Search Results (643)

Stock index forecasting is essential for financial market analysis and risk monitoring, yet it remains challenging because index price series are nonlinear, non-stationary, and driven by heterogeneous market factors. Existing methods remain limited in preserving local price patterns, capturing multi-scale local dependencies, and integrating attention-derived structures with long-range state-space representations. To address these limitations, we propose AG-SSM, an attention-guided state-space model for multi-step stock index forecasting. The model first uses variable-wise patch embedding to construct local semantic units, which are then processed by the AG-SSM architecture for temporal representation learning. Its core block integrates dual-path local attention (DPLA), S4D-based state-space feature generation, attention-guided aggregation (AGA), and gated update (GU). Specifically, DPLA combines sliding and dilated local attention to capture contiguous and sparsely distributed dependencies, while AGA reuses local attention maps to refine state-space features, thereby coupling local market structures with long-range sequential dynamics. Experiments on six stock index datasets (SSE, SZSE, SMESE, SP500, DJIA, and NIKKEI225) under one-, five-, ten-, and fifteen-step forecasting horizons show that AG-SSM achieves the lowest horizon-averaged MAPE on all six datasets while maintaining competitive performance across other metrics and individual horizons. Averaged over five independent runs, the horizon-averaged MAPE values are 1.5466%, 2.2173%, 2.2293%, 1.4373%, 1.2995%, and 1.8738% on the six datasets, respectively. Ablation studies, state-space variant comparisons, sensitivity analyses, and statistical tests further support the effectiveness and robustness of the proposed framework. Full article

This study investigates whether Chinese sectoral stock returns respond heterogeneously to international oil price movements and whether such responses vary across crisis regimes. Using daily data from January 2015 to June 2026, we analyze ten major sectoral indices of the Shanghai Stock Exchange, Dubai crude oil returns, oil price volatility, and the US dollar–Chinese yuan exchange rate. Dubai crude oil is used as the benchmark because it reflects Asia-oriented crude oil pricing and China’s imported energy cost conditions. The empirical analysis proceeds in several steps. First, baseline regressions are estimated to examine the average effect of oil returns on sectoral stock returns while controlling for domestic market-wide movements and exchange rate changes. Second, market-adjusted sectoral returns are used to isolate genuine sector-specific oil transmission from common market shocks. Third, GARCH(1,1)-based oil volatility and crisis-period interaction terms are introduced to identify the uncertainty effect of oil price movements during the COVID-19 pandemic, the post-pandemic period, and the US–Iran/Middle East geopolitical conflict period. Finally, DCC-GARCH dynamic conditional correlations are used as a robustness check. The results show that Chinese sectoral stock returns do not respond uniformly to oil price movements. The timing-adjusted results provide little evidence that lagged Dubai oil returns systematically predict next-day sectoral returns. Nevertheless, oil-price uncertainty exhibits selective and regime-dependent effects, particularly for Energy, Materials, Consumer Staples, Consumer Discretionary, Industrials, and Utilities. Full article

There is a large and rapidly growing stock of retired power batteries from new energy vehicles in China. Unregulated informal recycling and improper disposal of these waste batteries trigger serious environmental hazards. Though a batch of regulatory policies on battery recycling have been released in recent years, the power battery recycling sector still faces prominent governance bottlenecks, especially ambiguous responsibility division and poor implementability under the entrusted recycling mode. To fill the existing research gap regarding tripartite interest conflicts and pricing mechanisms in entrusted recycling, this paper constructs a three-party evolutionary game model covering power battery producers, recyclers and government regulators. Two pricing models are further developed to distinguish producer self-operated recycling and third-party entrusted recycling channels. Numerical simulation is adopted to investigate multi-stakeholder interest contradictions, dynamic evolutionary trajectories and equilibrium stability of the recycling system, and the influences of subsidy intensity, supervision intensity and recycling cost on participants’ strategic choices are quantitatively analyzed. The research results demonstrate that inadequate government supervision and insufficient economic returns for formal recyclers serve as the primary obstacles hindering the effective deployment of entrusted recycling. An inherent and reasonable price gap exists between self-operated and entrusted recycling modes. Essentially, the price differential of standardized entrusted recycling represents the profit margin conceded by producers to recyclers instead of direct financial subsidies. To solve existing industry problems, this study proposes targeted recommendations for tripartite collaboration. The government should refine the regulatory framework of Extended Producer Responsibility and adopt differentiated reward and penalty mechanisms. Producers are expected to standardize entrusted recycling management and formulate a scientific pricing range for retired batteries. Recyclers ought to advance recycling technologies and maintain standardized operations. Collective efforts from all stakeholders can facilitate the long-term sustainability of the closed-loop recycling system for retired power batteries. Full article

Stock market price prediction remains a persistent challenge owing to the non-stationarity, high noise content, and intricate spatiotemporal dependencies that characterize financial time series. Existing approaches typically excel at either local pattern extraction or long-range dependency modeling, yet seldom reconcile both within a unified framework. This paper introduces a CNN–Transformer dual-channel architecture equipped with a dynamic attention fusion module for stock price forecasting. The convolutional channel applies hierarchical dilated convolutions to distill fine-grained local patterns from multi-indicator sequences while suppressing high-frequency noise. Simultaneously, the Transformer channel employs multi-head self-attention to capture long-distance temporal correlations and regime-shift dynamics. A learnable gating mechanism then fuses the two feature streams by adaptively weighting local detail against global trend information according to market conditions. Experiments conducted on four real-world stock datasets spanning the S&P 500, CSI 300, NASDAQ Composite, and Hang Seng Index show that the proposed model reduces mean absolute error by 9.7–15.3% and root mean square error by 9.5–13.8% relative to competitive baselines including LSTM, CNN–LSTM, Informer, and PatchTST. Ablation studies further indicate that both channels and the fusion module contribute to prediction accuracy, and the architecture remains effective across markets with differing volatility profiles. Full article

This study examines the asymmetric relationship between housing prices and stock market returns across China’s major economic regions. Specifically, it investigates whether positive and negative shocks exhibit different transmission dynamics and whether these dynamics vary across regions characterized by different levels of financial development and housing market maturity. Using monthly data from 2005 to 2024, the study employs region-specific asymmetric vector autoregression (VAR) models, asymmetric Granger causality tests, and generalized impulse response analysis based on asymmetric decompositions of housing prices and stock market returns. The results suggest that statistically significant housing-to-stock predictability is observed primarily following negative housing price shocks in selected regions, whereas positive shocks generally exhibit weaker or statistically insignificant predictive effects. Conversely, positive stock market shocks generally provide more consistent evidence of stock-to-housing predictability, particularly in the Eastern and Central regions, although the responses are more mixed in the Western region and vary in magnitude, statistical significance, and persistence across regional markets. Overall, the results provide evidence of heterogeneous dynamic transmission patterns across China’s major economic regions and suggest that housing-related downside risk may represent an important source of regional macro-financial vulnerability. Full article

This study extends analytical barrier-option pricing models by jointly incorporating Markov-switching liquidity risk, asymmetric double-exponential jump risk, and a state-dependent Heath–Jarrow–Morton interest-rate structure. The underlying stock price dynamics under imperfect liquidity are driven by a Markovian regime-switching liquidity-adjusted double-exponential jump-diffusion model, and the risk-neutral valuation is obtained through an Esscher transform. Compared with existing DEJD barrier-option, liquidity-adjusted option-pricing, and Markov-modulated stochastic-interest-rate models, the proposed models highlight the joint effects of liquidity conditions, jump risk, regime switching, and state-dependent forward rates on European-style barrier option prices. Numerical illustrations based on Monte Carlo simulation are provided to examine model implications and benchmark special cases. Full article

The decentralized nature of cryptocurrencies makes them an appealing choice for investors around the world in times of uncertainty. Recent global health concerns, political conflicts, and subsequent economic issues have contributed to uncertainty in financial markets worldwide. In the first part of this study, we examine the recent changes in the price of Bitcoin and explore the relationship between various financial factors and the price of Bitcoin. Previous studies have been inconclusive in establishing a linear relationship between Bitcoin and various market indexes, including the S&P 500, Dow Jones Industrial Average, Nasdaq 100, Russell 2000, and Nikkei 225, as well as the prices of commodities such as Gold and Oil. In this study, to address the issue of uncertainty due to the volatility of the stock market and its effect on the Bitcoin price, we examine the relationship between the popular measure of the stock market’s expectation of volatility based on the S&P 500 index (VIX) and Bitcoin. Some non-linear analytical methods are employed to examine the impact of the aforementioned indices, commodities, and financial uncertainty measures on Bitcoin prices. This study utilizes different time frames, including weekly and monthly data from 1 January 2016 to 1 March 2026. In the second part of this study, Principal Component Analysis is utilized. Since the suggested analytical models are based on correlated financial factors, Principal Component Analysis will be used to address this issue while maintaining the high explanatory power of our suggested models. Full article

This study examines whether corporate governance helps explain cross-sectional stock return variation on the Tokyo Stock Exchange. We construct 32 portfolios sorted by firm size, book-to-market equity, profitability, investment, and a governance indicator distinguishing institutional and participatory structures. Using monthly data from 2010–2017, we estimate Fama–French five-, six-, and seven-factor models with ARIMAX specifications to address serial correlation. Unlike most existing Japan-focused studies that examine corporate governance primarily through firm-level regressions or simple portfolio sorts without incorporating it as a risk factor, this study adopts a more comprehensive approach by constructing governance-sorted portfolios and including a governance-mimicking factor (IMP) as an additional risk factor within multi-factor asset pricing models. We find governance has strong explanatory power, second only to market risk, and is associated with lower returns for firms with greater shareholder participation. Furthermore, governance alters size and value effects, while momentum is largely insignificant. In sum, the findings provide valuable information that portfolio managers, analysts, and investors may use for optimizing portfolio choices. Full article

Whether environmental, social, and governance (ESG) disclosure stabilizes share prices or merely masks bad news, it remains unsettled, and the evidence is conspicuously weak whenever the relationship is assumed to be linear. This study revisits the question by allowing the effect of ESG disclosure on future stock price crash risk to be nonlinear and by breaking down disclosure into its environmental, social, and governance components. Using an unbalanced panel of non-financial firms listed on the Ho Chi Minh Stock Exchange over 2018–2024, we estimate firm and year fixed effects models with firm-clustered standard errors, measuring one-year-ahead crash risk by negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL). Consistent with prior work, the linear association between overall ESG disclosure and crash risk is statistically insignificant. Once a quadratic term is introduced, however, a U-shaped relationship emerges, and dimension-level tests show that this curvature is driven almost entirely by social disclosure: the linear term is negative and the squared term positive and significant for both crash risk proxies, with turning points of 0.3316 (NCSKEW) and 0.2918 (DUVOL). The U shape is confirmed by the formal test of Lind and Mehlum for both proxies, is robust to additional profitability and valuation controls and, most strongly for NCSKEW, to panel-corrected and feasible-GLS estimators. Low variance inflation factors confirm that multicollinearity does not affect the estimates. The findings support a “too-much-of-a-good-thing” interpretation: social disclosure improves transparency and reduces crash risk up to a moderate threshold, beyond which incremental, hard-to-verify narrative disclosure becomes consistent with impression management and heightens crash risk. Because the turning point lies below the first quartile of social disclosure, most sample firms already operate where additional disclosure raises crash risk. This study reframes the ESG crash risk debate around the level and dimension of disclosure rather than its mere quantity. Full article

This study provides new evidence on the predictability of confidence indices in China’s stock returns. We demonstrate that, during the sample period from January 2005 to December 2022, the business confidence index (BCI) positively and significantly predicted subsequent stock market returns, outperforming mainstream economic predictors and other confidence indices. Further, for the pricing effectiveness of the stock market, the BCI and investor sentiment provide complementary sources of information. The predictive power of confidence indices for stock market returns declined significantly during the COVID-19 pandemic. Meanwhile, confidence indices predicted better during bear market periods compared to bull market periods. Finally, in practical investment applications, the BCI and alternative confidence index produce appreciable economic gains for investors. These empirical results also pass the robustness test. Full article

This study proposes a hybrid deep learning model named LSTM-CBAM-Time2Vec-KAN based on symmetry awareness and optimized multi-module collaborative features, aiming to improve the accuracy and stability of stock price prediction. To address common shortcomings in traditional forecasting models such as insufficient feature extraction, difficulties in parameter optimization, and inadequate utilization of temporal characteristics, the research innovatively exploits the symmetry inherent in financial time series, particularly their temporal periodicity and cross-dimensional feature consistency, to construct an intelligent prediction framework that integrates multiple modules. First, wavelet transform is applied to perform multi-scale decomposition and signal reconstruction on the raw stock price sequence, effectively extracting high signal-to-noise ratio features. Second, the Northern Goshawk Optimization (NGO) algorithm is employed to jointly optimize key hyperparameters of the model, including the LSTM hidden layer dimension and CBAM compression ratio, thereby resolving the challenge of parameter coupling across modules. Third, the CBAM attention mechanism enhances the importance of temporal features extracted by LSTM through a dual mechanism of channel and spatial attention, enabling the model to focus on critical price movement points. Meanwhile, Time2Vec encoding transforms temporal information into embedding representations with periodic properties, effectively capturing cyclical patterns at daily, weekly, and monthly trading intervals. Finally, the Kolmogorov–Arnold network (KAN) fuses multimodal features and produces precise predictive outputs. Experimental results show that the proposed model significantly outperforms all baseline models in four evaluation metrics, namely mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), which verifies its superior prediction accuracy and robustness. Furthermore, analyses of stock price forecasting under different time spans and simulated trading performance under various trading strategies further demonstrate that this study provides a feasible and effective technical solution for financial time-series forecasting, with important theoretical research value and practical application value. Full article

This study investigates how disaggregated environmental, social, and governance (ESG) indicators are associated with firm value, stock returns, and the cost of capital, emphasizing the moderating role of ESG controversies. Using panel data of publicly listed firms in the Asia-Pacific region from Refinitiv and applying firm and year fixed effects with forward-looking specifications (t to t + 3), the results show that the ESG–performance association is neither uniform across indicators nor stable over time: several environmental indicators lose statistical relevance beyond the short horizon, while selected social and governance indicators remain associated with outcomes only where materiality is high. The central finding is that ESG controversies do not merely add explanatory power but systematically reshape these relationships—attenuating or reversing the association between ESG and firm value or returns, while strengthening the association with the cost of capital. This pattern indicates that ESG is priced by the market only when it is perceived as credible, underscoring reputational risk—rather than ESG performance itself—as the dominant mechanism linking sustainability information to firm outcomes. Full article

This study proposes a novel hybrid mathematical framework that integrates the Time-Varying Parameter Vector Autoregression (TVP-VAR) connectedness approach with Long Short-Term Memory (LSTM) deep learning networks to analyze, forecast, and manage volatility spillovers in green financial markets. The framework is motivated by the increasing complexity of risk transmission across sustainable assets, including green bonds, renewable energy stocks, carbon markets, and conventional energy assets. The proposed methodology follows a two-stage structure. First, the TVP-VAR model is employed to quantify dynamic connectedness and time-varying spillover effects across markets. Second, the extracted connectedness measures are used as inputs to an LSTM network to forecast future systemic risk dynamics and generate forward-looking variance–covariance matrices for portfolio optimization and hedging purposes. Using daily data from 2015 to 2025, the empirical results reveal that renewable energy stocks are the dominant transmitters of volatility within the system, exerting substantial spillover effects on green bonds and other sustainable assets. The forecasting evaluation demonstrates that the proposed hybrid TVP-VAR-LSTM framework significantly outperforms traditional econometric models (ARIMA and GARCH) as well as conventional machine-learning benchmarks (SVR, Random Forest, and XGBoost), reducing the Root Mean Squared Error (RMSE) by more than 46% in out-of-sample forecasting. Moreover, the enhanced forecasting accuracy translates into economically meaningful benefits, leading to substantial reductions in realized portfolio risk and improved hedging effectiveness. The findings further highlight the importance of carbon pricing mechanisms and standardized green bond certification in mitigating volatility transmission across sustainable financial markets. Overall, this study contributes to the literature on financial mathematics, systemic risk modeling, and machine learning in green finance by providing a unified framework for volatility spillover analysis, forecasting, and dynamic portfolio optimization. Full article

This article examines Liquid Equity Rewards (LERs), a proposed blockchain-enabled mechanism designed to provide shareholders with time-weighted, utility-only incentives as a potential tool for improving corporate governance. LERs employ a dual architecture comprising voucher-based rewards for off-chain equities and programmable on-chain units for tokenized stocks to encourage shareholder retention amid proxy battles, activist challenges, and corporate political complexities. Drawing on NASDAQ’s tokenized stock framework, stablecoin infrastructure, and DeFi liquid staking principles, this article develops a conceptual and normative framework for LERs and evaluates its potential effectiveness relative to conventional defenses such as poison pills. The analysis assesses LER’s plausible legal compatibility with Delaware corporation law, U.S. securities rules, and the EU’s MiCA framework, while acknowledging that definitive legal conclusions require case-specific adjudication and future regulatory interpretation. The article advances four testable hypotheses regarding LER’s potential to mitigate stock price volatility, reduce activist success rates, and address ESG, M&A, and political expenditure disputes in a market context shaped by shareholder activism. The proxy-fight context is the principal application; ESG, M&A, political-spending, and executive-compensation contexts are discussed as illustrative extensions of the framework rather than as equally mature use cases. A comparative evaluation against existing governance mechanisms and a cost–benefit analysis suggest that LER’s governance enhancements and market opportunities may outweigh implementation challenges, subject to empirical validation. This article contributes a structured analytical framework and identifies conditions under which LERs could offer a scalable, transparent alternative that fosters stakeholder alignment. Full article

Announcements of major transport infrastructure can reorganize land markets long before construction begins, as expectations are capitalized into prices and building decisions—with direct implications for sustainable territorial planning. This study examines the real estate response to the 2024 announcement of the Torquemada airport project in the Valparaíso Region, Chile. We assemble a high-resolution microterritorial panel at the block–semester–land-use level, integrating three Chilean administrative registers: the SII cadastre (over 100 million construction lines across 16 semestral snapshots, 2018–2025), the F2890 conveyance records (1.49 million geolocated transactions), and the daily Unidad de Fomento series. We estimate a multi-outcome spatial difference-in-differences design, complemented by an event study, land-use heterogeneity analysis, local indicators of spatial association, placebo tests, spatial-weight sensitivity analysis, and the heterogeneity-robust Callaway–Sant’Anna estimator. We find a robust increase in new-parcel construction in the zone of influence—identified by an annual event study against never-treated controls whose pre-announcement coefficients are small and trendless, in sharp contrast to the uniformly positive pre-trends of the expansion and aggregate-stock series—together with selective appreciation of non-residential uses and no detectable effect on housing value. The expansion and aggregate-stock components are not separately identified: their pre-announcement trends are strongly non-parallel, so the corresponding fixed-effects coefficients are read as design-conditional associations. The evidence supports an activation of the extensive margin (new-parcel building) rather than a recomposition away from densification. We read the evidence as the anticipatory footprint of the announcement rather than a point causal effect. Detecting this footprint before construction enables anticipatory value capture and sprawl-containment policy while the planning window remains open. Full article