Search Results (1,375)

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

This study examines stock market reactions to share repurchase announcements by firms listed on the Korean Stock Exchange from 2015 to 2024. Unlike the U.S. market, where share repurchases are generally viewed as a shareholder-friendly signal of strong firm performance, Korea’s institutional environment permits relatively discretionary treasury stock transactions, potentially leading to heterogeneous investor responses. Using an event-study methodology, we analyze short-term abnormal returns around repurchase announcements, differences across stated repurchase motives, and the moderating role of firm valuation. We document significantly positive short-term abnormal returns following repurchase announcements, consistent with signaling-based explanations. However, these positive market reactions are driven exclusively by repurchases explicitly intended to enhance shareholder value. Furthermore, the market response to shareholder-value-oriented repurchases is significantly stronger among firms with lower valuation levels, suggesting that undervaluation enhances the credibility of repurchase signals. Overall, our findings indicate that repurchase announcements are not interpreted uniformly in the Korean market. Instead, investors condition their reactions on both managerial intent and firm-specific valuation contexts. By jointly considering repurchase motives and valuation effects, this study contributes to the literature by showing that the informational content of repurchase announcements is contingent rather than universal, and that signaling effects materialize primarily when managerial actions align with credible undervaluation signals. Full article

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty shock is proxied by the U.S. presidential election of 5 November 2024, while the action shock is proxied by President Trump’s 2 April 2025 announcement of reciprocal tariffs. Using daily log returns for the S&P 500 and leading indices for Canada, France, Germany, Italy, Japan and the United Kingdom, we cover January 2010 to July 2025 and assess event effects using correlation paths for June 2024–June 2025 and symmetric ±30-day windows. We employ a DCC-GARCH model to jointly estimate conditional variances and dynamic correlations for six USA-G7 pairs. The results indicate persistent correlation dynamics, with Canada/USA the highest and Japan/USA the lowest. Election-related uncertainty is associated with declines in correlation for European pairs, suggesting temporary decoupling, while Canada and Japan show only small changes. By contrast, the tariff action shock significantly increases conditional correlations across all country/USA pairs, implying stronger market synchronisation, with the largest increases in North America and parts of Europe, and the smallest adjustment in Japan. Full article

Net energy—defined as the energy remaining after accounting for the energy required for resource extraction and processing—shapes the fundamental physical constraints of energy systems. Although the extended Energy Return on Investment (EROIext) incorporates extraction, refining, transportation, and end-use infrastructure, its long-term structural dynamics remain underexplored. This study applies a Single-Cycle Lotka–Volterra (SCLV) model to examine interactions between resource stock, capital accumulation, and EROIext in the global petroleum system. The model is calibrated using historical data from 1965 to 2012 to explore structural trajectories under simplified assumptions. Results indicate that production peaks endogenously around 2041 within the model framework, while EROIext declines and falls below unity by 2081 under the assumed structural relationships. These years represent model-derived structural outcomes rather than deterministic forecasts. Capital stock reaches its maximum at the same energetic threshold (EROIext = 1), marking an internally generated transition in the resource–capital system. An entropy-based indicator is introduced as a thermodynamic proxy mirroring the decline in energetic efficiency within the modeled subsystem. These findings show how energetic reinvestment constraints generate endogenous peak and threshold behavior in resource-dependent systems. The analysis offers a structural perspective on interactions between depletion, capital accumulation, and net energy under simplified thermodynamic assumptions. These results provide insights into long-term structural constraints of the oil system, which may inform energy planning and policy discussions under conditions of declining net energy availability. Full article

We investigate a stochastic coupled nonlinear Schrödinger (Manakov-type) system for option price and volatility wave fields within the Ivancevic adaptive-wave option-pricing paradigm, and derive exact wave families together with statistical diagnostics of the resulting dynamics. This system combines behavioral market effects with classical efficient-market dynamics and incorporates a controlled stochastic volatility component. Randomness in both the option price and volatility is incorporated via white noise, and a system of stochastic partial differential equations (PDEs) is developed that governs the joint evolution of option prices and stock price volatility. We derive advanced solutions of the proposed system using a newly created methodology. The obtained solutions are expressions of cnoidal, snoidal, dnoidal, hyperbolic, trigonometric, and exponential functions. The stochastic dynamical investigation, together with the statistical measures are presented. The autocorrelation function (ACF) of squared returns for the obtained analytical solutions is demonstrated to show distinct differences in second-order temporal dependence, while asymmetries in the temporal evolution of the fluctuations are depicted via leverage correlation (LC). The probability distribution function (PDF) dynamics of the soliton solutions illustrate prominent temporal variability and non-stationary statistical dynamics. Differences in dynamical coupling between the two components of the considered system are presented via phase velocity cross-correlation analysis and are supported by phase difference dynamics visualizations. The strength and structure of coupling between components are displayed via the amplitude cross-correlation function. Mean amplitude dynamics and variance as a function of noise intensity $\sigma$, provide a systematic influence of stochastic forcing on their energy and a quantitative measure of stochastic dispersion of soliton solutions. All the results are displayed in 3D and 2D graphs of the stochastics and statistical dynamics of the obtained solutions. Full article

This study investigates the volatility nexus between exchange rates, interest rates, and stock market returns in South Africa, an emerging economy characterised by deep financial integration and exposure to global capital flows. Using monthly data from January 2003 to February 2025, the analysis employs a multi-layered econometric framework combining asymmetric GARCH models (EGARCH and GJR-GARCH), an Asymmetric Dynamic Conditional Correlation (ADCC-GARCH) specification, and a GARCH-MIDAS–DCC approach that decomposes volatility into long-run and short-run components while modelling time-varying cross-market dependence. The findings indicate that exchange rate volatility is the dominant and most persistent driver of financial market risk, highlighting the central role of the South African rand in transmitting global shocks to domestic markets. Equity market volatility is largely shock driven and mean reverting, with sharp increases during major crisis episodes such as the Global Financial Crisis and the COVID-19 pandemic. Dynamic correlations across markets are persistent but predominantly negative between stock returns and exchange rates, while linkages involving interest rates are weaker and more episodic. Overall, the results suggest that South Africa’s financial volatility nexus operates primarily through exchange rate-driven transmission rather than short-run contagion effects. Full article

This study examined the influence of Environmental, Social, and Governance factors on the financial performance of companies listed on the Saudi Stock Exchange (Tadawul). Employing a panel data approach, the analysis covers 450 firm observations collected annually during the period 2018–2023. Financial performance is measured using Return on Assets (ROA) and Return on Equity (ROE), while ESG disclosure scores are disaggregated into their three constituent pillars. Firm size, revenue per share, and leverage are incorporated as control variables. The fixed effects regression results reveal that social factors demonstrate statistically significant positive relationships with both ROA and ROE, supporting the stakeholder theory-based perspective that strong social practices enhance operational efficiency and investor confidence. Conversely, environmental and governance factors exhibit no significant association with either financial performance metric within the study period. Leverage shows a significant negative relationship with ROA but not with ROE, while revenue per share consistently demonstrates strong positive associations with both performance measures. These findings contribute to the limited literature on ESG–performance linkages in Gulf Cooperation Council markets and offer important implications for corporate managers, investors, and policymakers seeking to advance sustainability objectives within the framework of Saudi Vision 2030. Full article

This paper examines how assets from emerging and developed stock markets can be efficiently allocated during periods of financial crisis by integrating traditional portfolio theory with Extreme Value Theory (EVT), using the Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) approaches to model tail risks. This study evaluates mean-variance portfolios constructed under each EVT framework and finds that portfolios based on GPD estimates consistently favour emerging market assets, which outperform both developed market and internationally diversified portfolios during extreme market conditions. In contrast, GEV-based portfolios indicate superior performance for developed market assets, highlighting the distinct behaviour of returns in the upper and lower tails of the distribution. These contrasting results reveal the unique nature of safe-haven characteristics associated with developed economies, the assets of which demonstrate greater stability and resilience during episodes of financial stress. By showing how tail-risk modelling alters optimal portfolio weights across market types, this paper contributes new evidence to the literature on crisis-informed asset allocation and offers practical insights for investors seeking robust diversification strategies under extreme market fluctuations. Full article