term risk penalties.

Lifestyle diseases such as diabetes manifest through subtle and non-stationary clinical patterns, posing significant challenges for real-time prediction and monitoring. Conventional machine learning models often struggle to maintain performance under evolving data distributions due to concept drift. This study proposes an adaptive deep learning framework designed to handle concept drift through incremental learning in clinical data streams. The investigation centers on evaluating the effectiveness of various buffering strategies namely, adaptive buffering, FIFO buffering, and streaming without buffering combined with drift detection mechanisms for healthcare prediction. A balanced clinical dataset exhibiting evolving patterns was used to benchmark model performance. Deep learning architectures, including BiLSTM, GRU, LSTM, and Bayesian Neural Networks were incrementally trained, and their drift(Abrupt, Gradual and Recurring) responsiveness was assessed using three categories of detectors: 1) statistical test-based, 2) error-rate-based, and 3) uncertainty-based approaches leveraging Monte Carlo Dropout. Results indicated that adaptive buffering strategies consistently outperformed FIFO and no-buffer strategies, yielding higher accuracy, precision, and recall, especially under abrupt and large drift magnitudes. The hybrid drift detection method, combined with Bi-LSTM, demonstrated the best performance in maintaining retention and minimizing forgetting, even as the drift magnitude increased. Additionally, the drift magnitude study highlighted that larger drifts had a significant impact on model performance, with adaptive buffering and uncertainty-based drift detection proving to be more resilient to high drift intensities. This research underscores the importance of combining robust drift detection methods and adaptive buffering strategies to enhance the robustness of models dealing with concept drift in real-world applications.

This study examines the impact of overall Environmental, Social, and Governance (ESG) performance and its pillars on the default probability of Australian-listed firms. Using a panel dataset spanning 2014 to 2022 and applying the Generalized Method of Moments (GMM) regression, we find that firms with higher ESG scores exhibit a significantly lower likelihood of default. Disaggregating the ESG components reveals that the Environmental and Social pillars have a negative association with default risk, suggesting a risk-mitigating effect. In contrast, the Governance pillar demonstrates a positive relationship with default probability, which may reflect potential greenwashing behavior or an excessive focus on formal governance mechanisms at the expense of operational and financial performance. Furthermore, the analysis identifies trade credit financing (TCF) as a partial mediator in the ESG–default risk nexus, indicating that firms with stronger ESG profiles rely less on external short-term financing, thereby reducing their default risk. These findings provide valuable insights for corporate management, investors, regulators, and policymakers seeking to enhance financial resilience through sustainable practices.

This research investigates the dynamic spillover effects among green bond markets and the impact of investor sentiment on these spillovers. We employ different research methods, including a time-varying parameter vector autoregression, an exponential general autoregressive conditional heteroscedasticity, and a generalized autoregressive conditional heteroskedasticity-mixed data sampling model. Our sample is for twelve international green bond markets from 3 January 2022 to 31 December 2024. Our results evidence the strong correlation between twelve green bond markets, with the United States and China being the net risk receivers and Sweden being the largest net shock transmitter. We also find the varied impact of direct and indirect investor sentiment on the net total directional spillovers. Our research offers fresh contributions to the existing literature in different ways. On the one hand, it adds to the green finance literature by clarifying the dynamic spillovers among leading international green bond markets. On the other hand, it extends behavioral finance research by including direct and indirect investor sentiment in the spillovers of domestic and foreign green bond markets. Our study is also significant to related stakeholders, including investors in their portfolio rebalancing and policymakers in stabilizing green bond markets.

This study examines the relationship between crude oil returns (CRT) and Islamic stock returns (ISR) in BRIC countries during the Global Financial Crisis (GFC), employing wavelet-based comovement analysis and regression models that incorporate both contemporaneous and lagged CRT across 40 cases. The wavelet analysis reveals strong long-term comovement at low frequencies between ISR and CRT during the GFC. Contemporaneous regressions show that increases (decreases) in CRT align with corresponding movements in ISR. Lagged regressions indicate that CRT can predict ISR up to one week ahead for Brazil, Russia, and China, and up to two weeks for India, although the predictive strength weakens beyond this window. These findings challenge the perception that Islamic stocks were immune to the GFC, showing they were affected by global oil market dynamics, albeit with varying degrees of resilience across countries and time horizons.

This study describes its objectives by leveraging the significance of the European Union (EU)’s 2050 long-term carbon neutrality strategy to examine the impact of green financing on carbon dioxide (CO 2 ) emissions. The experiments include models based on cointegrated relations, particularly unit root testing, panel cointegration testing, and fully modified ordinary least squares and dynamic ordinary least squares regressions, using EU countries’ panel data covering 2001 to 2019. We first analyze the impact of green finance on CO 2 emissions and find an insignificant relationship. When we incorporate biofuel consumption and technological progress, we observe a significant link between green finance and reduced carbon emissions. Our findings suggest that green finance becomes substantially effective in lowering emissions when focusing on the biofuel industry and the transition to low-carbon transportation. The findings provide relevant implications for the EU 2050 long-term strategy for reducing CO 2 emissions.

The impact of intellectual capital on green innovation has been extensively studied at the firm level. However, the influence of moderating factors on this dynamic at the national level remains underexplored in previous studies. This study examines the role of institutional quality in moderating the relationship between national intellectual capital and green innovation across seventeen Asia–Pacific economies over the last twenty years, starting from 2000. Various techniques are employed to account for cross-sectional dependence and slope homogeneity in panel data analysis, enabling the examination of this relationship over the long and short term. The study also considers the marginal effects of national intellectual capital on green innovation at different degrees of institutional quality. Overall findings indicate that increasing national intellectual capital and institutional quality increases green innovation. Interestingly, the effects of national intellectual capital on green innovation intensify with a greater degree of institutional quality. We also find that enhancing economic growth and the efficient exploitation of natural resources appear to stimulate green innovation in Asia–Pacific economies. Findings imply that policies to improve green innovation should align with traditional economic growth strategies and effectively leverage intangible resources, particularly national intellectual capital. This unique empirical study examines the moderating role of institutional quality in the national intellectual capital–green innovation nexus in Asia–Pacific economies.

This study quantifies the influence of FinTech, particularly Neo-banking, on inclusive finance, with a focus on how Neobanking as FinTech innovation bridges the financial exclusion gap. We employ Pooled Ordinary Least Squares (OLS) and Quantile Regressions using data across 72 countries from 2017 to 2022. This study also extends empirical experiments to time-fixed effects and Three-Stage Least Squares (3SLS) regressions to address endogeneity and show the robustness of the experiment. We find that Neo-banking has a significantly positive impact on inclusive finance, particularly in encouraging traditional access to finance adoption and inspiring borrowing from traditional banking. Besides, the institutional quality is also significantly fostering inclusive finance. Overall, the output indicates that Neobanking has the ability to reduce the financial exclusion gap by reaching underprivileged communities in isolated or rural locations where there might not be many conventional physical bank branches.