Search Results (751)

Despite the growing importance of the Saudi capital market, sectoral-level volatility connectedness within Tadawul remains largely unexplored. This study contributes to the literature by applying the Diebold–Yılmaz framework to examine volatility connectedness across 16 Tadawul sectors over the period January 2017 to December 2024. Total, directional, and net pairwise volatility spillovers are quantified from daily closing prices using a VAR(4) model combined with generalized forecast error variance decomposition. The static analysis reveals a high overall connectedness of 80.49%, indicating that cross-sectoral spillovers account for the majority of volatility fluctuations. Materials, Transportation, and Real Estate Management and Development are identified as the dominant net transmitters of volatility, while Utilities and Telecommunication Services are persistent net receivers. The dynamic analysis shows that sectoral connectedness is highly time-varying, peaking at 93.70% during the COVID-19 period, with additional episodes of elevated spillovers during 2022–2023. The network analysis reveals that the strongest pairwise linkages exist among Materials, Transportation, Real Estate Management and Development, and Banks, forming the core of the spillover network. While block-bootstrap results reinforce the identification of dominant net transmitters and receivers, they reveal substantial uncertainty in the rank-order of intermediate sectors, necessitating a more nuanced interpretation. The results are robust to alternative rolling window sizes and forecast horizons. These findings have important implications for portfolio diversification, sectoral risk monitoring, and macroprudential policy in the Saudi capital market. Full article

Accurate price forecasting and portfolio optimization in emerging financial markets remain challenging due to the non-stationary dynamics, structural breaks, and heterogeneous behavior of traded instruments. This paper proposes the Time-series Adaptive Forecast Ensemble (TAFE), a method that combines single popular forecasting methods, such as ARIMA, by using an algorithm derived from both the simulated annealing (SA) and Threshold Accepting algorithms. The TAFE is applied to twenty-four weekly price series of Mexican exchange-traded funds (ETFs) and Real Estate Investment Trusts (FIBRAs) over the period 2020–2025. A top-K pre-selection strategy is used, mitigating the adverse cross-model interaction effect of some assets over others, in other words, reducing the propagation of errors from poorly performing base learners. In addition, the sample results show that the TAFE achieves the lowest mean SMAPE across the panel, with statistical superiority over the equal-weight benchmark and a Hybrid Model, confirmed by Diebold–Mariano and Harvey–Leybourne–Newbold tests. Out-of-sample evaluation over a 26-week horizon reveals a regime-shift-driven performance reversal consistent with the bias–variance tradeoff in adaptive combination schemes. Portfolio optimization using SA-generated forecasts yields with an expected return of 35.77%; thus, the model presents a slight overestimation of the return, with a variance of 2.4%. However, it has an acceptable level of risk. Full article

Effective management of organic wastes is essential for green and low-carbon development. Conventional technologies, including incineration, pyrolysis, hydrothermal carbonization (HTC), gasification, anaerobic digestion (AD), and composting, have supported waste reduction and basic resource recovery, but they remain limited in high-efficiency conversion and high-value utilization. This review comparatively evaluates these conventional routes together with advanced and intensified technologies, including microwave-assisted pyrolysis (MAP), plasma treatment, supercritical water gasification (SCWG), and flash joule heating (FJH), with emphasis on suitable feedstocks, performance characteristics, application boundaries, and integration potential. In general, wastes with high moisture content are more suitable for HTC, AD, and SCWG, whereas relatively dry wastes and wastes with high carbon content are more suitable for pyrolysis, gasification, plasma treatment, and FJH upgrading. The review also discusses representative integrated pathways, such as HTC-SCWG, pyrolysis and plasma coupling, AD and gasification coupling, and pyrolysis and FJH coupling, which may improve carbon conversion, broaden product portfolios, and reduce residual pollutants. However, large-scale implementation is still constrained by feedstock heterogeneity, heat and mass transfer limitations, catalyst deactivation, reactor corrosion, and system cost. Overall, no single technology is universally optimal; technology selection should depend on feedstock properties, moisture content, and target products. Full article

Insurance pricing plays a central role in risk management and financial decision-making, as accurate premium estimation directly impacts portfolio stability and profitability. This study investigates insurance pure premium estimation by integrating classical actuarial models with modern machine learning techniques. We compare the traditional frequency–severity decomposition framework with direct modeling approaches, including XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, generalized additive models, and XGBoost. For claim severity, we compare a Gamma generalized linear model with XGBoost. The results show that XGBoost improves predictive performance for both components based on the evaluation metrics considered. Within the decomposition framework, the XGBoost–XGBoost model achieves the lowest prediction error among the models considered. However, lift-based analysis reveals that the XGBoost–Gamma model provides superior risk segmentation, highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling approaches, while competitive, do not consistently achieve lower error than the decomposition framework across the evaluation metrics considered. Overall, the findings demonstrate that machine learning enhances predictive performance, but its effectiveness is maximized within the frequency–severity framework. The results highlight the importance of both frequency and severity modeling in insurance pricing, while suggesting that their relative contributions to risk segmentation depend on model specification and evaluation criteria. These findings have important implications for risk management and pricing strategies in insurance portfolios. Full article

We propose various hybrid predictive systems to forecast the Bitcoin next-day price. In particular, we combine the decomposition methods based on signal processing techniques including maximum overlap discrete wavelet transform (MODWT), empirical wavelet transform (EWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD) for feature extraction from original price series. Then, the extracted features are fed to the machine learning models for training and forecasting. We implemented five machine learning models, including regression Gaussian process (RGP), support vector regression (SVR), k-nearest neighbors algorithm (kNN), regression trees (RT), and feedforward neural networks (FFNN). The grey wolf optimization (GWO) algorithm is employed for hyperparameter optimization of the machine learning models. The root mean squared error (RMSE) is used for the evaluation and comparison of 20 hybrid predictive systems. The simulation results show that the RGP-GWO-VMD hybrid predictive system achieved the lowest forecasting error. In addition, RGP-GWO yielded on average the lowest forecasting error across all of the machine learning systems. Furthermore, among signal decomposition methods, the lowest forecasting error is generally achieved under the EWT. Hence, we presented the best results in forecasting Bitcoin prices from 20 hybrid prediction systems to serve as the baseline for future work and to guide traders, investors, and portfolio managers. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

Oil price volatility forecasting remains a central challenge in financial risk management and macroeconomic policy, particularly when market uncertainty stems from expert judgment, geopolitical assessments, or imprecisely quantified fundamentals rather than statistical frequencies. We propose a bivariate uncertain vector autoregressive (UVAR) model to jointly forecast crude oil realized volatility (RV) and the Overall Equity Market Volatility (EMV) tracker within the framework of uncertainty theory, using 204 monthly observations from January 2008 to December 2024. Three cross-validation schemes consistently identify UVAR(1) as optimal, and least-squares estimation reveals an asymmetric bidirectional relationship between the two variables. Residual analysis and uncertain hypothesis testing confirm the adequacy of the fitted model at both $\alpha =0.05$ and $\alpha =0.10$, the conventional significance levels reported in the empirical literature. Relative to a univariate UAR benchmark, UVAR(1) yields lower residual variance and, on average, narrower 95% confidence intervals for both variables and remedies the hypothesis-test failure of UAR(1) for realized volatility; while its fixed-origin ATE is marginally higher on the EMV tracker, this is more than offset by substantial gains on realized volatility, the primary economic variable of interest. Against a probabilistic VAR(1) benchmark, UVAR(1) attains marginally lower out-of-sample sum of squared mean errors while uniquely supporting principled uncertain-statistical inference under non-frequentist data-generating mechanisms. These results provide principled inputs for value-at-risk assessment and portfolio hedging in oil-dependent economies. Full article

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility. Full article

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article

The agrifood sector faces well-documented barriers to climate-smart agriculture (CSA) adoption, reflecting systematic difficulty identifying where digital tools address specific stakeholder needs rather than a technology shortage. This paper presents a prescriptive framework for mapping digital innovation opportunities in complex, multi-stakeholder agrifood systems. Grounded in Jobs-to-be-Done (JTBD) theory and structured as a two-dimensional matrix of meta-jobs and value chain segments, the framework was developed through a design science research (DSR) paradigm evaluated on utility, coherence, and actionability. Construction involved a purposive synthesis of three literature streams and iterative refinement through 136 stakeholder engagements within a six-month university-affiliated startup studio cycle. Applied to climate-smart agriculture, the framework produces 54 strategic opportunity areas across nine meta-jobs and six value chain segments. A cross-cutting pattern analysis identifies three structural constraints: agricultural data fragmentation and absence of interoperability standards; inadequate measurement, reporting, and verification infrastructure; and misalignment between financing mechanisms and climate-smart time horizons. The framework equips entrepreneurs and investors with a segment-differentiated opportunity map, supports agribusiness portfolio analysis, and directs policymakers toward three priority areas where coordinated systemic action generates value across the opportunity landscape. Full article

Analysis of Estates Return Information Collection (ERIC) 2018/19–2024/25 data for 1104 acute NHS hospital sites in England found persistently high energy use intensity (EUI), averaging 211 kWh/m² in 2024/25, with total acute-sector energy use of 9.99 billion kWh, with approximately 75% derived from gas. Longitudinal trends indicated relatively stable EUI despite portfolio growth. Cross-sectional exploratory analyses for 2024/25 showed that clinical floor area share (mean 59%) exhibited the strongest observed association with EUI (r = 0.52, R² = 0.27), followed by gross internal area (r = 0.39, R² = 0.15) and backlog intensity (r = 0.23). Associations between building age cohorts and EUI were generally weak or negligible, except for a weak positive association for 1985–94 buildings (r = 0.064) and a moderate negative association for 2005–14 buildings (r = −0.126). Among the decarbonization and operational indicators examined, renewable electricity fraction showed the strongest bivariate association with EUI (R² = 0.224), followed by water intensity (R² = 0.101), gas share (R² = 0.085), LED coverage (R² = 0.027), climate incidents (R² = 0.020), and waste intensity (R² = 0.004). Sites with heat decarbonization plans, high LED coverage, or heat pump installations tended to exhibit higher EUI values alongside differing renewable electricity uptake patterns, potentially reflecting the prioritization of interventions at more energy-intensive facilities. Overall, the findings suggest that hospital energy intensity is associated with functional mix, estate characteristics, and decarbonization-related indicators, although these relationships should be interpreted as exploratory associations rather than independent causal effects. The study provides a national-scale exploratory benchmarking assessment intended to inform future multivariable and longitudinal research on NHS estate decarbonization strategies. Full article

Recent advances in large language models (LLMs) have raised questions about their potential role in portfolio allocation beyond traditional sentiment analyses. This study investigated whether LLMs, when prompted directly, can autonomously generate portfolio weights that compete with classical optimization and AI-enhanced strategies. We evaluated seven medium-sized open-source LLMs—Gemma-7B, Mistral-7B, Jansen Adapt-Finance-Llama2-7B, DeepSeek-R1-8B, QuantFactory Llama-3-8B-Instruct-Finance, Qwen-7B, and Llama2-7B—using systematic prompt engineering and temperature tuning. Portfolios were constructed from financial news headlines for S&P 500 equities and benchmarked against mean–variance optimization (MVO), the Black–Litterman model, AI-driven optimizers, and naive diversification strategies. The results show that, while LLM-generated portfolios outperformed naive diversification (Sharpe ratio up to 0.741), they lagged behind AI-optimized benchmarks (Sharpe ratio up to 1.361). A transaction cost analysis revealed that low-turnover LLM strategies retain their competitiveness post-costs, surpassing cap-weighted benchmarks. Statistical tests confirmed significant performance differences ($p\le 0.01$). These findings highlight the ability of LLMs to extract signals from unstructured text, but also their limitations without explicit optimization. Future research should explore hybrid frameworks that combine LLM reasoning with quantitative optimization for cost-sensitive environments. Full article

Credit risk assessment under severe class imbalance requires both structured imbalance correction and principled decision rules, yet most studies treat these as independent steps. This study develops a general integrated three-layer framework for credit risk assessment under class imbalance. The first layer introduces Sequential Hybrid Data Oversampling (SHDO), which sequentially applies five complementary oversampling techniques to enrich minority-class representation in mixed-type credit data. The second layer formulates credit approval as a decision-theoretic optimisation problem: a closed-form optimal threshold is derived under asymmetric costs, extended to constrained portfolios via a Lagrangian formulation with Karush–Kuhn–Tucker conditions, and further extended to minimax-robust decision making under estimation uncertainty. The third layer compares eleven classifiers under a unified evaluation protocol with an ablation isolating the effect of SHDO. The framework is empirically validated on the Home Credit Default Risk dataset, which is used as an illustrative case study rather than defining the scope of the contribution. On the held-out test set, XGBoost trained with SHDO achieves the highest minority-class F1 (0.254), while gradient-boosted models collectively attain ROC-AUC values of 0.713–0.750, outperforming classical baselines (0.540–0.620). The ablation confirms that without SHDO, all models exhibit near-zero minority-class recall despite adequate ranking ability. SHAP analysis on XGBoost confirms that the learned risk structure aligns with established creditworthiness determinants. The decision framework converts these probability estimates into analytically justified approval thresholds responsive to economic parameters, institutional constraints, and estimation uncertainty. Full article

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict–optimize–update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity–mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity–mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R²) of 0.89 for duration prediction, as well as an MAE of 7.4 × 10⁴ CNY and an R² of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule–cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects. Full article

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article