Search Results (1,383)

Raw sentiment extracted from Chinese financial news is noisy and difficult to use directly for market prediction. This study proposes a mathematical filtering framework that converts noisy Chinese financial news sentiment into reliable quantitative signals for financial market prediction. Three daily sentiment measures were constructed from Chinese financial news: sentiment mean, sentiment dispersion, and polarity imbalance. Seven filtering methods were applied to each measure, including exponential smoothing, autoregressive filtering, ARIMA filtering, moving average smoothing, discrete wavelet transform, Savitzky–Golay filtering, and Kalman filtering. The seven filtered outputs were averaged to produce an ensemble-smoothed sentiment signal. Support vector machines and neural networks were then used to compare the predictive performance of raw and filtered signals for stock index log returns and realized volatility. Filtering reduced the standard deviation of sentiment mean by 48%, sentiment dispersion by 55%, and polarity imbalance by 50%, while mean levels remained stable. Filtered sentiment consistently outperformed raw sentiment across all model configurations. The improvement was larger for realized volatility than for returns: the best support vector machine reduced volatility prediction error by 16.9% and return prediction error by 5.8%. A moderate neural network with 20 hidden neurons achieved optimal performance for both outcomes. Mathematical filtering extracts stable and informative sentiment signals from Chinese financial news. Filtered sentiment is more useful than raw sentiment for predicting market volatility, and the improvement holds across multiple machine learning models. Full article

Small pelagic fishes are essential for artisanal fisheries and food security in São Tomé and Príncipe, yet biological information required for stock assessment remains scarce. This study examined the population dynamics and life-history traits of Caranx crysos, Euthynnus alletteratus, Hemiramphus balao, and Cheilopogon melanurus using 9052 specimens collected from artisanal landings between 2023 and 2025. C. melanurus (35.2%) and H. balao (34.0%) dominated the sampled catches, followed by C. crysos (18.1%) and E. alletteratus (12.7%). Standardized CPUE series modelled using GAMs revealed significant temporal and seasonal variation in relative abundance, with contrasting species-specific trends. Length–weight relationships revealed negative allometric growth in three of the four species examined (75%), with b values ranging from 2.50 to 3.19, while Fulton’s condition factor differed significantly among species (Kruskal–Wallis χ² = 6702.7, p < 0.001). Sex-ratio analyses showed significant deviations from parity in C. crysos and C. melanurus, whereas E. alletteratus and H. balao remained balanced. Gonadosomatic index and maturity-stage distributions indicated year-round reproductive activity with distinct spawning peaks. Length at first maturity (L50) ranged from 30.2 cm to 38.8 cm among species. Growth parameters estimated from length-frequency data using the von Bertalanffy Growth Function fitted through ELEFAN_GA in TropFishR yielded L∞ values of 43.9–68.4 cm and K values of 0.065–0.336 yr⁻¹. Growth performance index (φ′) ranged from 2.48 to 2.99, corresponding to theoretical longevities of 8.9–46.3 years. Length-based cohort analysis indicated biomass concentration in intermediate size classes and increasing fishing mortality towards larger individuals. Exploitation rates revealed contrasting fishing pressures among species, while sensitivity analyses identified growth and mortality parameters as the main sources of uncertainty. These findings provide the first integrated biological baseline for the assessment and management of small pelagic resources in São Tomé and Príncipe. Full article

This paper examines global equity market integration, commodity-price exposure, and volatility spillovers in Ghana’s frontier equity market. Using daily data from January 2011 to December 2025, we estimate a multi-factor asset pricing model nested within a GARCH framework for the Ghana Stock Exchange Composite Index (GSECI) and the Financial Sector Index (GSEFSI). The model jointly estimates first-moment return exposures and second-moment volatility spillovers from a global equity market and three key global commodity markets: gold, crude oil, and cocoa, while controlling for asymmetric volatility, return serial dependence, and domestic macro-financial shifts associated with banking sector recapitalization and the Domestic Debt Exchange Programme (DDEP). The Ghanaian equity market is exposed to the global equity market, indicating measurable but economically modest global integration, with stronger exposure in the financial sector. Commodity-price exposures are selective, with gold and crude oil exposures concentrated in the financial sector, whereas the cocoa factor is negatively associated with returns on both indices. The variance results show persistent volatility, inverse asymmetric volatility responses, and differentiated volatility spillovers from global equity and commodity markets. The DDEP period is associated with significant equity market repricing, particularly in the financial sector. These findings indicate that Ghana’s equity market dynamics are shaped jointly by global equity and commodity market information, frontier market frictions, and sovereign–bank conditions. Full article

This paper studies the informational hierarchy of individual stocks in the Colombian Stock Exchange (BVC), with particular attention to the role of cross-listed securities. The paper addresses a gap in the literature on small emerging markets, where evidence on intra-market information and return transmission remains scarce, particularly in the presence of illiquidity, cross-listing, and external risk exposure. Using daily data for 2015–2024, we estimate a five-asset vector autoregression VAR (3) with exogenous global controls and compute generalized forecast error variance decompositions within the Diebold–Yilmaz connectedness framework, with residual-bootstrap inference and CBOE Volatility Index (VIX)-based regime analysis. The VIX regimes are used to distinguish low-, medium-, and high-global-risk environments because global risk appetite is a key channel through which external shocks affect emerging equity markets. Three results stand out. First, total connectedness is moderate in the full sample, at 25.2%, but rises sharply with global risk, from 17.5% in low-VIX periods to 28.4% in high-VIX periods. Second, Ecopetrol’s American Depositary Receipt listed on the New York Stock Exchange (EC, NYSE) emerges as the dominant net transmitter of return innovations, and its informational leadership becomes stronger as global uncertainty increases. Third, when the local Ecopetrol share is excluded, leadership shifts to Bancolombia’s ADR (CIB), suggesting that directional spillover leadership is associated not only with firm identity but also with the offshore trading venue. These findings document a regime-dependent and venue-driven informational hierarchy, consistent with ADR-listed securities acting as dominant transmitters of return innovations to the domestic Colombian equity system. For portfolio managers, the results imply that diversification across local Colombian equities may overstate the number of independent information sources, especially during high-risk periods, and that monitoring ADRs, global volatility, oil prices, and exchange-rate conditions may improve hedging and risk management. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article

Non-ductile reinforced concrete frames with unconfined joints dominate the collapse hazard of the existing building stock. Their CFRP-retrofit margin at collapse demand is poorly quantified. Two one-third-scale portal sub-frames were tested under Froude similitude. Specimen 1 was bare. Specimen 2 carried a three-ply hoop CFRP jacket on columns, beams, and joints. Both received the Antakya 3141 record from the 2023 Kahramanmaraş $M_w$ 7.7 mainshock at design intensity 0.35 g and collapse intensity 1.0 g. Cyclic response was decomposed into flexural, shear, and slip energy. At design intensity, the retrofit cut peak roof drift by 54%, suppressed residual offset, and lowered the calibrated Park–Ang index from 0.89 to 0.32. Slip share dropped from 47% to 5%. At collapse intensity, the retrofitted frame transitioned to joint-panel debonding-controlled failure at 8% drift with 245 mm residual, and shear share rose to 64%. The dominant-half-cycle ratio $R_1$ ≈ 0.72 emerged as a candidate brittle-damage signature for collapse-level response. A Lam–Teng confinement check confirms that the failure migrates from the column ends to debonding fracture in the wrapped panel rather than being eliminated by the retrofit. Supplementary joint-corner anchorage is recommended for non-ductile joints at collapse demand. Full article

This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return–volatility regimes, and the LSTM/GRU components learn nonlinear temporal patterns from regime-conditioned information. The framework is evaluated using the CSI 300, S&P 500, and Nikkei 225 indices through forecasting-accuracy measures, Bootstrap Diebold–Mariano tests with Modified Bayes Factor evidence, out-of-sample trading simulations, and robustness checks. The empirical results show that regime conditioning is the primary source of forecasting and economic improvement. KF–MS–LSTM performs best for the CSI 300 and Standard MS performs strongest for the S&P 500, while KF–MS–LSTM and KF–MS–GRU are more competitive for the Nikkei 225. In contrast, models without regime information, including pure LSTM/GRU and the standalone Transformer, generally exhibit weaker forecasting and trading performance. The findings suggest that latent market-state information is more important than neural-network complexity alone for robust financial forecasting, while the incremental value of Kalman filtering and recurrent learning remains market dependent. Overall, the results support regime-aware forecasting as an interpretable and economically meaningful approach for stock-index prediction under heterogeneous market environments. Full article

In the context of stock-based development, assessing the development potential of idle rural resources and formulating differentiated utilization pathways are important for improving resource-use efficiency and stimulating endogenous rural development in mountainous counties. However, existing studies mainly focus on single resource types and lack both an integrated framework for multiple idle rural resources and effective links between potential assessment and classified utilization. Taking Suichang County, Zhejiang Province, as a case study, this study constructs an evaluation index system for idle rural resource development potential. GIS-based spatial analysis and geographically weighted regression (GWR) reveal the spatial differentiation of development potential and its driving factors. On this basis, a three-dimensional framework of “potential–driving force–resistance” is used to classify resource utilization types and formulate differentiated pathways. The results show significant spatial heterogeneity in the development potential of idle rural resources in Suichang County, characterized by “central agglomeration, two-wing diffusion, and peripheral weakening” and a “three cores and two zones” pattern. The driving factors display significant spatial non-stationarity. Idle rural resources are classified into six utilization types with corresponding utilization strategies. This study provides a scientific basis and practical reference for classified revitalization, zoned policy implementation, and sustainable rural transformation in similar mountainous counties. Full article

Forecasting spare parts demand is challenging due to its intermittent, sparse, and highly irregular nature. Traditional inventory strategies, based on stable demand patterns, often lead to inefficiencies, including excess inventory and poor service performance. This study examines the impact of feature engineering combined with ABC–XYZ inventory segmentation on forecasting accuracy in a real-world industrial context. A biweekly forecasting framework was developed using six years (2019–2024) of transactional data from ERP and Field Service Management (FSM) systems of a forklift service company. Fifteen derived features capturing demand dynamics, intermittency, service behavior, and statistical structure were constructed and evaluated using Random Forest, XGBoost, and Support Vector Regression (SVR) models. The results show that restricting modeling to AY/BY inventory categories substantially improves predictive accuracy, reducing RMSE from >22 to <3 compared to full-SKU modeling. A reduced seven-feature set further lowers XGBoost’s RMSE to 2.51 (MAE = 2.14), achieving the best performance across all tested configurations on the 2024 hold-out period. The best-performing configuration achieves a Predicted-Demand Turnover Index (PDTI) of 44.13, compared with a baseline actual stock turnover of 2.78 (€65,944 actual demand/€23,721 historical average stock). PDTI is a theoretical scenario index; operationalizing it requires inventory-policy simulation under realistic constraints. These findings highlight that forecasting performance in intermittent-demand environments depends more on data representation and segmentation than on model selection alone. The study provides a reproducible, interpretable framework for integrating feature engineering and inventory segmentation into data-driven inventory management. Full article

The global financial system is constantly evolving through technological integration. This has led to the inception and rise in the cryptocurrency market, opening new avenues of comparative studies on market behavior. Therefore, the current study aimed to identify nuances in stock and cryptocurrency behavior. Based on the socionomic theory of finance, the study is a pioneer in considering the interplay of economic, market, and social media sentiments while providing a comparative view of cryptocurrencies and stocks. The study utilizes data of economic news sentiments, cryptocurrency fear and greed index, CNN fear and greed index, and Twitter sentiments against the movement of S&P Cryptocurrencies and S&P 500 stock index return spanning from 2018 to 2023. The study applied a vector autoregressive-based spillover model to assess the theorized linkage and applied robustness measures, including linear regression and the Granger causality test, for validation. The findings unveil distinct weak and moderate associations of sentiments across cryptocurrencies and stocks, respectively. The former is primarily driven by market sentiments while shaping economic news and social media sentiments. Meanwhile, the findings for stock return movements are found to be significantly associated with economic and market sentiments. This led to the inference that the cryptocurrency environment is an isolated system driven by internal sentiments, while stock markets are more economically integrated, and in both cases, social media sentiments are found to be the receiver of market spillover, weakly influencing economic news. The study is pioneering in its exploration of the interlinkage between selected macroeconomic sentiments; additionally, the comparative findings further add to the existing debate on influence of sentiment across financial markets. The varying realities identified in the findings hold significant practical implications for portfolio optimization, risk assessment and policy making. Full article

Promoting the high-quality development of farmers’ specialized cooperatives and narrowing regional development gaps is critical for advancing China’s rural revitalization strategy. Based on provincial panel data covering 30 Chinese regions from 2015 to 2023, this paper constructs a five-dimensional evaluation index system including standardized operation, operational performance, service scope, driving effect, and industrial upgrading, and adopts the entropy weight method to quantify the comprehensive development level of cooperatives. By combining spatial autocorrelation, kernel density estimation, the Dagum Gini coefficient and the Geodetector model, this paper explores the spatio-temporal evolution, regional disparities and multi-factor coupled driving mechanism of cooperative development. The main findings are as follows: (1) While the total quantity of cooperatives keeps expanding nationwide, their overall development level presents an evolutionary feature of declining first and then rising; industrial upgrading gradually becomes a new growth engine, whereas operational performance and driving effect slip downward. (2) The spatial layout of cooperatives maintains a typical pyramid structure; high-value agglomeration shifts from the Yangtze River Delta to southeast coastal regions, and low-value clusters are persistently concentrated in Northeast China. (3) The overall Dagum Gini coefficient reflects widening-then-shrinking regional gaps, and intra-eastern provincial differences constitute the primary source of nationwide spatial divergence. (4) Household consumption and rural labor force stock serve as core driving factors; regional economic development, agricultural production efficiency, rural human capital and land resource allocation form a coupled driving system, and all explanatory variables show mutual enhancement effects without offsetting interactions. Targeted policy suggestions are put forward to realize balanced and high-quality development of farmers’ specialized cooperatives across China. Full article

Accurate volatility forecasting and timely risk early warning are foundational requirements of financial risk management: Value-at-Risk estimates, portfolio risk limits, derivative hedging ratios, and stress-test scenario calibrations all depend on forward-looking volatility signals that remain reliable when market conditions depart from average. This paper develops a public-data causal multiscale wavelet spillover learning (CMWSL) framework that jointly addresses stock-index volatility forecasting and high-volatility early warning under strict walk-forward evaluation. CMWSL integrates three components: a heterogeneous autoregressive (HAR) persistence block as the dominant linear baseline, causal stationary wavelet transform (SWT) summaries that encode within-index multiscale market dynamics, and a cross-index spillover layer that tests whether medium- and long-scale wavelet energy from peer indices carries incremental risk-relevant information. The empirical analysis covers the S&P 500, Nasdaq-100, and Dow Jones Industrial Average over a 2513-step out-of-sample evaluation period from 2016 to 2025, with forecast horizons $h\in \{1,5,10\}$ and OHLC-based volatility targets. All preprocessing, wavelet decomposition, calibration rules, and warning thresholds are re-estimated inside each rolling training window to eliminate look-ahead bias. HAR remains the strongest average model in the main Rogers–Satchell specification, confirming that daily index volatility risk is highly persistence-driven. The multiscale extension delivers statistically significant improvements at longer horizons, in richer public macro-financial information environments, and under the Parkinson target. Clark–West tests detect significant spillover gains in five of nine index–horizon cells (CW $=4.83$, $p<0.001$ for S&P 500 at $h=10$). Critically, tail-conditioned and rolling-window diagnostics show that multiscale and cross-index gains concentrate in upper-volatility regimes and synchronized stress episodes—precisely the conditions in which risk management decisions are most consequential. For market-risk early warning, a logistic classifier built on the same causal feature pipeline delivers the most stable precision–recall performance across all settings, providing an interpretable and operationally auditable alert mechanism suitable for practical risk monitoring. Full article

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index $\overline{\alpha }=1.667$ across all constituents, substantially below the Gaussian benchmark of $\alpha =2.0$; (ii) Hurst exponents clustering below 0.5 ($\overline{H}=0.417$), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes. Full article