Search Results (69)

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article

Remote off-grid microgrids are often locked into diesel-backed operation because renewable variability creates multi-day and seasonal energy gaps that short-duration batteries cannot economically bridge. This work examines how renewable hydrogen can complement batteries and dispatchable biomass to push an existing hybrid microgrid toward near-autonomous, low-carbon operation, while remaining robust under future electrification demands. The analysis is based on real operational load insights from a remote off-grid system, combined with techno-economic optimization in HOMER Pro. The examined architecture includes PV panels, battery energy storage, a biomass CHP unit, and a diesel generator as backup; the hydrogen pathway additionally incorporates an electrolysis, storage and a PEMFC. Three scenarios are considered: a baseline PV/BAT configuration, an intermediate PV/BAT/BIO configuration that strengthens dispatchable renewable supply and short-term flexibility, and a PV/BAT/BIO/H₂ configuration targeting an increase in renewable energy penetration (REP). Results show that hydrogen integration shifts the system from curtailment-limited, diesel-supported operation to storage-enabled operation: surplus renewable production that would otherwise be curtailed is converted into hydrogen and later dispatched during prolonged deficits, enabling deep diesel displacement without compromising reliability. Hydrogen-enabled configurations achieve 90–99% REP, reduced diesel consumption, and lower CO₂ emissions, primarily by converting curtailed surplus into storable hydrogen. A rule-based EMS highlights technology complementarity across timescales, with batteries providing diurnal balancing and hydrogen covering longer deficits, which also reduces battery cycling stress. Overall, the study clarifies key design trade-offs, especially the need for coordinated PV expansion and storage sizing, and illustrates how a multi-storage portfolio can support high renewable penetration in such systems. Full article

This paper examines several portfolio rules for comparing performance against the Shanghai Composite Index. The investor can use mutual funds or sector-based Exchange-Traded funds (ETFs). Five different approaches are applied for analysis. Two core approaches are discussed in detail and compared to passive investing: The top-N strategy and the sector rotation strategy. The Top-N strategy shifts capital each period into the last period rank-N fund, and the sector rotation strategy ranks funds based on their performance in the preceding investment period, forming three baskets: “Winners”, “Median”, and “Losers”. Extensive statistical tests on more than 300 equity mutual funds are performed for the top-N strategy to evaluate performance persistence using quintile sorts, winner–loser spreads, and transition tests. In contrast, the sector-rotation strategy and a holdings-based replication strategy (constructed from annual reports and sector funds) are implemented as case studies using the ten largest funds. Their performance is evaluated using multiple return and risk metrics. Full article

We propose an agent-based, non-probabilistic framework for modeling the joint evolution of two discounted asset prices expressed in units of a third asset acting as numeraire. The framework is based on a trajectorial superhedging theory, in which pricing, arbitrage, and null events are defined purely in financial terms, without reference to probability measures or martingale assumptions. A central necessary theoretical requirement is that the global property $\left(L\right)$-a.e. holds, ensuring consistency of the model construction. Admissible price evolutions are described by multidimensional trajectory sets generated from observable price movements and operational rebalancing rules representing a prescribed class of agents. Within a fixed trajectory set, relative price bounds between the two assets are obtained via superhedging and subhedging by means of self-financing portfolios that trade one asset against the other. Full article

This study advances a hierarchical bilevel optimization paradigm to rigorously characterize the intertwined processes of strategic bidding and regulatory market participation in electricity systems increasingly dominated by renewable resources. At the upper tier, a central regulatory authority orchestrates participation rules, renewable integration mandates, and incentive mechanisms with the overarching aim of maximizing system-wide social welfare while driving decarbonization and reliability objectives. At the subordinate level, profit-maximizing generation firms—each managing heterogeneous renewable portfolios—pursue strategic bidding under deep uncertainty, conceptualized as a multi-agent game governed by imperfect and asymmetric information. The interaction between these tiers is formalized as a bilevel Stackelberg game that encapsulates price-responsive demand, intertemporal reserve adequacy, and policy-driven incentive structures. To ensure both computational tractability and robustness against strategic indeterminacy, the lower-level equilibrium is reformulated into a mathematical program with equilibrium constraints (MPEC), enabling a hybrid solution procedure that combines penalty-based regularization with exact decomposition algorithms. The framework’s efficacy is validated through a stylized multi-zone case study featuring diverse renewable assets and strategic participants, revealing how policy signals, capacity ceilings, and market power asymmetries reshape efficiency frontiers and bidding equilibria. A set of high-resolution post-processing visualizations is further employed to illustrate the dynamic evolution of marginal prices, equilibrium trajectories, and regulatory impacts under uncertainty. Full article

The enclave villages of Lake Malawi National Park (LMNP) are human settlements within a World Natural Heritage landscape. While social heterogeneity has been widely discussed in social–ecological systems (SES) scholarship, ethnic diversity has often remained analytically implicit. This study makes ethnic diversity central to analysis by examining how it shapes livelihoods, resource use, and governance across enclave villages. Drawing on an integrated household survey, key informant interviews, and extended field observations, and informed by collaboration theory, the SES framework, and scholarship on social differentiation, the analysis shows that ethnic diversity facilitates exchanges of fishing techniques, farming skills, ecological knowledge, and market linkages, producing plural and seasonally adaptive livelihood portfolios. Households routinely combine fishing, agriculture, tourism, petty trade, and forest use, contributing to diversified resource use. However, pressures on fish stocks, forest resources, and agricultural land highlight the need for more inclusive co-management. Emerging community-based institutions and collaborative initiatives increasingly facilitate coordination, rule-making, and shared stewardship. Overall, the findings identify practical and conceptual entry points through which ethnic diversity, ecological knowledge, and adaptive livelihoods can jointly support more resilient and inclusive pathways for sustainability at the crossroads of resource-dependent livelihoods and conservation, offering insights for socially diverse human–nature landscapes. Full article

Cryptocurrency markets are characterized by extreme volatility, fat-tailed return distributions, and frequent regime shifts, challenging traditional mean–variance portfolio optimization. In such environments, downside risk management becomes central, and tail-sensitive measures such as Conditional Value-at-Risk (CVaR) are increasingly adopted. However, empirical evidence remains mixed regarding whether CVaR-based strategies provide consistent protection across market regimes and tail depths. This study conducts a comprehensive empirical evaluation of tail-risk-based portfolio strategies using cryptocurrency data from 2018 to 2025. A rolling-window back-testing framework with weekly rebalancing is employed. We compare traditional benchmarks, moment-based and robust CVaR strategies, regime-dependent CVaR optimization, regression-enhanced ES–CVaR hybrids, and reinforcement learning-based CVaR policies. Performance is evaluated using mean return, volatility, CVaR at multiple confidence levels (90%, 95%, and 99%), and maximum drawdown. Market regimes are identified through volatility-based rules, and robustness is assessed via sensitivity analysis and block-bootstrap confidence intervals. The results show that no single strategy dominates across all conditions. Hybrid ES–Reg–CVaR strategies provide stable protection under moderate tail risk, reinforcement learning-based CVaR strategies adapt better to extreme tails, and regime-based CVaR optimization consistently limits drawdowns during stress periods. These findings demonstrate that effective CVaR-based portfolio management in cryptocurrency markets requires a regime- and tail-depth-dependent approach rather than a universal optimization rule. Full article

The mean-variance rule (M-V) conforms with the expected utility paradigm only in limited and economically unacceptable scenarios. Thus, the most widely employed portfolio-selection rule seemingly loses ground. We show with the commonly employed utility functions in economics, with a preference for a positive skewness, that choosing from the M-V efficient frontier conforms with expected utility maximization even with long investment horizon and skewed distributions of returns. The economic loss induced by choosing from the M-V frontier is negligible. Thus, the M-V rule is universal, or almost universal, provided that the commonly employed utility functions in economics are employed. This is an astonishing result that even Markowitz has not dreamed of. Full article

This paper proposes a continuous-time dynamic clearing model on a multilayer financial network to study systemic risk propagation and optimal intervention. The model incorporates interbank credit, equity crossholdings, and overlapping portfolios, and models bankruptcy as a jump event triggered by insolvency or illiquidity. Based on the system’s dynamic structure, we develop a model predictive control (MPC) framework that enables forward-looking and flexible allocation of limited bailout resources between debt relief and capital injection. Numerical results show that the proposed MPC strategy substantially outperforms both no-intervention and rule-based policies in terms of financial stability and resource efficiency. Compared with no intervention, the MPC strategy reduces the number of defaulting banks by approximately 56%. In contrast, the simple rule-based intervention achieves a reduction of about 48.83%, while improving rescue efficiency by approximately 28.57%. Overall, the framework provides a unified and effective approach to systemic risk control in financial networks. Full article

The assessment of Industrialized Building System (IBS) adoption in construction projects—a critical metric for evaluating prefabrication levels and construction modernization—remains largely manual, time-intensive, and prone to inconsistencies, with practitioners typically requiring 4–8 h to evaluate a single building using spreadsheet-based frameworks and visual documentation review. This paper presents a novel AI-enhanced workflow architecture that automates IBS scoring through systematic processing of Industry Foundation Classes (IFC) building information models—the first documented integration of web-based IFC processing, visual workflow automation (n8n), and large language model (LLM) reasoning specifically for construction industrialization assessment. The proposed system integrates a web-based frontend for IFC file upload and configuration, an n8n workflow automation backend orchestrating data transformation pipelines, and an Azure OpenAI-powered scoring engine (GPT-4o-mini and GPT-5-0-mini) that applies Construction Industry Standard (CIS) 18:2023 rules to extracted building data. Experimental validation across 136 diverse IFC building models (ranging from 0.01 MB to 136.26 MB) achieved a 100% processing success rate with a median processing duration of 61.62 s per model, representing approximately 99% time reduction compared to conventional manual assessment requiring 4–8 h of expert practitioner effort. The system demonstrated consistent scoring performance with IBS scores ranging from 31.24 to 100.00 points (mean 37.14, SD 8.84), while GPT-5-0-mini exhibited 71% faster inference (mean 23.4 s) compared to GPT-4o-mini (mean 80.2 s) with no significant scoring divergence, validating prompt engineering robustness across model generations. Processing efficiency scales approximately linearly with file size (0.67 s per megabyte), enabling real-time design feedback and portfolio-scale batch processing previously infeasible with manual methods. Unlike prior rule-based compliance checking systems requiring extensive manual programming, this approach leverages LLM semantic reasoning to interpret ambiguous construction classifications while maintaining deterministic scoring through structured prompt engineering. The system addresses key interoperability challenges in IFC data heterogeneity while maintaining traceability and compliance with established scoring methodologies. This research establishes a replicable architectural pattern for BIM-AI integration in construction analytics and positions LLM-enhanced IFC processing as a practical, accessible approach for industrialization evaluation that democratizes advanced assessment capabilities through open-source workflow automation technologies. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

This comprehensive review maps how China’s demand-side resources are aggregated, bid into markets, and monetized at the grid edge. We synthesize original studies and pilots to compare edge architectures for local estimation and privacy-preserving coordination, bidding frameworks that span deterministic, stochastic, chance-constrained, and robust designs, and retailer plan optimization that turns wholesale signals into simple user choices. Our headlined findings are fourfold. First, risk-aware bidding frameworks that use chance constraints or conditional value at risk (CVaR) reduce shortfalls without eroding expected revenue when penalties are strict and data are noisy. Second, joint design of retail prices with storage dispatch stabilizes delivery and consumer bills, raising participation and persistence. Third, intraday refresh of envelopes and redispatch improves balance and profit when provincial rules allow updates. Fourth, transparent baselines and settlement rules determine realized value and should be co-designed with aggregation and pricing. We organize reproducible metrics for revenue, reliability, latency, and consumer welfare, and provide simulation templates aligned with Chinese spot practice to enable head-to-head comparisons. The review closes with a research agenda on correlation modeling for heterogeneous portfolios, distribution-aware coordination, and long-run equipment impacts as areas where larger field trials and open data would unlock credible evaluation and faster deployment in China. Full article

This paper examines whether information-theoretic complexity measures enhance industry-group return forecasting and portfolio construction within a machine-learning framework. Using daily data for 25 U.S. GICS industry groups spanning more than three decades, we augment gradient-boosted decision tree models with Shannon entropy and fuzzy entropy computed from recent return dynamics. Models are estimated at weekly, monthly, and quarterly horizons using a strictly causal rolling-window design and translated into two economically interpretable allocation rules, a maximum-profit strategy and a minimum-risk strategy. Results show that the top performing strategy, the weekly maximum-profit model augmented with Shannon entropy, achieves an accumulated return exceeding 30,000%, substantially outperforming both the baseline model and the fuzzy-entropy variant. On monthly and quarterly horizons, entropy and fuzzy entropy generate smaller but robust improvements by maintaining lower volatility and better downside protection. Industry allocations display stable and economically interpretable patterns, profit-oriented strategies concentrate primarily in cyclical and growth-sensitive industries such as semiconductors, automobiles, technology hardware, banks, and energy, while minimum-risk strategies consistently favor defensive industries including utilities, food, beverage and tobacco, real estate, and consumer staples. Overall, the results demonstrate that entropy-based complexity measures improve both economic performance and interpretability, yielding industry-rotation strategies that are simultaneously more profitable, more stable, and more transparent. Full article

This paper compares the rulebooks of five main Shariah-compliant equity indices—DJIMI, KLSI, FTSE Shariah, MSCI Islamic, and STOXX Europe Islamic 50—inside one fixed S&P 500 stock list from Q1 2019 to Q4 2023. For each index, we build both equally weighted and market-capitalization-weighted portfolios, then check their performances with the Sharpe, Treynor, and Jensen’s alpha ratios. All Islamic portfolios beat the regular S&P 500 after adjusting for risk, with STOXX as the most stable winner. Its market-cap version reaches a level of 253.01 by Q4 2023, far above the S&P 500 level of 210.46. Market-cap portfolios, in general, perform better than equally weighted ones. Furthermore, STOXX offer better protection in rough markets, while DJIMI shows relatively better performance when prices recover. Most rule sets cause small advantages to the Islamic portfolios compared to conventional ones, but STOXX’s 33% limit on leverage and liquidity results in higher Sharpe ratios. These results suggest that screening details shape portfolio behavior and point to the need for one clear, shared Shariah rulebook so investors can compare products with confidence. From a business ethics view, our study also shows that strict and open screening brings a real “moral dividend”, as follows: smaller losses when markets fall and stronger risk-adjusted returns overall, linking faith-based rules to the wider talk on responsible investing and stakeholder welfare. Full article