Search Results (634)

Before the enactment of the Dodd–Frank Act, firm size was taken into account by rating agencies in determining the credit ratings of banks. Therefore, the “too-big-to-fail” problem was, at least partially, reflected in big banks’ elevated ratings, which are more than justified by intrinsic creditworthiness. What is unclear is whether the bond market still gives an additional discount in yield to big banks over and above the lower yield spread that is already reflected in the elevated credit ratings due to their size. In this study, we examine this question and document a significant incremental yield discount for large banks even after controlling for credit ratings. Furthermore, we find that big banks with lower ratings pay lower borrowing costs than non-big banks with higher ratings. This additional discount, however, mostly disappeared after the Dodd–Frank Act. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

The main purpose of this research is to examine the significance of green bonds and green banking loans as financing tools for ecologically sustainable projects in the face of increasing worldwide environmental issues. This research seeks to uncover the determinants of both instruments’ issuance and the obstacles to their acceptance. A thorough systematic literature review will be conducted to assess the efficacy of these tools in improving company financial performance and cost of debt, advancing environmental sustainability, and influencing investor behavior. This methodology guarantees a comprehensive and impartial examination of peer-reviewed publications from reputable sources such as Web of Science and Scopus. Although issues such as greenwashing, market liquidity, and regulatory discrepancies still exist, both tools are growing steadily in the sustainable financing spectrum. The results also suggest that both instruments are influenced by several factors, often overlapping due to their common focus on financing sustainable projects. The credit rating, financial health, and overall environmental performance of the issuing entity significantly influence the attractiveness and pricing of green bonds, as do the market conditions, regulatory frameworks, and certification. The environmental profile and creditworthiness of the borrower are key determinants for green banking loans. The review enhances the current body of knowledge by presenting a theoretical structure for comprehending the dynamics of green debt markets and proposing practical recommendations for policymakers and financial institutions. Furthermore, it emphasizes the deficiencies in existing research, including the need for further longitudinal investigations into green bank loans and a more thorough examination of the notion of ‘greenium’. We searched Web of Science and Scopus up to 26 April 2024. Eligibility criteria included peer-reviewed English-language studies on green bonds or green banking loans. After screening, 128 studies were found to have met the inclusion criteria. Full article

Credit card fraud detection remains a critical challenge for financial institutions, particularly due to extreme class imbalance and the continuously evolving nature of fraudulent behavior. This study investigates two complementary approaches: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While anomaly detection effectively identifies deviations from normal transaction patterns, its static nature limits adaptability in real-time systems. In contrast, the DQN reinforcement learning model continuously learns from every transaction, autonomously adapting to emerging fraud strategies. Experimental results demonstrate that, although initial performance metrics of the DQN are modest compared to anomaly detection, its capacity for online learning and policy refinement enables long-term improvement and operational scalability. This work highlights reinforcement learning as a highly promising paradigm for dynamic, high-volume fraud detection, capable of evolving with the environment and achieving near-optimal detection rates over time. Full article

Artificial intelligence (AI) use has become a major emerging trend in corporate finance and governance. AI is used for a variety of business tasks, such as assessing credit risk, document analysis, corporate default forecasting, and detecting fraud. This study first provides an overview of the development of AI applications related to financial reporting and corporate governance and then examines the financial performance of firms rated highly for their use of AI. AI applications can improve risk management, auditing processes, financial distress, fraud detection, and board performance. The findings can help directors, managers, financial personnel, and others interested in AI. Full article

This study examines the systemic operational risk in Morocco’s banking sector using a Panel VAR model based on data from three banks over ten years. The model includes real GDP, interbank rate (TMP), and bank credit, alongside indicators of operational, credit, and liquidity risks. The Impulse Response Functions (IRF) show that operational risk shocks reduce GDP and affect TMP with a lag, confirming their systemic impact. Forecast Error Variance Decomposition (FEVD) reveals that GDP significantly explains the variance in operational risk. To strengthen the analysis, a dynamic panel GMM model is used to address endogeneity. The GMM results demonstrate that systemic operational risk in Moroccan banks is both persistent and procyclical, highlighting how macro-financial dynamics such as growth, inflation, and monetary conditions, directly shape banks’ resilience. These findings provide new empirical evidence on the determinants of systemic operational risk in emerging markets. This dual approach supports the integration of operational risk into Morocco’s macroprudential policy frameworks. Full article

Driven by carbon neutrality policies, the cumulative issuance volume of the global green bond market has surpassed $2.5 trillion over the past five years, with China, as the second largest issuer, accounting for 15%. However, there exists a yield difference of up to 0.8% for bonds with the same credit rating across different policy regions, and the premium level fluctuates dramatically with market cycles, severely restricting the efficiency of green resource allocation. This study innovatively constructs a Bayesian hierarchical spatiotemporal model framework to systematically analyze pricing deviations through a three-level data structure: the base level quantifies the impact of bond micro-characteristics (third-party certification reduces financing costs by 0.15%), the temporal level captures market dynamics using autoregressive processes (premium volatility increases by 50% during economic recessions), and the spatial level reveals policy regional dependencies using conditional autoregressive models (carbon trading pilot provinces and cities form premium sinkholes). The core breakthroughs are: 1. Designing spatiotemporal interaction terms to explicitly model the policy diffusion process, with empirical evidence showing that the green finance reform pilot zone policy has a radiation radius of 200 km within three years, leading to a 0.10% increase in premiums in neighboring provinces; 2. Quantifying the posterior distribution of parameters using the Markov Chain Monte Carlo algorithm, demonstrating that the posterior mean of the policy effect in pilot provinces is −0.211%, with a half-life of 0.75 years, and the residual effect in non-pilot provinces is only −0.042%; 3. Establishing a hierarchical shrinkage prior mechanism, which reduces prediction error by 41% compared to traditional models in out-of-sample testing. Key findings include: the contribution of policy pilots is −0.192%, surpassing the effect of issuer credit ratings, and a 10 yuan/ton increase in carbon price can sustainably reduce premiums by 0.117%. In 2021, the “dual carbon” policy contributed 32% to premium changes through spatiotemporal interaction channels. The research results provide quantitative tools for issuers to optimize financing timing, investors to identify cross-regional arbitrage, and regulators to assess policy coordination, promoting the transformation of the green bond market from an efficiency priority to equitable allocation paradigm. Full article

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments. Full article

This study examines the determinants of environmental, social, and governance (ESG) performance among Chinese financial institutions, with particular emphasis on community engagement, firm size, and ownership structure as drivers of ESG performance and their contribution to the Sustainable Development Goals (SDGs). Utilizing ESG ratings from CSRHub and annual reports from 107 financial companies spanning 2022–2024, hierarchical regression analyses demonstrate that community engagement significantly predicts ESG performance (β = 0.816, p < 0.001), explaining 67.7% of the variance in ESG ratings. Conversely, the firm (β = 5.687 × 10⁻⁶, p > 0.05) and the ownership structure (β = 1.35, p > 0.05) exhibit no statistically significant effect. Robustness evaluations, concerning bootstrapping methodologies and calculations of heteroscedasticity-consistent standard errors, check these findings. The cross-sectional design limits causal inference. Longitudinal studies would allow deeper exploration of temporal dynamics. The results specify that community engagement acts as the primary factor affecting ESG performance within Chinese financial institutions, whereas firm size and ownership structure exercise insignificant influence. Financial institutions should prioritize substantive, sustained community initiatives rather than relying on organizational scale or state affiliation. For policymakers, the findings suggest that incentive mechanisms (e.g., tax credits or green-finance subsidies) should reward verifiable community-impact outcomes rather than firm size or state ownership, which do not reliably predict superior ESG performance. Full article

In recent years, the financial sustainability and survival of microfinance institutions (MFIs) have been seriously threatened by factors such as the reduction in donations, cooperation funds and international aid, and increased competition from commercial banks. Faced with this hostile scenario, which may limit access to credit for disadvantaged groups, MFIs must apply techniques to improve their efficiency, viability, lending capacity and survival. The objective of this study is to design a microcredit pricing model based on the Internal Ratings-Based approach, Basel III and probability of default to enhance access to credit for disadvantaged groups. We analysed a sample of 4550 microcredit transactions and 30 influential variables (25 idiosyncratic and 5 systemic). Our empirical results reveal that the IRB system is more equitable for borrowers and more efficient for MFIs, as it allows lower interest rates to be applied to borrowers with better credit histories. The application of the proposed IRB model can improve the sustainability, competitiveness and viability of MFIs by promoting operational efficiency and reducing default rates, thus contributing to financial inclusion by increasing supply. Full article

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article

In a situation where the number of non-performing loans (NPLs) increases, lenders may raise interest rates to compensate for potential losses, and the amount of credit granted in the market may decrease, leading to credit rationing. Such actions may become vital based on their potential consequences for the economy, entrepreneurs and consumers, which makes this topic extremely important. This study, by using an empirical VAR analysis, has strived to determine whether credit rationing by banks operating in the Polish banking sector is driven by risky loans (which are the main determinant of credit rationing and are represented by the ratio of NPLs to total loans). According to the results, it has been found that credit rationing, made by Polish banks, is not statistically significant when the risk in the credit market rises due to non-performing loans. Therefore, it can be claimed that the risky structure due to NPL in the credit market may not be one of the determinant factors of credit rationing in the Polish banking sector. The low sensitivity of the Polish banking sector to the risky structure of the credit market may result from the relatively low share of loans in total assets compared to debt instruments. Furthermore, restrictive lending policies and the predominance of mortgage loans secured directly by real estate limit portfolio risk, which may reduce the need for a risk-sensitive lending strategy. Full article

European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic mispricing. We employ a twofold econometric analysis on a dataset of consumer loans from a Lithuanian peer-to-peer platform. After data preparation for the regression, the sample consists of 9707 loans. First, logistic regression is used to model actual default risk, controlling for credit rating, age, loan amount, and education. Second, Ordinary Least Squares (OLS) regression is used to model the interest rate set by the platform. The Logit model finds that gender is a highly significant predictor of default (p < 0.001), with male borrowers associated with a higher probability of default. Conversely, the OLS model finds that gender is not a statistically significant factor in loan pricing (p = 0.263), confirming the platform’s compliance with EU law. The findings empirically demonstrate the regulatory paradox: the legally compliant, gender-blind pricing model fails to account for a significant risk differential. This leads to systematic risk mispricing and an implicit cross-subsidy from lower-risk female borrowers to higher-risk male counterparts, highlighting a critical tension between regulatory intent and outcome fairness. The analysis is limited to observed loan-level characteristics; it does not incorporate household composition or the internal structure of the platform’s proprietary scoring model. Full article

Selecting Construction and Demolition Waste (CDW) credits in LEED-certified projects is essential for sustainable building management, often requiring specialised expertise and contextual sensitivity. However, existing studies provide limited analytical insight into why certain CDW credits succeed or fail across different project contexts, and no explainable AI–based framework has been proposed to support transparent credit decisioning. This gap underscores the need for a data-driven, interpretable approach to CDW credit evaluation. This study proposes an explainable artificial intelligence (XAI)-based model to support CDW credit selection and to identify the key factors influencing credit performance. A dataset of 407 LEED green building projects was analysed using twelve machine learning (ML) algorithms, with the top models identified through Bayesian optimisation. To handle class imbalance, the SMOTE was utilised. Results showed that MRc2 and MRc4 credits had high predictive performance, while MRc1.1 and MRc6 credits exhibited relatively lower success rates. Due to data limitations, MRc1.2 and MRc3 were excluded from analysis. The CatBoost model achieved the highest performance across MRc1.1, MRc2, MRc4, and MRc6, with F1 scores of 0.615, 0.944, 0.878, and 0.667, respectively. SHapley Additive exPlanations (SHAP) analysis indicated that the Material Resources feature was the most influential predictor for all credits, contributing 20.6% to MRc1.1, 53.4% to MRc2, 36.5% to MRc4, and 22.6% to MRc6. In contrast, the impact of design firms on credit scores was negligible, suggesting that although CDW credits are determined in the design phase, these firms did not significantly influence the decision process. Higher certification levels improved the performance of MRc1.1 and MRc6, while their effect on MRc2 and MRc4 was limited. This study presents a transparent and interpretable XAI-based decision-support framework that reveals the key sustainability drivers of CDW credit performance and provides actionable guidance for LEED consultants, designers, and decision-makers. Full article

Remediating complex-contaminated soils demands the synergistic optimization of efficiency, cost-effectiveness, and carbon emission reduction. Currently, ultra-high-temperature thermal desorption technology is mature in terms of principle and laboratory-scale performance; however, ongoing efforts are focusing on achieving stable, efficient, controllable, and cost-optimized operation in large-scale engineering applications. To address this gap, this study aimed to (1) verify the energy efficiency and economic benefits of removing over 98% of target pollutants at a 7.5 × 10⁴ m³ contaminated site and (2) elucidate the mechanisms underlying parallel scale–technology dual-factor cost reduction and energy–carbon–cost optimization, thereby accumulating case experience and data support for large-scale engineering deployment. To achieve these objectives, a “thermal stability–chemical oxidizability” classification criterion was developed to guide a parallel remediation strategy, integrating ex situ ultra-high-temperature thermal desorption (1000 °C) with persulfate-based chemical oxidation. This strategy was implemented at a 7.5 × 10⁴ m³ large-scale site, delivering robust performance: the total petroleum hydrocarbon (TPH) and pentachlorophenol (PCP) removal efficiencies exceeded 99%, with a median removal rate of 98% for polycyclic aromatic hydrocarbons (PAHs). It also provided a critical operational example of a large-scale engineering application, demonstrating a daily treatment capacity of 987 m³, a unit remediation cost of 800 CNY·m⁻³, and energy consumption of 820 kWh·m⁻³, outperforming established benchmarks reported in the literature. A net reduction of 2.9 kilotonnes of CO₂ equivalent (kt CO₂e) in greenhouse gas emissions was achieved, which could be further enhanced with an additional 8.8 kt CO₂e by integrating a hybrid renewable energy system (70% photovoltaic–molten salt thermal storage + 30% green power). In summary, this study establishes a “high-temperature–parallel oxidation–low-carbon energy” framework for the rapid remediation of large-scale multi-contaminant sites, proposes a feasible pathway toward developing a soil carbon credit mechanism, and fills a critical gap between laboratory-scale success and large-scale engineering applications of ultra-high-temperature remediation technologies. Full article