Search Results (1,874)

This paper investigates tail-risk transmission and asymmetric dependence in commodity markets using an asymmetric fuzzy vine copula framework applied to gold, crude oil, natural gas, and silver from 1 January 2015 to 1 January 2025, extracted from Yahoo Finance. Bootstrap-based trapezoidal fuzzy numbers are used to estimate fuzzy tail dependence, VaR, and CoVaR, capturing both sampling variability and parameter uncertainty. Results show generally weak and symmetric dependence among commodities, except for strong lower-tail dominance between crude oil and natural gas, indicating downside contagion within the energy sector. Adding the SKEW index as a market-implied tail-risk proxy has negligible effects on dependence and spillovers, revealing that equity-market tail-risk sentiment does not influence commodity markets. Systemic risk remains localized within energy and precious-metal linkages, underscoring the need for sector-specific monitoring. Full article

As an emerging trading market, the crude oil futures market has exhibited substantial uncertainty since its inception. Influenced by macroeconomic and geopolitical factors, its price movements are highly nonlinear and nonstationary, making accurate forecasting challenging. Therefore, it is vital to develop a powerful forecasting model for crude oil futures prices. However, conventional forecasting models rely solely on historical data and fail to capture the intrinsic patterns of complex sequences. This work presents a hybrid deep learning framework that incorporates multi-source features and a state-of-the-art attention mechanism. Specifically, search engine data were collected and integrated into the explanatory variables. By using lagged historical prices and search engine data to forecast future crude oil futures closing prices, the proposed framework effectively avoids lookahead bias. To reduce forecasting difficulty, the initial time series were then decomposed and reconstructed into several sub-sequences. Thereafter, traditional time series models (ARIMA) and attention-enhanced deep learning models were selected to forecast the reconstructed sub-sequences based on their distinct data features. The empirical study conducted on the INE crude oil futures price proves that the proposed model outperforms other benchmark models. The findings help fill the gap in the quantitative literature on crude oil futures price forecasting and offer valuable theoretical insights for affiliated policymakers, enterprises, and investors. Full article

Background: Financial sustainability remains a central challenge for U.S. hospitals as rising operating costs, shifting federal reimbursement, and policy uncertainty intensify economic pressures. This study estimates the prevalence and recent changes in financial distress among U.S. short-term acute care hospitals. Methods: We conducted a national longitudinal analysis of all U.S. short-term acute care hospitals from 2021 to 2023 using financial and operational data from Medicare cost reports linked with community-level data from the American Community Survey. Financial distress was measured using the Altman Z-score, with severe distress defined as Z ≤ 1.8. Logistic regression models were used to identify organizational, operational, and market characteristics associated with distress. Results: The proportion of hospitals classified as severely financially distressed increased from 18.6% in 2021 to 22.0% in 2023. Operating margins and returns on assets declined significantly over the study period, while mean Z-scores showed a modest but non-significant downward trend. In adjusted models, urban hospitals had higher odds of distress (OR 1.27, 95% CI 1.15–1.40, p < 0.001), as did hospitals with longer average lengths of stay (OR 1.07 per day, 95% CI 1.04–1.09, p < 0.001) and higher debt-to-equity ratios (OR 1.05 per unit, 95% CI 1.05–1.06, p < 0.001). Higher occupancy rates were protective (OR 0.31, 95% CI 0.25–0.40, p < 0.001). Larger market population was also associated with increased distress risk (OR 1.61, 95% CI 1.21–2.14, p = 0.001), while other market characteristics were not significant. Conclusions: Financial distress remains widespread and appears to be increasing among U.S. acute care hospitals. Operational efficiency, capital structure, and local market scale are key drivers of financial vulnerability, highlighting the need for targeted strategies to strengthen hospital resilience and preserve access to essential acute care services. Full article

In recent years, the operational performance of agricultural enterprises has been influenced by both natural conditions and market environments, resulting in high uncertainty and volatility. When performance falls below expectations, agricultural enterprises consciously engage in strategic change and proactive risk-taking to alleviate performance pressures. Based on Firm Behavioral Theory, Performance Feedback Theory, and Prospect Theory, we examine how performance expectation gap affects risk-taking of agricultural enterprises by using panel data of Chinese A-share listed agricultural firms from 2007 to 2023. The results show that performance expectation gap has a positive effect on risk-taking, which means the greater the gap, the higher the level of risk-taking. And the better developed the institutional environment, the greater the tendency for risk-taking. Further analysis shows that performance expectation gap promotes risk-taking by driving strategic change within agricultural enterprises. This research enriches the study on the influencing factors of risk-taking in agricultural enterprises, offering decision-making insights for them to prudently assess and manage risks. Full article

Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments. Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns, we propose employing reinforcement learning (RL) to address dynamic decision-making challenges. However, traditional RL methods often struggle to adapt to significant market volatility, primarily by focusing on adjusting existing asset weights. Different from traditional RL methods, the multi-agent reinforcement learning correctable strategy (MAC) developed in this study detects and replaces potentially harmful assets with familiar alternatives, ensuring a resilient response to market crises. Utilizing the multi-agent reinforcement learning model, MAC empowers individual agents to maximize portfolio returns and minimize risk separately. During training, MAC strategically replaces assets to simulate market changes, allowing agents to learn risk-identification through uncertainty estimation. During testing, MAC detects potentially harmful assets and replaces them with more reliable alternatives, enhancing portfolio stability. Experiments conducted on a real-world US Exchange-Traded Fund (ETF) market dataset demonstrate MAC’s superiority over standard RL-based PM methods and other baselines, underscoring its practical efficacy for real-world applications. Full article

Amid profound transformations in the global political and economic landscape and increasingly stringent resource and environmental constraints, enhancing corporate competitiveness under high uncertainty and achieving sustainable development have become core challenges for firms. Based on data from Chinese A-share listed companies during 2013–2024, this study constructs a corporate sustainable development indicator system under the triple bottom line framework and measures it using the entropy method. Meanwhile, the Supply Chain Innovation and Application Pilot policy is treated as a quasi-natural experiment, and a Staggered Difference-in-Differences (DID) model is employed to systematically examine the impact of supply chain innovation on corporate sustainable development. The results indicate that supply chain innovation significantly enhances firms’ sustainable development performance, and this finding remains robust across a series of robustness checks. Mechanism analysis shows that the policy effect primarily operates through two channels: relational effects and informational effects. On the one hand, supply chain innovation strengthens collaboration and trust between firms and their upstream and downstream partners, improving supply chain stability and overall operational efficiency. On the other hand, it promotes information sharing and digital coordination, alleviates information asymmetry, and optimizes resource allocation, thereby boosting corporate sustainability. Further heterogeneity analysis reveals that the policy effect is more pronounced in firms with higher levels of digitalization and weaker market pricing power, in upstream segments of the value chain, in industries with higher warehousing and transportation costs and lower market competition, and in regions with more advanced digital infrastructure and relatively richer resource endowments. Full article

Although responsible for 10% of global greenhouse gas emissions, Latin America faces disproportionate vulnerability to climate-related events, making the need for clear, transparent, and rigorous action critically urgent. Corporate disclosure practices across the region show high variability in transparency and methodological consistency, posing a substantial obstacle in evidence-based measures against climate change. This study provides the first multi-country assessment of the quality and rigor of carbon footprint disclosures in the Latin American context, analyzing 103 company reports across five countries (Chile, Colombia, Ecuador, Mexico, and Peru) with the Carbon Integrity Index, a 10-indicator standardized metric quantifying the transparency of Scopes 1, 2, and 3 and uncertainty disclosures. Three distinct patterns emerged from the analysis. Although 83.5% of companies disclose some value-chain emission data, Scope 3 disclosure quality remains a systemic deficiency across the region (average 0.19–0.31) with uncertainty quantification nearly absent (94% non-disclosure). Median scores for all five countries cluster narrowly (2.65–4.20), independently of heterogenous governance frameworks. Finally, disclosure deficiencies appear uniform across sectors, suggesting structural rather than industry-specific barriers. These findings suggest that voluntary or international frameworks produce regional convergence at low quality levels, whereas adequate transparency requires differentiated capacity-building initiatives and national enforcement frameworks in emerging market contexts. Full article

Urban microgrids are evolving into socially coupled energy systems in which prosumer decisions are shaped by both market incentives and peer influence. Conventional optimization approaches overlook this behavioral interdependence and offer limited adaptability under environmental disturbances. This study develops a behaviorally embedded multi-agent optimization framework that integrates social influence propagation with physical power network coordination. Each prosumer’s decision process incorporates economic, comfort, and behavioral components, while a community operator enforces system-wide feasibility. The resulting bilevel structure is formulated as an equilibrium problem with equilibrium constraints (EPEC) and solved using an iterative hierarchical algorithm. A modified 33-bus urban microgrid with 40 socially connected agents is assessed under stochastic wildfire ignition and propagation scenarios to evaluate resilience under hazard-driven uncertainty. Incorporating behavioral responses increases welfare by 11.8%, reduces cost variance by 9.1%, and improves voltage stability by 23% compared with conventional models. Under wildfire stress, socially cohesive agents converge more rapidly and maintain more stable dispatch patterns. The findings highlight the critical role of social topology in shaping both equilibrium behavior and resilience. The framework provides a foundation for socially responsive and hazard-adaptive optimization in next-generation human-centric energy systems. Full article

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Thailand is globally recognized for its tourism potential and rich diversity of cultural and natural heritage. Community-based tourism (CBT), in particular, holds significant promise for inclusive and sustainable development. However, CBT initiatives across the country remain fragile in the face of uncertainty, whether from pandemics, climate events, or market shifts, and are often constrained by fragmented practices and the absence of a shared service development framework that addresses sustainability tensions. This study addresses that gap by developing and validating a sustainability-oriented new service development (NSD) process comprising five phases and sixteen steps, tailored specifically for CBT under uncertainty. Through expert interviews and iterative action research in two contrasting Thai communities, the process was refined to include tools for place identity, customer analysis, service testing, and adaptive planning. The framework enables CBT communities to move from ad hoc efforts to structured, resilient, and market-aligned service practices. Expert validation confirmed its effectiveness and adaptability, while also recommending digital transformation and financial integration as future directions. This process offers a pathway for improving CBT outcomes in Thailand, and a potentially adaptable framework for CBT development across diverse contexts in uncertain tourism environments. Full article

This article analyzes an electrical system based on the IEEE-57 bus case, which integrates thermal and wind generation to meet hourly demand. Using the previous day’s wind forecasts as firm market bids, the optimal Pareto frontier for thermal dispatch is calculated, balancing total cost and emissions. The system operator selects a dispatch point based on the desired cost–emissions ratio. To reflect real-world uncertainty, the study incorporates statistical deviations in actual wind production derived from historical data. For each deviation scenario, new optimal thermal dispatch curves are generated. This approach allows for preventive scheduling across the range of expected wind deviations and supports real-time adjustments through mechanisms such as redispatching, intraday markets, or secondary/tertiary regulation. Full article

This paper presents a closed-loop price–dispatch framework for park-scale virtual power plants (VPPs) with coupled electric–thermal processes under high penetrations of photovoltaics (PVs) and electric vehicles (EVs). The outer layer clears time-varying prices for operator electricity, operator heat, and user feed-in using an improved particle swarm optimizer with adaptive coefficients and velocity clamping. Given these prices, the inner layer executes a lightweight linear source decomposition with feasibility projection that enforces transformer limits, combined heat-and-power (CHP) and boiler constraints, ramping, energy balances, and EV state-of-charge requirements. PV uncertainty is represented by a small set of scenarios and a conditional value-at-risk (CVaR) term augments the welfare objective to control tail risk. On a typical winter day case, the coordinated setting aligns EV charging with solar hours, reduces evening grid imports, and improves a social welfare proxy while maintaining interpretable price signals. Measured outcomes include 99.17% PV utilization (95.14% self-consumption and 4.03% routed to EV charging) and a reduction in EV charging cost from CNY 304.18 to CNY 249.87 (−17.9%) compared with an all-from-operator benchmark; all transformer, CHP/boiler, and EV constraints are satisfied. The price loop converges within several dozen iterations without oscillation. Sensitivity studies show that increasing risk weight lowers CVaR with modest welfare trade-offs, while wider price bounds and higher EV availability raise welfare until physical limits bind. The results demonstrate an effective, interpretable, and reproducible pathway to integrate market signals with engineering constraints in park VPP operations. Full article