Search Results (798)

This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance matrix of normalized asset prices and subsequently incorporated into the classical Markowitz mean–variance framework to obtain analytical representations of the global minimum-variance portfolio, the mean–variance efficient portfolio, and the corresponding efficient frontier. The proposed methodology establishes a direct connection between continuous-time stochastic asset-price modeling and portfolio optimization through a model-implied covariance structure. Its practical implementation is investigated through both numerical experiments and an empirical study using daily stock price data from 20 constituents of the $S\&P$ 500 index over the period 2020–2024. Monte Carlo simulations demonstrate the finite-sample sensitivity of portfolio optimization to covariance estimation, while the empirical analysis illustrates how the estimated model parameters, obtained using the maximum likelihood framework of Aït-Sahalia for discretely sampled diffusion processes, can be incorporated into the analytical covariance matrix for constructing efficient frontiers under realistic market conditions. Overall, the proposed framework provides an analytically tractable methodology that integrates continuous-time asset pricing models with classical mean–variance portfolio optimization, offering a coherent model-based covariance representation for portfolio selection under time-varying market environments. Full article

China’s 2026 land-use policy mandates that commercial and residential construction remain within existing urban footprints, making urban renewal the primary source of spatial capacity for future urban development. Understanding how diverse stakeholders make decisions regarding urban renewal and shape the resulting spatial configurations is therefore of critical importance. This study develops an integrated multi-agent system (MAS) and cellular automata (CA) model to simulate the spatial dynamics of urban renewal. The model explicitly incorporates the location preferences of three key agents—government, developers, and residents. Using Wuhan as a case study, we project land-use patterns for 2035 under three scenarios: a traditional development scenario, a multi-agent simulation (MAS) scenario, and an urban renewal scenario. The findings reveal that (1) urban renewal significantly improves land-use patterns by curbing urban expansion and reducing the loss of farmland and forest cover; (2) government agents exert the strongest driving force on construction land expansion (contribution coefficient: 0.1758), while residents demonstrate a discernible influence on the selection of renewal sites (0.1199), with developer preferences largely aligning with government priorities; (3) a marked preference divergence exists among agents—within central urban areas, resident selection probabilities are only 60–70% of those of government and developers, indicating a notable mismatch between market demand and planning objectives. Moreover, more than 74% of renewal areas are projected to generate economic returns below renewal costs, signaling a potential financial sustainability risk. These results suggest that while urban renewal can effectively slow urban sprawl and mitigate ecological land loss, it simultaneously necessitates multi-stakeholder governance frameworks and sustainable financing mechanisms to reconcile competing interests and reduce fiscal vulnerabilities. The proposed simulation framework offers quantitative support for spatial policy making in China’s future land stock-constrained urban development contexts. Full article

This study examines the relationship between corporate financial technology (FinTech) disclosure and environmental, social, and governance (ESG) reporting performance among non-financial firms listed on the Saudi Stock Exchange (Tadawul), with a focus on the post-COVID period from 2021 to 2024. Using an ESG Disclosure Index (ESGDI) constructed from annual reports and a textual measure of FinTech adoption, the analysis provides market-level evidence on the evolution of digital transformation and ESG disclosure in Saudi Arabia. Descriptive results indicate that ESG reporting among Tadawul firms is moderate yet heterogeneous, with governance disclosure consistently stronger than environmental and social components. Correlation analysis indicates a positive association between FinTech disclosure and overall ESG disclosure, particularly within the environmental pillar. Regression results further show that the firms with stronger FinTech disclosure tend to report higher ESGDI scores. The two-way fixed effects (TWFE) model yields statistically significant results, and the direction of the relationship remains consistent with theoretical expectations. Pillar-level analysis suggests that digital transformation is most closely aligned with environmental reporting. Taken together, the results indicate that sustainability disclosure and digital capabilities appear to co-develop in the Tadawul market. Businesses may improve their ability to track, organize, and disseminate ESG-related data by investing in digital reporting systems, analytics, and technology modernization. In this way, FinTech serves as a governance-supporting instrument that improves transparency and reporting discipline in addition to being a financial innovation. This study adds to the expanding body of knowledge by providing important emerging-market-level evidence from the Saudi capital market and highlighting how FinTech can support sustainability-driven growth in an institutional context undergoing rapid transformation. Full article

This study provides an exploratory predictive analysis to examine how different dimensions of digital infrastructure—capital market development, digital payment adoption, e-commerce penetration, and market volatility—predict the financial performance metrics of fintech ventures in Egypt. Using panel data from ten fintech ventures listed on the Egyptian Stock Exchange over the period 2017–2023, the research employs Random Forest machine learning algorithms alongside Logistic Regression as a baseline comparator. Feature importance analysis identifies the most significant predictors of profitability across four performance metrics: gross revenue, sales growth, gross margin, and net profit margin. This study employs Random Forest with five-fold cross-validation. Hyperparameters were optimized via grid search, and feature importance scores are reported with cross-validation standard deviations. To address panel structure concerns, we additionally employ leave-one-firm-out cross-validation. All findings reflect predictive associations only; no causal claims are made due to potential reverse causality. Findings show that capital market development emerges as the most important predictor across all profitability metrics, accounting for 45% of feature importance for net profit margin and 42% for gross revenue (mean importance across five folds; SD = 0.07–0.08). Digital payment adoption exhibits a paradoxical dual association—positively associated with revenue and margins through operational efficiency (38% importance for gross margin; SD = 0.08) while negatively associated with sales growth (22% importance; SD = 0.10). Gross online sales show limited predictive efficacy, affecting only gross margin. Market volatility correlates solely with sales growth. Random Forest consistently outperforms Logistic Regression across all models, with accuracy rates ranging from 68% to 76% (compared to a chance level of 50% and a majority-class baseline of 52–58%). Due to the limited sample of 70 firm-year observations, these findings must be interpreted as strictly exploratory and hypothesis-generating; they apply uniquely to publicly listed fintech firms on the Egyptian Stock Exchange and cannot be generalized to private, early-stage, or unlisted fintech startups without further empirical validation. Full article

This article examines Liquid Equity Rewards (LERs), a proposed blockchain-enabled mechanism designed to provide shareholders with time-weighted, utility-only incentives as a potential tool for improving corporate governance. LERs employ a dual architecture comprising voucher-based rewards for off-chain equities and programmable on-chain units for tokenized stocks to encourage shareholder retention amid proxy battles, activist challenges, and corporate political complexities. Drawing on NASDAQ’s tokenized stock framework, stablecoin infrastructure, and DeFi liquid staking principles, this article develops a conceptual and normative framework for LERs and evaluates its potential effectiveness relative to conventional defenses such as poison pills. The analysis assesses LER’s plausible legal compatibility with Delaware corporation law, U.S. securities rules, and the EU’s MiCA framework, while acknowledging that definitive legal conclusions require case-specific adjudication and future regulatory interpretation. The article advances four testable hypotheses regarding LER’s potential to mitigate stock price volatility, reduce activist success rates, and address ESG, M&A, and political expenditure disputes in a market context shaped by shareholder activism. The proxy-fight context is the principal application; ESG, M&A, political-spending, and executive-compensation contexts are discussed as illustrative extensions of the framework rather than as equally mature use cases. A comparative evaluation against existing governance mechanisms and a cost–benefit analysis suggest that LER’s governance enhancements and market opportunities may outweigh implementation challenges, subject to empirical validation. This article contributes a structured analytical framework and identifies conditions under which LERs could offer a scalable, transparent alternative that fosters stakeholder alignment. Full article

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI capabilities onto the full digital twin lifecycle—from sensor-driven calibration through real-world deployment to district-scale operation. This review addresses this gap through six objectives: analyzing AI-enhanced modeling approaches for building digital twins; examining data infrastructure and interoperability requirements; evaluating validation, calibration, and uncertainty quantification practices; synthesizing real-world implementation evidence across diverse building typologies; assessing integration with renewable energy systems and smart grids; and identifying challenges, research gaps, and a strategic deployment roadmap. Physics-based, data-driven, and hybrid modeling strategies occupy distinct and complementary roles. Physics-informed surrogate models preserve thermodynamic interpretability while reducing computational overhead; deep learning architectures—including recurrent networks and reinforcement learning agents—deliver adaptive control; and federated learning frameworks enable privacy-preserving optimization across distributed building portfolios. Rigorous multi-metric validation aligned with established calibration standards proves essential for trustworthy deployment, while Bayesian and ensemble-based uncertainty quantification methods emerge as indispensable components of operationally credible digital twins. Evidence from real-world deployments in residential, commercial, healthcare, and industrial facilities confirms that AI-powered digital twins consistently deliver substantial energy savings and measurable improvements in occupant comfort. Scaling to district and urban levels introduces challenges in data governance, computational architecture, and multi-stakeholder coordination, yet federated digital twin frameworks are beginning to demonstrate viable pathways. The paper concludes with a decade-long strategic roadmap spanning technological maturation, market development, regulatory alignment, and decarbonization impact—positioning AI-enhanced digital twins not as incremental optimization tools, but as the foundational infrastructure for the coordinated transformation of the global building stock. Full article

This paper examines the association between external verification of sustainability reports and ESG performance of Saudi-listed firms from the years 2014–2021. With regard to the Saudi stock exchange (Tadawul) dataset consisting of 188 firm-year observations, it is concluded that external sustainability report verification and ESG performance are positively associated. This study constructs the premise that the enhancement of credibility and transparency of sustainability reports in turn fosters stakeholder confidence. This paper documents a positive association between voluntary assurance and ESG performance from an emerging market perspective, which broadens the scope of the ESG literature. This observation particularly justifies the need to endorse more assurance services in support of sustainable development and to strengthen the reporting frameworks and policies. The study results support the objectives of Vision 2030, specifically the pillars of promoting environmental sustainability, corporate transparency, and governance. The evidence aligning national goals to encourage transparency in corporate systems and sustainability in assurance services is the positive relationship between ESG and sustainability reporting assurance. Moreover, the results highlight Saudi Arabia’s dedication to the United Nations Sustainable Development Goals, specifically SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), as they underscore the role of assurance and disclosure practices in fostering sustainable business practices in Saudi Arabia. Full article

This study investigates the dynamic interactions between Bitcoin, global financial indicators, and the Kazakhstan Stock Exchange (KASE) index within a VAR-based econometric framework, addressing a notable gap in the literature on emerging and shallow financial markets. While prior research predominantly focuses on developed economies, evidence suggests that cryptocurrency–stock market linkages are time-varying, crisis-sensitive, and often asymmetric. In this context, the present study examines both short-term causality structures and shock transmission mechanisms among KASE, Bitcoin (BTC), oil prices, the U.S. dollar index (DXY), and the VIX using monthly data for the period 2017M01–2026M04. Empirical findings indicate that, despite the absence of statistically significant Granger causality from individual global variables to KASE, the joint dynamics suggest a non-negligible, albeit indirect, interaction structure. Variance decomposition and impulse-response analyses further reveal that KASE dynamics are predominantly driven by its own shocks, reflecting the relatively segmented and internally driven nature of the market. Diagnostic tests confirm the robustness of the model, with no evidence of serial correlation or heteroskedasticity in residuals. These findings are consistent with the structural characteristics of the Kazakh financial system, including limited market depth, lower investor participation, and high sensitivity to domestic macroeconomic conditions. Unlike developed markets where stronger integration is observed, KASE appears only weakly connected to global financial and cryptocurrency markets. The study contributes to the literature by providing empirical evidence from a frontier market and highlights the importance of considering country-specific structural factors when evaluating financial integration. Policy implications emphasize the need to enhance market depth, transparency, and investor confidence to strengthen the responsiveness of KASE to global financial developments. Full article

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

Do enterprises with high tax contributions exhibit green transformation inertia due to the alignment of government and enterprise interests? Using data from the Chinese A-share market, this paper finds that high tax contributions significantly inhibit corporate green transformation. The mechanism lies in the fact that local governments implement inclusive regulation for high-tax-contribution enterprises, reducing their environmental compliance pressure and cutting environmental protection investment; at the same time, the halo effect of tax contributions provides a cover for enterprises’ low-quality environmental information disclosure. Exclusion analysis shows that financing constraints are not a limiting factor. Heterogeneity analysis indicates that the above effects are more prominent in samples with close government–enterprise connections, fierce industry competition, and loose local environmental regulations. Economic consequences show that insufficient green transformation caused by high tax contributions ultimately damages enterprises’ environmental performance and long-term sustainable development capabilities, and significantly increases stock price crash risk. Optimizing internal management and external supervision within enterprises can help mitigate this negative effect. This paper reveals the hidden obstacles to corporate green transformation under the symbiosis of government and enterprise interests, providing a new perspective for understanding the complexity of environmental governance. Full article

This paper develops a theoretical framework for corporate financial resilience under incomplete-market conditions, in which firm-specific equity derivatives are structurally unavailable or only weakly developed. Using the Romanian capital market and the Bucharest Stock Exchange (BSE) as a focal context rather than as the paper’s sole relevance, the study links Tobin’s q, liquidity policy, capital structure, ESG governance, and the domestic quasi-risk-free benchmark ($R_f^{ROM}$) to explain how firms may partly support financial flexibility when direct hedging instruments are missing. This is a conceptual framework paper: it does not provide empirical tests or validated firm-level results but instead formulates empirically testable propositions (P1–P4) and a future empirical research agenda. Building on selective hedging theory, Tobin’s q investment theory ESG finance and organisational resilience research, the framework identifies six assumptions of the classical model that are violated and four limitations affecting q measurement on the BSE. Within thin and illiquid markets, Tobin’s q is treated as a noisy, imperfect valuation signal rather than as a precise decision threshold. The paper contributes by delimiting the scope conditions under which classical q-based and selective-hedging assumptions weaken in derivative-constrained markets by reframing financial flexibility as a conditional resilience mechanism rather than a hedge substitute and by specifying falsifiable propositions for future empirical testing in the Romanian capital-market context. Full article

Hidden hunger and grassland degradation represent interconnected governance challenges in northern China’s pastoral areas. Nutrition-sensitive agriculture (NSA) has been conceptualised largely around crop-based systems, with limited attention to grassland grazing systems, where nutritional value is shaped by ecology, feeding practices, seasonality, local knowledge, and market institutions. Drawing on five rounds of fieldwork (2019–2025) across meadow, typical, and desert steppes in Inner Mongolia, this study employs a multi-case comparative design involving 92 semi-structured interviews, 58 policy documents, and long-term observations. Using reflexive thematic analysis, we develop an ecology–nutrition synergy framework to explain local practices and institutional constraints in nutrition-sensitive livestock farming. Three pathways are identified: grass–livestock nutritional balancing, scientific valorisation of native forage, and market experimentation linking ecological origin to nutritional quality. These pathways operate through three mechanisms: ecological mediation of nutritional quality, endogenous quality fluctuation as an inherent feature, and scientific codification of traditional pastoral knowledge. Four structural dilemmas constrain scaling: incompatibility between natural quality fluctuation and industrial standardisation; absence of institutional trust in nutritional premiums; short-term trade-offs between stocking control and nutritional enhancement; and fragmented cross-sectoral governance. The study extends NSA to grassland systems and offers a framework for integrating ecological protection, livestock quality, and nutrition-oriented governance in arid and semi-arid rangelands. Three theoretical contributions are advanced: (i) extending NSA’s conceptual boundary from cropping systems to natural grassland pastoral systems; (ii) embedding a nutrition-output dimension within Ostrom’s SES framework, thereby creating a triple-nested ecology–nutrition synergy framework; and (iii) specifying three grazing-system-specific mechanisms that distinguish grassland livestock systems from both crop-based and confined animal production systems. Full article

Decades of cultivation and the often exclusive use of mineral fertilisers as a substitute for organic inputs have reduced the soil organic carbon (SOC) content of agricultural soils, meaning they now represent a potential sink for carbon sequestration to mitigate climate change and improve soil function. As well as being a legacy of management, SOC will also be dependent on local scale climate, topography, and soil properties; accounting for this local context is important when benchmarking fields and quantifying the potential for additional carbon sequestration. We developed a landscape-scale methodology, using a handheld infrared device, for baselining SOC stocks in the top 30 cm across a 45,000 ha farm cluster in the UK. The cluster is exploring opportunities for landscape-scale environmental improvement with a focus on natural flood protection and water pollution reduction through conversion of arable land to permanent grassland. We used the baseline data to estimate additional benefits of arable reversion for soil carbon sequestration. Because all the farms in the cluster share the same pedoclimatic conditions, variance in SOC at the field scale could be confidently attributed to differences in soil type and land use. Average SOC stocks in arable and permanent pasture fields were 103.9 and 140.3 Mg C ha⁻¹, respectively. Variance in %SOC was modelled using soil series, sample depth, land use, and clay content, and fields were benchmarked based on deviation from the expected value. The fields with the largest SOC stocks were identified and used as references to predict future potential sequestration. The conversion of arable land to permanent pasture resulted in a predicted average uplift in SOC of 55.0 Mg C ha⁻¹. Our landscape-scale methodology provides robust evidence on current and future carbon stocks for public subsidy schemes and natural capital markets that account for local constraints and opportunities. Full article

As the main drivers of the market economy, enterprises must fully grasp the importance and urgency of building an ecological civilization and hasten the transition to green practices. Due to the fundamental goal of enterprises being to maximize profits, the cost-effectiveness of enterprises is directly related to their initiative and implementation effectiveness in carrying out green transformation. This article uses panel data from heavily polluting companies listed on the Shanghai and Shenzhen stock exchanges in China from 2011 to 2020 to empirically test the cost-economic effects of corporate green transformation (CGT). Results reveal: (1) CGT has a positive effect on firm performance, and managerial incentives and capital intensity can strengthen the positive relationship between CGT and firm performance. In addition, in economically developed regions with high levels of environmental regulation, the green transformation of heavily polluting enterprises with lower management agency costs has a more significant positive impact on corporate performance. (2) Environmental costs mediate the link between CGT and firm performance, with the mediating effect of corporate environmental costs playing a role only in the non-three major economic circles. Full article