Search Results (1,382)

The present study provides a high-frequency empirical assessment of the financial performance, volatility, and market integration of thematic sustainability-oriented equity funds, focusing on clean energy and environmental innovation indices. Specifically, the study compares the financial performance of representative thematic green equity funds, such as ICLN and QCLN, and an emerging-market benchmark (ECON) with conventional developed-market indices (SPY, QQQ, GSPC, and XLE) using daily stock prices from 2010 to 2025. The analysis employs a transparent and replicable framework based on daily logarithmic and cumulative returns and incorporates the compound annual growth rate (CAGR), Sharpe and Sortino ratios, beta estimation, correlation analysis, and maximum drawdown. The research frequency is appropriate for a thorough analysis of short-term market structures and performance. The results indicate that sustainability-oriented equity indices exhibit higher volatility, deeper drawdowns, and greater sensitivity to broad market movements than conventional benchmarks. Sustainability-focused equity indices that emphasize clean energy exhibit higher market sensitivity (betas above 1) and strong correlations with traditional equity indices. Correlation and beta estimates suggest a high degree of integration with traditional equity markets, implying limited diversification benefits within an equity-only framework. Periods of relative outperformance appear to be associated with favorable policy conditions and energy market dynamics, but are not consistently sustained over the sample period. In addition, the overall results suggest that sustainability investments generate substantial environmental and social externalities. Risk-adjusted performance measures suggest weaker historical performance over the sample period relative to conventional benchmarks. These findings should be interpreted as a comparative historical assessment rather than a structural risk model. From a policy perspective, the findings suggest that stable and credible regulatory frameworks, including long-term climate policy support and investment-enabling institutions, may be important for improving the financial resilience and long-term viability of green equity instruments. From a sustainability transition perspective, the observed volatility and market dependence of sustainability-oriented equity indices may constrain their effectiveness as standalone market-based financing mechanisms without complementary institutional and policy support. Full article

As a novel tool for predicting stock trends, hypergraphs are used to effectively represent high-order relationships among stocks, capturing symmetric dependencies inherent in market interactions. However, the instability of hyperedges limits their ability to capture dynamic stock changes, and existing methods neglect the influence of time decay on feature importance. To address these challenges, a hybrid hypergraph–dynamic graph attention network based on temporal decay attention and conditional aggregation for stock trend prediction, namely HDGAN, is developed. Specifically, we utilize dynamic graphs to capture the dynamic relationships among stocks, which mitigates the instability of the hyperedge structure in dynamic markets. A temporal decay attention mechanism is designed to identify important feature points in the evolution of stock prices, and then a conditional aggregation method is proposed to aggregate information from different pathways. Extensive experiments on A-share, NASDAQ, and NYSE datasets demonstrate HDGAN outperforms other state-of-the-art methods in stock trend prediction and investment return. Full article

Enhancing the resilience of real-economy firms is essential to sustainable development because firms must not only absorb shocks but also maintain long-term adaptive and renewal capacity. Against this background, this study examines whether insurance institutional ownership, as a form of patient capital, is systematically associated with corporate resilience. Using panel data for Chinese A-share listed firms from 2008 to 2024, we construct a multidimensional corporate resilience index based on risk resistance, adaptive recovery, and renewal and development and estimate two-way fixed-effects models. The results show that insurance ownership is positively associated with the baseline corporate resilience index, and this pattern remains qualitatively similar when we examine stock-return volatility, financial performance growth, and a stricter capability-oriented resilience index. The positive association is stronger for state-owned enterprises, small firms, non-manufacturing firms, and firms located in northern China. Channel analysis suggests that insurance ownership is associated with lower agency costs, stronger internal controls, greater external scrutiny, and lower financing constraints, patterns that are consistent with the proposed channels linking insurance ownership to corporate resilience. Further analyses show that higher insurance ownership and increases in insurance holdings are associated with stronger resilience, whereas decreases in holdings are associated with weaker resilience. Long holding duration is negatively associated with resilience, suggesting that performance-evaluation pressure may weaken the long-term governance role of insurance capital. Overall, the findings suggest that insurance investors may support corporate resilience and, when governance incentives and evaluation mechanisms are appropriately aligned, contribute to the sustainable development of the real economy. Full article

This paper examines how the predictive content of insider trading varies across industries. Using U.S. insider transaction data from 2005 to 2025 and firm-month level measures of insider trading and forward returns, we compare technology, banking, and utility firms within a unified framework. The results show that insider purchases in banking firms contain the strongest information about future returns, while the signal is substantially weaker in technology firms and moderate in utilities. We also document a clear asymmetry between buying and selling. Insider purchases are more informative than sales, while sales reflect more heterogeneous motives and are therefore harder to interpret. This buy–sell gap varies across industries and is most pronounced in banking and utilities. Finally, we compare insider-trading informativeness before and after the 2022 amendments to Rule 10b5-1. The results show that sell-side informativeness appears weaker in the post-2023 period, while the predictive content of purchases remains largely unchanged. This evidence is descriptive and does not imply a causal effect of the reform. Overall, the findings highlight the importance of industry-specific information environments and regulatory conditions in shaping the relation between insider trading and future stock returns. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

Sustainable forest management is increasingly recognized as an important climate change mitigation strategy because forests capture and store large amounts of carbon. This study presents a regional framework that integrates LiDAR data, forest cartography, and Life Cycle Assessment (LCA) to quantify biomass-related carbon dynamics and greenhouse gas emissions associated with forest management operations. The methodology was applied to the Autonomous Community of La Rioja (Spain) for the period 2010–2016 using public LiDAR campaigns, the Forest Map of Spain, and inventory data for reforestation and logging operations. Results show that above-ground biomass increased from 4,537,956 t in 2010 to 7,092,890 t in 2016, which corresponds to an increase of 1,200,819 t C in above-ground carbon stock. A complementary first-order estimate based on IPCC default root/shoot ratios suggests that total living biomass carbon (above- plus below-ground) increased by approximately 1,495,269 t C during the same period. In parallel, LCA results indicate that logging has substantially higher operational impacts than reforestation, particularly in terms of global warming potential. Even under a conservative scenario in which part of the carbon removed through logging is returned to the atmosphere, the regional balance remains net negative in CO₂-equivalent terms, indicating a net sink over the analyzed period. However, the approach has important limitations, including the absence of independent field validation, stand-age stratification, and explicit soil-carbon accounting. Full article

This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A day was labeled a risk event (1) if its return fell below the 5th percentile of the returns observed over the preceding 100 trading days, indicating a sharp decline. Nine classification models—Logistic Regression, k-nearest Neighbor, Decision Tree, Random Forest, Linear Discriminant Analysis, Naive Bayes, Quadratic Discriminant Analysis, AdaBoost, and Gradient Boosting—were trained and validated. Among these, Logistic Regression demonstrated the strongest overall performance across multiple evaluation metrics, including accuracy, non-risk F1 score, risk F1 score, and AUC. Full article

Multi-source environmental data are increasingly used for measurement, reporting and verification, and for coordinating low-carbon innovation across interorganizational networks. However, voluntary data sharing remains limited because participants face asymmetric costs, leakage and compliance risks, and uncertainty in value capture. This study develops a network evolutionary game model to examine how cooperative data sharing emerges and stabilizes in green innovation networks. We specify a two-strategy game in which heterogeneous agents choose between sharing and withholding. The payoff structure integrates private innovation gains from their own data, cross-partner synergy, external incentives, fixed governance costs, and stock-scaled sharing and risk burdens. Agents interact on a Barabási–Albert scale-free network and update strategies via local imitation under a Fermi rule. Simulations show that cooperation can diffuse from low initial participation and converge to a high-sharing regime when benefit allocation and incentive intensity jointly offset cost and risk frictions. Several governance levers exhibit threshold-type effects, including the allocation share, the opportunity loss of non-sharing, and the marginal cost–risk burden. Multi-source synergy and subsidies further raise the attainable cooperation level, but with diminishing marginal returns. Degree heterogeneity accelerates diffusion once hub organizations adopt sharing, while also raising fairness concerns when benefits concentrate on central nodes. Overall, the findings provide green-innovation-specific governance conditions that translate threshold regions into implementable design targets for sustainable environmental data sharing. Full article

This study analyzes the relationship between stock market returns and investment flows in investment funds in Spain. Through a quantitative analysis covering the period from December 2001 to June 2025, it examines not only the existence of a correlation but also its temporal structure, functional form, and heterogeneity across different geographical areas (U.S., Europe, Japan, and Spain). Using monthly data on net flows from INVERCO and market indices, the study employs Ordinary Least Squares (OLS) regression models, segmented regressions, and fixed-effects panel models to obtain robust estimates. The results confirm a positive and statistically significant relationship between past returns and subsequent investment flows, with a temporal lag ranging from one to three months. This delay varies notably by geographical region, suggesting the existence of different investor profiles and information channels. The study also finds evidence of a convex relationship, indicating that investors react asymmetrically, aggressively pursuing high returns more than penalizing low ones. These findings, interpreted through the lens of behavioral finance, point to pro-cyclical and reactive behavior of Spanish investors, driven by biases such as loss aversion, trend-following, and delays in information processing. The study contributes to the academic literature by providing updated and methodologically robust evidence on Spain, a market that has traditionally been underexplored, and offers practical implications for investors, fund managers, and regulators in terms of financial education and risk management. Full article

Environmental and financial datasets often display complex distributional characteristics, including heavy tails, high skewness and the presence of extreme observations. Traditional probability models such as the exponential, gamma or log-normal distributions may not adequately capture these behaviours particularly when modelling extreme events such as rainfall, pollution levels, stock returns or loss severities. By integrating the characteristics of Pareto and exponential distributions into an exponentiated framework that can describe datasets arising from environmental and finance fields, this study presents a novel three-parameter exponentiated Pareto exponential distributions using the exponentiated Pareto family of distributions with classical exponential distribution as the baseline model. This novel model extends the classical exponential distribution with the addition of extra shape parameters which simultaneously regulate the centre and tail behaviours of the new model. The statistical and mathematical characteristics of the proposed distribution are determined and studied. The maximum likelihood estimate approach is used in a conducted simulation exercise, and the estimator’s efficiency is evaluated as seen from the results. The practical applicability of the model is illustrated with four real-life datasets utilising model adequacy and goodness-of-fit measurements such as log–likelihood, Akaike information criteria and Bayesian information criteria. The data reveal that the proposed model gives a better fit than the models chosen as comparators, making the EPE distribution useful and robust in environmental and financial fields of study. Full article

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, theme-based models generate economically meaningful and regime-dependent predictive gains. Short-Term Reversal and seasonality exhibit stronger expansion-period predictability, while size and profitability perform better during recessions. Regularized linear models provide the most stable performance in limited-data environments, whereas nonlinear ensemble methods improve only when training windows are extended. The findings underscore the importance of economically structured dimensionality reduction and adaptive factor allocation in managing concentration risk among systemically important mega-cap firms. Full article

We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass Estimation Method (3PEM) serves as both a robust estimator and a diagnostic tool for factor purification. By assuming the Fama–French five-factor model as the baseline model, we first show that the 3PEM yields risk premium estimates for the European market that are more economically plausible and statistically robust than those obtained using the traditional two-pass estimation method (2PEM). Moreover, our results show that the 3PEM is able to detect noise in tradable factors. Furthermore, the 3PEM is used to denoise the observed factors, providing purified versions that better capture the systematic components of risk. We also identify both noisy factors and denoised factor series that improve the estimation of stock-level exposures and expected returns. Full article

This study develops and evaluates an equity selection pipeline that converts quarterly fundamentals into a monthly frequency, constructs profitability, leverage, liquidity, and growth characteristics, and learns a linear ranking model via a genetic algorithm (GA). The GA is trained to maximize either (i) mean monthly NDCG@30 using 12-tile relevance labels or (ii) mean monthly Spearman information coefficient (IC). The learned ranker is tested out-of-sample using monthly forward returns, benchmarked against the S&P 500, with different types of allocation weights, and further evaluated under sector concentration limits. In the last layer, the monthly-selected stock universe is used in a daily dynamic allocation which is solved by the penalized Max-Sharpe or Min-Variance optimization problems under only long positions and transaction fees. Performance is examined across Pre-COVID, COVID, Post-COVID (Train), and Final Test regimes, demonstrating how ranking objectives and diversification constraints impact performance and stability. Results show that TTM-based accounting signals, when optimized through genetic learning and disciplined allocation, yield economically meaningful stock selection and robust portfolio performance across market regimes. Full article

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity measure and integrates domain-specific features including technical indicators, volume–price relationships, and Qualified Foreign Institutional Investor (QFII) activity. A custom 60-day input window with a 20-day forecast horizon is applied to capture medium-term market dynamics. The framework was evaluated through extensive backtesting and real-world validation with the TAIEX Futures. The results demonstrate that the model achieves a peak directional accuracy of 85.1% under optimal parameter settings. Moreover, trading simulations confirm its practical viability, yielding a cumulative return on investment (ROI) of approximately 1600% during the short-term evaluation period (2023–2025) and nearly 2000% in the long-term evaluation (2019–2025), even after accounting for transaction costs and stop-loss mechanisms. These findings indicate that combining historical pattern similarity with institutional investor behavior substantially improves predictive power and profitability. Nevertheless, the framework remains constrained by its reliance on Taiwan-specific institutional features and historical trading data, limiting generalizability. Future research should extend applications to other markets while incorporating macroeconomic variables, corporate fundamentals, and news-driven signals to enhance adaptability. Full article

This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile Regression (QQR) framework, we analyze the risk transmission mechanisms across the conditional distribution of stock returns. The empirical results reveal a notable regime-dependent reversal: a negative influence is exerted by geopolitical risk during a bullish market regime, while a counterintuitive positive association is present for the bearish market conditions. This effect is more pronounced for emerging and commodity-rich markets, which may provide a potential hedge during supply-side shocks. Moreover, the QQR analysis focused on the United States of America stock market provides an examination of the different potential transmission mechanisms of geopolitical variants. The results suggest that geopolitical threats (GPRT) represent a persistent factor that negatively affects the market for normal and bullish market regimes, while geopolitical acts (GPRA) represent a tail-risk catalyst that exacerbates losses during severe market crashes. The results remain robust to an alternative specification of returns and indicate the necessity of distinguishing between geopolitical acts and threats from a risk management standpoint, as well as correctly identifying the market regime. Full article