Search Results (146)

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Objective: Contrary to the traditional notion of risk–return trade-off, prior studies document that financially distressed stocks tend to earn lower future returns than their healthier peers. Extending this strand of literature, this study revisits the distress risk anomaly in UK stocks and further examines whether proximity to the 52-week high and lottery-like characteristics of stocks help explain the financial distress anomaly, if any. Data and methods: In this paper, we analyse the distress risk anomaly using a sample of 4514 UK stocks over the period 2000–2021. The analysis is conducted using both the portfolio-sorting method and Fama–MacBeth cross-sectional regressions. Key findings: The empirical findings confirm the persistence of the financial distress anomaly, showing that high-distress stocks earn lower returns than their low-distress counterparts. Consistent with a mispricing explanation, this inverse distress–return relationship is more pronounced for stocks that are difficult to arbitrage and is stronger following periods of market optimism. Furthermore, the analysis reveals that both the 52-week high effect and lottery-like trading, independently and jointly, contribute to the poor performance of financially distressed stocks. This suggests that underreaction and overreaction interact to shape the observed overvaluation of distressed stocks. These findings remain robust to a battery of robustness checks. The results have several important implications for investors, researchers, and regulators. Full article

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

Physical inactivity and household financial fragility are often studied separately, yet households may respond to health and financial shocks through interrelated behavioral, health, and financial processes. This study examines whether physical activity, health capital, and household financial resilience are dynamically associated in China. Using five waves of the China Family Panel Studies, we construct a household-wave panel and multidimensional indices of health capital and financial resilience. We apply lagged household fixed-effects models, dynamic mediation analysis, and panel vector autoregression with impulse response functions and forecast error variance decomposition. The results indicate that physical activity is positively associated with subsequent health capital, health capital positively predicts subsequent household financial resilience, and financial resilience has a smaller but statistically significant association with later physical activity. The mediation results are consistent with health capital serving as a partial transmission channel between physical activity and financial resilience. The PVAR results show persistent cross-variable responses, suggesting modest dynamic interdependence among the three components rather than definitive causal evidence of a strong self-reinforcing system. Heterogeneity analyses suggest that these associations are more pronounced among low-income, older-head, and chronic-risk households. These findings extend health-capital and household finance research by showing that health behavior and financial resilience can be examined as jointly evolving household-level processes. The results suggest that integrated approaches to physical activity promotion and household financial protection may be worth further policy experimentation and evaluation, especially for vulnerable households. Full article

Generative artificial intelligence (GAI) is increasingly embedded in personal finance, yet little is known about how models make recommendations using financial information and demographic cues. This study audits three frontier GAI models, GPT 5.5, Gemini 3.1 Pro, and Claude Opus 4.7, using a conjoint experiment in which each model evaluated the same hypothetical investor profiles and selected among standardized conservative, balanced, and aggressive portfolios. Investor profiles systematically varied attributes, including risk tolerance, time horizon, goal type, income, and age, gender, ethnicity, marital status, and employment type. Ordered logistic regressions and matched-profile comparisons show that all three models base recommendations primarily on financial attributes, especially risk tolerance and time horizon. Age and marital status shift recommendations towards conservatism in all models, conversely only Claude conditions on gender and employment type. Ethnicity exerts no detectable influence on the recommendations of ChatGPT or Claude, but is a small, statistically significant predictor for Gemini, with non-White profiles receiving slightly more conservative recommendations than otherwise identical White profiles. Overall, we find that the models are not interchangeable: they differ significantly in overall risk appetite and in how they translate risk tolerance, time horizon, goal type, and age into portfolio choices, with economically meaningful differences in predicted recommendations for identical clients. These findings suggest that contemporary GAI investment advice is driven mainly by financially relevant attributes, but that demographic sensitivity may appear in model-specific and statistically nuanced ways, alongside a distinct form of platform risk arising from model-specific advisory logic. Full article

Climate-related disasters are escalating in frequency and severity, yet global adaptation finance remains critically insufficiently structured to respond after disasters occur rather than before. This study empirically examines disaster loss data, climate finance flows, and financial instrument evidence to test two hypotheses: whether climate finance is disaster-reactive, and whether policy uncertainty constrains it. We integrate data from the Emergency Events Database (EM-DAT), covering seven climate-induced hazard types (droughts, extreme temperatures, floods, glacial lake outburst floods, wet mass movements, storms, and wildfires), in addition to the OECD Creditor Reporting System (CRS), the World Uncertainty Index (WUI), the ND-GAIN vulnerability index, and the World Governance Indicators, the Green Climate Fund Open Data Library, and the Artemis Deal Directory across 131 countries (2011–2024) for Hypothesis 1 and 100 countries (2012–2024) for Hypothesis 2. Fixed-effects panel regressions with Driscoll–Kraay standard errors confirm that prior-year disaster losses significantly predict subsequent climate finance flows (β = 0.040, p = 0.009; N = 1769 country-year observations), establishing a reactive financing pattern. Policy uncertainty interacting with high vulnerability is found to suppress adaptation finance flows (β = −2.587, p = 0.080, N = 878 country-year observations), with the effect concentrated among the most climate-exposed economies. We propose a risk-layered climate finance architecture aligning instruments with distinct hazard tiers across the Global South. Credible policy signals, strategic public investment, and systematic integration of insurance mechanisms are essential preconditions for unlocking scalable, forward-looking resilience finance. Full article

This systematic literature review, guided by Kitchenham and Charters and following PRISMA 2020, analyzes explainable artificial intelligence (XAI) approaches for multiclass classification models, with an emphasis on explaining class differentiation and the relationship between feature contributions and changes in prediction probabilities. The protocol was defined in advance, but it was not preregistered. Searches were conducted in Scopus, Web of Science, SpringerLink, and ScienceDirect (2020–2025) using PICOC-based strings and explicit eligibility criteria. Following the PRISMA flow, 108 studies were included out of 8697 identified records. The most frequently reported approaches are based on feature contribution/attribution (e.g., SHAP, LIME, CAM, and Grad-CAM) and counterfactual explanations, with prominent applications in medicine, finance, and cybersecurity. Although several works analyze local contributions and, separately, probability variations, the synthesis reveals a methodological gap: there is a lack of a formal and explicit instance-level framework that quantitatively connects the differential contribution of a feature (e.g., SHAP values) with the probability variation between classes to explain class differentiation. In practical terms, such a linkage enables instance-level justification of why a model favors class A over a competing class B, improving traceability and decision support in high-stakes settings (e.g., differential diagnosis and risk assessment). These findings point to future directions toward more rigorous comparative local explanations in multiclass settings. Full article

Accurate prediction of loan defaults is essential for financial institutions seeking to minimize credit losses and maintain portfolio stability. In the vehicle financing segment of emerging markets, real-world datasets frequently exhibit extreme class imbalance ratios that far exceed those encountered in standard benchmark corpora, posing severe challenges for conventional machine learning pipelines. This study introduces a gradient boosting framework integrating Weight of Evidence (WoE) transformation, Bayesian hyperparameter optimization, and three complementary classifiers—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—to predict vehicle loan default risk. The methodology is evaluated on a large-scale, fully anonymized Turkish vehicle loan dataset ($N=207,572$) with an extreme imbalance ratio of 1:1133 (183 defaults versus 207,389 non-defaults). A strict three-way data partition (60% training, 20% validation, 20% test) is adopted to ensure leakage-free model selection and unbiased performance estimation. A multi-stage experimental pipeline is developed encompassing: (i) statistical feature selection via Mann–Whitney U and chi-square tests with adaptive thresholding, (ii) a comparative analysis of seven resampling strategies including Synthetic Minority Oversampling Technique (SMOTE) variants, Adaptive Synthetic Sampling (ADASYN), and focal loss weighting, (iii) a greedy forward selection ensemble procedure for heterogeneous model fusion, and (iv) a systematic training-set size sensitivity analysis across eight majority undersampling ratios. Under the leakage-free evaluation protocol, the highest-AUC individual model (LightGBM with SMOTE-ENN) achieves an Area Under the Curve (AUC) Receiver Operating Characteristic (ROC) of 0.710 (95% bootstrap CI: 0.614–0.798), while CatBoost with cost-sensitive weighting exhibits superior operational metrics (KS $=0.389$, PR-AUC $=0.011$). The greedy ensemble procedure exhibits high selection instability with only 37 validation-set positives, providing a methodological finding on the minimum sample requirements for reliable ensemble construction under extreme scarcity. Ablation results confirm that WoE encoding contributes 3.1 percentage points to the overall AUC gain. Tree SHAP-based interpretability analysis identifies the financing-to-age ratio, WoE-encoded occupation group, and log financing amount as the primary predictive drivers, with cross-model stability confirmed via Spearman rank correlation. A decision support analysis provides precision–recall curves, a Brier score of 0.0082, reliability diagrams, and threshold-dependent performance at operationally plausible review rates. Fairness evaluation across gender and marital status subgroups demonstrates that threshold-dependent metrics such as Disparate Impact Ratio and Equalized Odds Gap are inherently compromised under extreme minority scarcity, whereas rank-based subgroup AUC analysis with bootstrap 95% confidence intervals preserves meaningful discriminative assessment. These findings provide an empirically validated framework for credit default prediction in highly imbalanced and data-scarce financial environments. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

Climate change poses significant challenges to the agricultural and financial sectors, affecting crop productivity and the overall financial stability. This study evaluates the robustness of the Actuaries Climate Index^TM (ACI), a relatively recent tool to measure the impact of climate change, by comparing its explanatory power to well-established weather-based indexes (WBIs) across two key sectors. In the agricultural context, the yields of three major crops are predicted using generalized statistical models and advanced machine learning algorithms with climate indexes as explanatory variables. To enhance model reliability and address multicollinearity among weather-related variables, the study also incorporates both principal component analysis and functional principal component analysis. A total of 22 models, each constructed with different sets of explanatory variables, illustrate the significant impact of wind speed and sea-level changes, alongside temperature and precipitation, on crop yield variability across six regions of the United States. For the financial market application, the analysis adapts the weather-derivative framework, as it is a critical instrument for energy companies, insurers, and agribusinesses seeking to hedge against weather-related risks. By analyzing the payoffs of derivative contracts that use WBIs and ACI components as underlying variables, the findings reveal that the ACI framework holds a strong potential as a comprehensive climate risk indicator, not only for the agricultural sector but also for the finance and insurance industries. Full article

Community disaster resilience is increasingly guiding risk-reduction investments, but in many Southeast European settings, comparable subnational data remain scarce. This study assesses perceived community disaster resilience across Serbia by combining BRIC–DROP dimensions into a single index and analyzing differences across hazard types and sociodemographic factors. A cross-sectional household survey was conducted using multistage random sampling and the “next birthday” method for respondent selection. The final sample included 1200 adults from 22 local government units across four regions: Belgrade, Vojvodina, Šumadija & Western Serbia, and Southern & Eastern Serbia. Participants evaluated preventive measures and societal resilience for ten hazard types and considered five social dimensions: social structure, social capital, social mechanisms, social equity/diversity, and social beliefs. Descriptive statistics, bivariate analyses (including Pearson correlations, t-tests, and ANOVA), and multiple linear regression identified key predictors of preventive behavior and perceived resilience. Composite scores highlighted spatial resilience differences. Overall perceptions were generally low, mostly falling below the midpoint of the scale. Furthermore, the highest ratings for implemented preventive measures were recorded for pandemics/epidemics, storms/hail, and floods, whereas the lowest were observed for environmental pollution and droughts. Perceived resilience was highest for snowstorms, storms/hail, and pandemics/epidemics, and lowest for environmental pollution and droughts. Also, respondents reported relatively strong family ties and favorable perceptions of communication and access to basic supplies, but weak institutional capacity, particularly in budget allocation, early warning and public notification, rapid decision-making, and evacuation and shelter readiness. Regression results were statistically significant but explained only a small portion of the variance. Age and public-sector employment positively predicted perceived resilience; fear, income, and, to a lesser extent, education were negatively associated. These findings highlight the structural and psychosocial factors that shape perceptions of resilience. The BRIC–DROP composite indicates generally low perceived preparedness and resilience, especially in risk communication, evacuation and shelter readiness, and financing—the key bottlenecks in strengthening local resilience. The results recommend combining institutional reform with targeted risk communication to reduce fear and build trust, especially focusing on hazard areas with the lowest confidence, such as environmental pollution and drought. Full article

The phenomenon of followership is widely observed in the e-commerce industry. Crowdfunding, as a model of e-commerce entrepreneurship, has attracted many investors. Principal investors function as “leaders” who exert influence on follow-on (subsequent) investors. Accurately identifying principal investors in online entrepreneurial ventures and analyzing their preferences could enhance the success rate of fundraising. Grounded in the BTS (Behavior–Text–Social) framework, this study constructs a multi-dimensional model comprising 15 sub-indicators across three domains: user behavior, textual data, and social connections. A neural network is employed for training and prediction. By integrating the central and peripheral routes elicited from the Elaboration Likelihood Model (ELM), which ranks influence, principal investors are identified. The experiment results indicate that ELM-derived ranking demonstrates the highest consistency (error = 0.15), followed by user behavior (error = 0.30), social metrics (error = 0.71), and textual features (error = 0.95). Weight analysis using SHAP highlights the relative importance of structural holes, out-degree centrality, investment times, and investment moments. Furthermore, principal investors exhibit a preference for local projects and occupy dual roles. This study provides a theoretical foundation and practical guidance for identifying principal investors, thereby improving financing performance and mitigating investment risks for follow-on investors. Full article