Search Results (1,307)

This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent financial index relationships which enables the detection of symmetric market dependencies including mutual spillover effects and bidirectional influence patterns. The symmetric network structures become most important during financial instability because market interdependencies strengthen at such times. The evaluation process compares these models against XGBoost and Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) traditional forecasting approaches. The results of 30 time-series cross-validation experiments show that GNN models produce lower RMSE and MAE values, especially during financial crises and recovery phases and volatile market periods. The models show reduced advantages when markets remain stable. The research demonstrates that graph-based forecasting models which incorporate symmetry effectively detect complex financial relationships which leads to important implications for investment strategies and financial risk management and global economic forecasting. Full article

Despite numerous global initiatives, such as the Sustainable Development Goals (SDGs) and the implementation of environmental, social, and governance (ESG) metrics aimed at mitigating climate change, promoting social welfare, and addressing a variety of other causes, progress has been significantly slower than expected, particularly in developing economies. Thus, we attempted to link corporate ESG to sustainable development. It was also investigated whether ESG contributes to a reduction in corporate risk. Using panel data and the Generalized Method of Moments (GMM) technique, we examine the relationship between ESG scores and important financial risk indicators such as systematic risk (beta), stock price volatility, unsystematic risk, and the cost of capital (WACC). The findings show that corporations place a disproportionate emphasis on governance (G) rather than environmental (E) and social (S) characteristics. ESG and G governance were also found to be statistically significant predictors of financial risk. This disparity shows that companies may be using high governance scores to conceal underperformance in environmental and social issues, raising worries about greenwashing and superficial compliance. As a result, their contributions to SDGs such as affordable and clean energy (SDG 7), climate action (SDG 13), and reduced inequalities (SDG 10) are minimal. The findings highlight the need for a more open, balanced, and integrated ESG approach, one that not only promotes sustainable development but also improves long-term financial resilience. Full article

Optimization of stock selection strategies has been a topic of interest in finance. Although deep learning models have demonstrated superior performance over traditional methods, there are still shortcomings. For example, previous studies do not provide enough explanation for feature selection and usually use features such as closing price directly to make predictions; for example, most studies predict the trend of multiple stock indices or only individual stocks, which is difficult to be directly applied to actual stock selection. In this paper, a multivariate hybrid neural network model CNN-LSTM-GNN (CLGNN) for stock prediction is proposed, in which the CNN and the LSTM modules analyze the local and the whole, respectively, while the multivariate time series GNN module is added to explore the potential relationships between the data through the graph learning, graph convolutional, and temporal convolutional layers. CLGNN analyzes the potential relationships between the data based on the returns to classify stocks, and then develops a stock selection strategy, and directly outputs the returns and stock codes. In this paper, a hybrid filter approach based on entropy and Pearson correlation is proposed for feature selection, and experiments are conducted on all stocks in the CSI All Share Index (CSI); the results show that among multiple models, the returns obtained when the features of daily return, turnover rate, relative strength index, volume, and forward adjusted closing price are used as inputs are all the highest, and the return obtained by CLGNN is even higher than that of the other models (e.g., TCN, Transformer, etc.). Full article

ESG has emerged as a key non-financial indicator, drawing significant investor focus. Disparities in ESG ratings may skew investor perceptions, potentially endangering stock values and financial market stability. This paper examines the link between ESG rating divergences and stock price crash risk, drawing on data from six Chinese and global ESG rating agencies. Focusing on Shanghai and Shenzhen A-share listed firms, it analyzes information from 2015 to 2022 within the theoretical contexts of information asymmetry and external monitoring. This study finds that ESG rating divergence markedly elevates stock price crash risk, a relationship that persists through a series of robustness checks. Specifically, the mechanisms operate through two key pathways: increased reputational damage risk due to information asymmetry and reduced external monitoring due to weakened external governance. The results of the heterogeneity analysis indicate that ESG rating divergence exacerbates stock price crash risk more significantly for non-state-owned firms, firms with low levels of marketization, and firms in high-pollution industries. This study provides clear actionable strategic paths and policy intervention points for investors to avoid risks, firms to optimize management, and regulators to formulate policies. Full article

Mediterranean ecosystems have been grazed by livestock for thousands of years. While considered both a major anthropogenic stressor and a potential habitat conservation tool, the effects of livestock grazing on vertebrate populations remain poorly understood. Our study focused on goat and sheep grazing on a large island off the coast of Greece in order to shed light on (1) the nature of the relationship between livestock grazing and vertebrate assemblages, and (2) the mediating mechanisms. Sampling small mammal, reptile, and passerine bird species across a range of livestock grazing intensities in a Mediterranean pastoral system, we used Generalized Linear Modeling to test for the presence of a unimodal relationship between grazing disturbance and vertebrate diversity in line with the Intermediate Disturbance Hypothesis (IDH). An information-theoretic approach helped elucidate which habitat characteristics best predicted vertebrate-grazing responses. Terrestrial species abundance decreased steadily with increasing grazing, while species richness exhibited a unimodal response, peaking at intermediate livestock stocking rates and offering support for the IDH. This response was best predicted by invertebrate food availability. Both passerine bird species’ richness and abundance showed no clear relationship with grazing yet were significantly correlated with changes in vegetation structure. Our findings suggest that there is no ideal grazing level for broadly optimizing both vertebrate abundance and richness. However, only light-to-intermediate livestock stocking rates are associated with healthy wildlife populations while also promoting terrestrial species richness. Agricultural policy that avoids overgrazing while encouraging a mosaic of different grazing intensities at the regional level is needed to best support diverse vertebrate assemblages. Full article

This study examines the relationship between energy use, economic growth, and stock market performance in the COMESA region. It utilizes yearly data from 1990 to 2022, sourced from the World Bank. It applies the Method of Moments Quantile Regression (MMQR), a statistical technique that captures how relationships vary across different levels of stock market development. The analysis examines how fossil fuels, renewable energy, and energy imports impact stock market size (market capitalization) at varying levels of performance. The results indicate that both the use of fossil fuels and renewable energy have a significant impact on stock markets, although the effects vary. Renewable energy has the most important positive effect in countries with smaller or weaker markets, suggesting it can help strengthen financial systems in developing economies. However, its impact becomes weaker in stronger markets, possibly due to the costs and challenges of switching to clean energy. On the other hand, economic growth does not always lead to stock market growth, likely due to structural problems in the region that prevent economic progress from boosting financial markets. This study shows how energy policy, economic growth, and market performance are closely linked. It calls for targeted policies to support the shift to renewable energy, manage short-term challenges, and build strong infrastructure to support long-term growth and financial stability. This research helps explain how energy and economic factors shape stock market outcomes in COMESA, offering helpful guidance for investors, researchers, and policymakers aiming for sustainable development. Full article

This paper introduces a new unit-Burr distortion function constructed via a transformation of the Burr random variable. The distortion can be applied to existing base copulas to create new copula families. The relationships of tail dependence coefficients and tail orders between the base bivariate copula and the unit-Burr distorted copula are derived. The unit-Burr distortion-induced family of copulas includes well-known copula classes, such as the BB1, BB2, and BB4 copulas, as special cases. The unit-Burr distortion of existing bivariate copulas may result in a family of copulas with both lower and upper tail coefficients ranging from 0 to 1. An empirical application to the rates of return for Microsoft and Google stocks is presented. Full article

Against the background of carbon emission reduction, this paper explores the driving factors of carbon footprint knowledge system toughness for building construction enterprises through the theory of constraints (TOC) and optimises the carbon footprint knowledge system toughness under static and dynamic perspectives, respectively. Under the static perspective, the fuzzy set qualitative comparative analysis method (fsQCA) is used to explore the development path of the carbon footprint knowledge system toughness for building construction enterprises, and the study finds three kinds of grouping paths. Under the dynamic perspective, system dynamics is used to analyse the causality of the driving factors of the carbon footprint knowledge system toughness and draw the causality diagram. The stock flow diagram is drawn according to the relationship between the factors, and G1 method is combined with the expert distribution to determine the weight of each factor, and then, the model equation is established to complete the construction of the system dynamics of the carbon footprint knowledge system toughness based on the control variable method of the four capabilities under the influence of the factors to simulate the comparison and to explore the extent of the influence of different factors on the carbon footprint knowledge system toughness. Through the two-dimensional analysis framework, we provide an integrated solution for path selection and dynamic regulation for building construction enterprises to help them achieve the adaptive optimisation of the carbon footprint knowledge system and promote the low-carbon transformation and sustainable development of the construction industry. Qualitative results show that three configuration paths affect resilience, with core factors including management, emission, predictive, and construction capabilities. Quantitative results indicate fsQCA overall consistency (0.861) and coverage (0.808); system dynamics simulation shows that management capability has the highest impact weight (0.355). Full article

Meadow grasslands are characterized by high primary productivity and are an important ecological barrier against sandstorms and desertification in northern China. The dynamic changes in reserves of soil organic carbon stocks (SOCSs), total nitrogen (TNS), and total phosphorus (TPS) in grassland ecosystems are easily disturbed by human activities. However, the effects of different treatments on the relationships among soil nutrient reserves (SOCS, TNS, and TPS) and the mechanisms underlying the effects of various key variables on changes in soil nutrient reserves remain unclear. This study investigated the changes in soil nutrient reserves in meadow grasslands in northern China after mowing (M), burning (F), and grazing (G) treatments than without any anthropogenic interference (E, control) from 2020 to 2023, as well as the vegetation and soil variables that may affect them. The results showed that compared with the control treatment, once-a-year mowing and burning significantly increased SOCS (M: 12.75%, F: 23.72%), TNS (M: 15.6%, F: 26.8%), TPS (12.4%, 27.2%) and strengthened the correlations between SOCS and TNS and between SOCS and TPS, while grazing treatments significantly reduced soil nutrient reserves (13.0%, 11.8%, 10.1%) and the correlation between soil nutrient reserves. In general, under different treatments, soil temperature was the important control variable affecting each reserve. Vegetation was also a key control variable affecting SOCS, while TNS and TPS were mainly regulated by soil factors. It should be pointed out that owing to different treatments, the key vegetation variables affecting SOCS differed notably from those affecting TNS and TPS. This study emphasized the impact of different treatments on soil nutrient reserves and their main controlling variables, providing an important theoretical basis for further optimizing and improving the scientific management strategy of grassland ecosystems. Full article

Bigeye grunt (Brachydeuterus auritus) is a dominant fish species and mostly a major target species in both artisanal and industrial fisheries in the coastal waters of Sierra Leone. It was listed as near threatened in 2015 by the International Union for Conservation of Nature (IUCN) Red List. Although this species has been repeatedly assessed as overexploited by the Fishery Committee for the Eastern Central Atlantic (CECAF) in the majority of its range in the Eastern Central Atlantic, there have never been studies of stock assessment in the coastal waters of Sierra Leone. We conducted a study on the population dynamics of bigeye grunt in the coastal waters of Sierra Leone, which is crucial for completing the resource status of this species in the Eastern Central Atlantic. The results showed that the bigeye grunt had a wide distribution in the coastal waters of Sierra Leone, with significant spatiotemporal variation characteristics in biomass and abundance. The growth parameters of bigeye grunt varied across different months, but all E values were below 0.5, indicating that no overfishing occurred. These findings were further corroborated by the results of the Length-Based Bayesian Biomass Estimation method (LBB). The results of the Generalized Additive Model (GAM) show that there is a certain nonlinear relationship between the resource abundance of the bigeye grunt and both environmental factors and geographical locations, among which the influence of latitude is the greatest. This study posits that the bigeye grunt in Sierra Leone’s coastal waters exhibits moderate exploitation potential. The findings are anticipated to provide a scientific framework for informing evidence-based management strategies for this fishery resource. Full article

Generic and consistent formulations for measurement of the backscattering cross section $\left({\sigma }_{bs}\right)$ and the volume backscattering coefficient $\left(s_v\right)$ using broadband pulse compression and narrowband echo integration are derived, for small- and finite-amplitude sound propagation. The theory applies to backscattering operation of echosounders and sonars in general, with focus on fisheries acoustics. Formally consistent mathematical relationships for broadband and narrowband operation of such instruments are established that ensure consistency with the underlying power budget equations on average-power form, bridging a gap in prior literature. The formulations give full flexibility in choice of transmit signals and reference signals for pulse compression. Generic and general criteria for quantitative consistency between broadband and narrowband operation are derived, establishing new knowledge and analysis tools. These criteria become identical for small- and finite-amplitude sound propagation. In addition to general criteria, two special cases are considered, relevant for actual operation scenarios. The criteria serve to test and evaluate the extent to which the methods used in broadband pulse compression and narrowband echo integration operating modes are correct and consistent, and to identify and reduce experienced discrepancies between such methods. These are topics of major concern for quantitative acoustic stock assessment, underlying national and international fisheries quota regulations. Full article

Background: The relationship between genetic diversity and fitness is well understood, but few studies have investigated how behavior influences genetic diversity, or vice versa. We investigated the relationship between feeding behavior (on a pelleted diet) and genetic diversity in pikeperch, a piscivorous species. Methods: A total of 135 juvenile pikeperch from the same stock were grouped into three behavioral groups: pellet consuming, pellet refusing, and cannibalistic. Eighteen microsatellite markers were used to characterize the genetic diversity and structure of individuals. Results: The juveniles were classified into two genetic clusters: one dominated by pellet-consuming individuals and the other by pellet-refusing individuals containing equal proportions of cannibal individuals. Three of the microsatellite markers were under selection, but only one showed significant genetic segregation between the groups. For this marker, the pellet consumption was associated with low fragment length. Individual multilocus heterozygosity was significantly higher in the pellet-refusing group. Conclusions: These results suggest that pellet consumption acts as an uncontrolled selective force during domestication, influencing the genetic variability of domesticated populations. The ability to habituate to pellets has a significant genetic basis. Cannibalism does not affect genetic variability, and the emergence of the trait is independent of the propensity to consume pellets. Full article

This study applies Gradient Boosting Machines (GBMs) and principal component regression (PCR) to forecast the closing price of the Johannesburg Stock Exchange (JSE) All-Share Index (ALSI), using daily data from 2009 to 2024, sourced from the Wall Street Journal. The models are evaluated under three training–testing split ratios to assess short-term forecasting performance. Forecast accuracy is assessed using standard error metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE). Across all test splits, the GBM consistently achieves lower forecast errors than PCR, demonstrating superior predictive accuracy. To validate the significance of this performance difference, the Diebold–Mariano (DM) test is applied, confirming that the forecast errors from the GBM are statistically significantly lower than those of PCR at conventional significance levels. These findings highlight the GBM’s strength in capturing nonlinear relationships and complex interactions in financial time series, particularly when using features such as the USD/ZAR exchange rate, oil, platinum, and gold prices, the S&P 500 index, and calendar-based variables like month and day. Future research should consider integrating additional macroeconomic indicators and exploring alternative or hybrid forecasting models to improve robustness and generalisability across different market conditions. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals. Full article