Search Results (167)

Reducing stock price crash risk is vital for capital market stability, particularly in emerging economies such as China. This study investigates whether Employee Stock Ownership Plans (ESOPs) can mitigate crash risk by analyzing panel data from A-share listed firms between 2014 and 2022. In contrast to prior research that has largely centered on managers and controlling shareholders, we highlight employees as active participants in corporate governance. Employing firm, year, and industry fixed effects, together with propensity score matching and instrumental variable techniques, we find robust evidence that ESOPs significantly reduce crash risk. Mediation analyses indicate that this effect operates through reduced agency costs both between managers and shareholders and between controlling and minority shareholders, as well as enhanced corporate productivity. Moderation tests further show that ESOPs are most effective when investor attention is high and when exit threats from non-controlling major shareholders are stronger. Heterogeneity analyses reveal that ESOPs exert greater influence in non-state-owned enterprises, in eastern regions, in firms with higher employee participation, and when shares are sourced from the secondary market. By extending the observation window to nearly a decade and deploying multiple robustness checks, this study provides one of the most comprehensive examinations of ESOPs and crash risk to date. It contributes to the literature by reframing employees as central actors in market stability and offers actionable insights for managers, investors, and regulators seeking to enhance corporate governance and reduce systemic risk. Full article

This study examines sustainability reporting practices (2010–2023) of emerging markets’ companies cross-listed in London as Global Depositary Receipts (GDRs). Despite the voluntary nature of sustainability reporting, all examined companies issued a corporate social responsibility (CSR) report. Additionally, 90 percent of companies hired an external auditor to provide assurance for CSR disclosure. Further, 99 percent of examined GDRs relied on the Global Reporting Initiative guidelines when preparing CSR reports, and 90 percent had a sustainability committee. Overall, cross-listed companies demonstrated an impressive level of CSR reporting. However, the gender diversity or independence of the board of directors is unrelated to the extent of CSR disclosure. Next, sustainability reporting scores are associated with lower liquidity position and are negatively related to reported earnings. This evidence supports the agency theory perspective in that executives of GDR cross-listed companies may use enhanced CSR reporting practices to divert attention from poor financial performance. The findings stand in contrast to previously documented results for New York cross-listed firms and have implications for regulators and global investors of European stock exchanges. Full article

In recent years, climate change mitigation and sustainable development have gradually become an important consideration in global economic and social governance. Firms’ carbon information disclosure is of great significance in global warming alleviation, drawing widespread attention from stakeholders, including institutional investors. However, limited attention has been devoted to how institutional investors in China affect such disclosure practices. This paper aims to explore the influence and underlying mechanisms of institutional investors on the quality of firms’ carbon information disclosure by employing fixed effect regression and mediating effect analysis on panel data that covers industrial firms traded on the A-share markets of the Shanghai and Shenzhen stock exchanges (SSE and SZSE) over the period from 2013 to 2023. The results suggest that institutional investors contribute to higher-quality firm carbon information disclosure, with analyst following serving as a mediating channel. Heterogeneity analysis further indicates that institutional investors’ positive influence is stronger among state-owned firms. Overall, the study highlights the role of institutional investors in advancing firms’ low-carbon development and offers practical guidance for improving carbon information disclosure. Full article

The study employs principal component analysis (PCA) to construct an investor attention index derived from seven key variables: abnormal trading volume, extreme returns, past returns, nearness to the 52-week high, nearness to the historical high, Google search volume, and mutual fund inflows. Subsequently, the research examines the impact of the investor attention index on the KSE-100 index excess returns. The analysis covers monthly data from January 2004 to December 2024. The PCA identified four components and constructed attention indices: $A^{PCA1}$ has highest weights of nearness to the 52-week high, abnormal trading volume, past returns, and mutual funds inflows; $A^{PCA2}$ has major weights of abnormal trading volume, extreme returns, past returns, and Google search volume; $A^{PCA3}$ has nearness to the 52-week high, nearness to the historical high, extreme returns, and mutual funds inflows; and $A^{PCA4}$ has nearness to the historical high, extreme returns, Google search volume, and mutual funds inflows. The $A^{PCA1}$ and $A^{PCA4}$ have a positive and significant impact on the excess returns of the KSE-100 index. This suggests that when investors are more motivated to invest, herding behavior increases, leading to improved index performance and higher returns. Subsequently, $A^{PCA3}$ has a negative but significant impact on index returns, indicating that a lack of investor interest leads to reduced trading activity and weaker index performance. The findings of this study have important implications for policymakers, investors, and mutual fund managers to understand the patterns of investor attention, creating policies and procedures to make the financial markets more transparent and protect the investor’s rights. Full article

With the growing prominence of large technology firms and the shift in news dissemination driven by social media, scholars have increasingly examined how public discourse about these companies shapes financial markets. Focusing on Apple, Amazon, and Microsoft during the transitional period of January 2015–January 2020, this study evaluates attention and sentiment across traditional news media, social media, and web search in relation to stock market outcomes. We use relatively fine-grained weekly data to link media attention and sentiment to stock returns, volatility, and trading volume. To compare media sentiment across sources, we apply FinBERT-based sentiment analysis, drawing on advances in domain-specific language modeling tailored to financial texts. Results show that social media sentiment (Twitter), exerts a consistently positive and significant influence, while the effects of traditional news media (New York Times) and web search activity (Google Trends) are more irregular. The impact also varies across firms: Twitter sentiment is strongly related to trading volume and volatility for Amazon and Microsoft, but appears less influential for Apple, whose large trading base may dilute the effect. These findings offer a historical baseline for media–finance interactions and highlight how text analysis illuminates the pre-COVID era of big technology firms. Full article

Stock trend prediction is a complex and crucial task due to the dynamic and nonlinear nature of stock price movements. Traditional models struggle to capture the non-stationary and volatile characteristics of financial time series. To address this challenge, we propose the Hybrid Relational Transformer (HRformer), which specifically decomposes time series into multiple components, enabling more accurate modeling of both short-term and long-term dependencies in stock data. The HRformer mainly comprises three key modules: the Multi-Component Decomposition Layer, the Component-wise Temporal Encoder (CTE), and the Inter-Stock Correlation Attention (ISCA). Our approach first employs the Multi-Component Decomposition Layer to decompose the stock sequence into trend, cyclic, and volatility components, each of which is independently modeled by the CTE to capture distinct temporal dynamics. These component representations are then adaptively integrated through the Adaptive Multi-Component Integration (AMCI) mechanism, which dynamically fuses their information. The fused output is subsequently refined by the ISCA module to incorporate inter-stock correlations, leading to more accurate and robust predictions. Extensive experiments on the NASDAQ100 and CSI300 datasets demonstrate that HRformer consistently outperforms state-of-the-art methods, e.g., achieving about 0.83% higher Accuracy and 1.78% higher F1-score than TDformer on NASDAQ100, with Sharpe Ratios of 1.5354 on NASDAQ100 and 0.5398 on CSI300, especially in volatile market conditions. Backtesting results validate its practical utility in real-world trading scenarios, showing its potential to enhance investment decisions and portfolio performance. Full article

This research examines the time–frequency co-movement patterns among the Johannesburg Stock Exchange (JSE) size-based indices, utilizing daily data covering the period from November 2016 to December 2024. To conduct the analysis, three sophisticated wavelet techniques are applied: the Maximal Overlap Discrete Wavelet Transform (MODWT), the Continuous Wavelet Transform (WTC), and the Wavelet Phase Angle (WPA) model. Subsequently, the Multivariate Generalized Autoregressive Conditional Heteroscedasticity–Asymmetric Dynamic Conditional Correlation (MGARCH-DCC) model is employed to evaluate the robustness of the findings. The results reveal that the co-movement among the JSE size-based indices is influenced by investment holding periods and prevailing market conditions. Notably, a lead–lag relationship is identified, indicating that a single size-based index often drives the co-movement of the others. These findings carry important implications for investors, policymakers, and portfolio managers. Investors should account for optimal holding periods to avoid increased correlation and reduced diversification benefits. Policymakers are advised to mitigate financial market uncertainty, particularly during bearish phases, to manage excessive index co-movement. Portfolio managers must integrate both holding periods and market conditions into their investment strategies. This research offers a novel contribution to the South African investment landscape by providing practical and risk-mitigating insights into the role of JSE size-based indices within diversified portfolios—a topic that has received limited attention despite its growing relevance. Full article

This study presents a novel approach to financial time series forecasting by introducing asymmetric loss functions. This is specifically designed to enhance directional accuracy across major stock indices (S&P 500, DJI, and NASDAQ Composite) over a 33-year time period. We integrate these loss functions into an attention-based Long Short-Term Memory (LSTM) framework. The proposed loss functions are evaluated against traditional metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and other recent research-based losses. Our approach consistently achieves superior test-time directional accuracy, with gains of 3.4–6.1 percentage points over MSE/MAE and 2.0–4.5 percentage points over prior asymmetric losses, which are either non-differentiable or require extensive hyperparameter tuning. Furthermore, proposed models also achieve an F1 score of up to 0.74, compared to 0.63–0.68 for existing methods, and maintain competitive MAE values within 0.01–0.03 of the baseline. The optimized asymmetric loss functions improve specificity to above 0.62 and ensure a better balance between precision and recall. These results underscore the potential of directionally aware loss design to enhance AI-driven financial forecasting systems. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

Financial time series prediction remains a challenging task due to the inherent non-stationarity, noise, and complex temporal dependencies present in market data. Traditional forecasting methods often fail to capture the multifaceted nature of financial markets, where temporal proximity, trend dynamics, and volatility patterns simultaneously influence price movements. To address these limitations, this paper proposes the Enhanced Multi-Aspect Transformer (EMAT), a novel deep learning architecture specifically designed for stock market prediction. EMAT incorporates a Multi-Aspect Attention Mechanism that simultaneously captures temporal decay patterns, trend dynamics, and volatility regimes through specialized attention components. The model employs an encoder–decoder architecture with enhanced feed-forward networks utilizing SwiGLU activation, enabling superior modeling of complex non-linear relationships. Furthermore, we introduce a comprehensive multi-objective loss function that balances point-wise prediction accuracy with volatility consistency. Extensive experiments on multiple stock market datasets demonstrate that EMAT consistently outperforms a wide range of state-of-the-art baseline models, including various recurrent, hybrid, and Transformer architectures. Our ablation studies further validate the design, confirming that each component of the Multi-Aspect Attention Mechanism makes a critical and quantifiable contribution to the model’s predictive power. The proposed architecture’s ability to simultaneously model these distinct financial characteristics makes it a particularly effective and robust tool for financial forecasting, offering significant improvements in accuracy compared to existing approaches. Full article

This study explores how firms’ strategic orientations—operational efficiency, customer intimacy, and product innovation—along with corporate social responsibility (CSR) disclosure, influence investment efficiency in emerging markets. Using 1594 firm-year observations from companies listed on the Tehran Stock Exchange (TSE) between 2015 and 2024, we combine quantitative analysis with textual evidence from Management Discussion and Analysis (MD&A) reports. The findings show that operational efficiency and customer intimacy are generally linked to lower investment efficiency, reflecting possible resource misallocation and short-term priorities. In contrast, product innovation has a more nuanced impact: it improves investment efficiency in R&D-intensive sectors and during stable economic periods. CSR disclosure is also negatively associated with investment efficiency, suggesting that while CSR reporting enhances legitimacy and stakeholder trust, it may shift managerial attention and resources away from core investments. Robustness checks—including firm fixed effects, alternative keyword dictionaries, placebo tests, and endogeneity controls—support these results. Additional sub-sample analyses indicate that strategic orientations and CSR disclosure also function as channels of financial innovation: operational efficiency fosters disciplined resource allocation, product innovation supports sustainable growth, and customer intimacy strengthens transparency and stakeholder engagement. Full article

This paper investigates how market expectations regarding profitability mean reversion are reflected in stock prices. We propose a model that infers implicit expectations of future earnings using publicly available share prices based on the assumption that markets efficiently incorporate forward-looking information. The study compares several adjustment models, including the classical partial adjustment framework and a mean reversion model, to identify the most suitable mechanism to capture the dynamics of expected earnings. Special attention is paid to the statistical characteristics of accounting data and ratio-based measures, which influence model performance. Using a dataset covering a twenty-year period, we find that the mean reversion model consistently outperforms partial adjustment models in explaining the behavior of cyclical and random components converging toward a long-term trend. The findings suggest that market prices embed rational expectations of profitability reversion, especially in periods of above average performance. These results align with previous research and provide a robust framework for understanding how earnings expectations are formed and adjusted in financial markets. Full article

Owing to the high volatility, non-stationarity, and complexity of financial time-series data, stock market trend prediction remains a crucial but difficult endeavor. To address this, we present a novel Multi-Perspective Fused Attention model (SGR-Net) that amalgamates Random, Global, and Sparse Attention mechanisms to improve stock trend forecasting accuracy and generalization capability. The proposed Fused Attention model (SGR-Net) is trained on a rich feature space consisting of thirteen widely used technical indicators derived from raw stock index prices to effectively classify stock index trends as either uptrends or downtrends. Across nine global stock indices—DJUS, NYSE AMEX, BSE, DAX, NASDAQ, Nikkei, S&P 500, Shanghai Stock Exchange, and NIFTY 50—we evaluated the proposed model and compared it against baseline deep learning techniques, which include LSTM, GRU, Vanilla Attention, and Self-Attention. Experimental results across nine global stock index datasets show that the Fused Attention model produces the highest accuracy of 94.36% and AUC of 0.9888. Furthermore, even at lower epochs of training, i.e., 20 epochs, the proposed Fused Attention model produces faster convergence and better generalization, yielding an AUC of 0.9265, compared with 0.9179 for Self-Attention, on the DJUS index. The proposed model also demonstrates competitive training time and noteworthy performance on all nine stock indices. This is due to the incorporation of Sparse Attention, which lowers computation time to 57.62 s, only slightly more than the 54.22 s required for the Self-Attention model on the Nikkei 225 index. Additionally, the model incorporates Global Attention, which captures long-term dependencies in time-series data, and Random Attention, which addresses the problem of overfitting. Overall, this study presents a robust and reliable model that can help individuals, research communities, and investors identify profitable stocks across diverse global markets. Full article

Board gender diversity (BGD) has gained attention as a governance mechanism that may influence corporate financial decisions. However, empirical evidence from Latin America and the Caribbean (LAC) remains limited despite the region’s significant gender disparities in corporate leadership and distinct institutional characteristics. This study examines how BGD affects capital structure decisions in LAC firms, drawing on agency theory and resource dependency theory. We analyze a panel dataset of 403 firms from 2015 to 2022, sourced from the London Stock Exchange Group database, using fixed effects models with Driscoll–Kraay standard errors to control for firm heterogeneity and econometric concerns. Results show that BGD is significantly and negatively associated with leverage ratios, with a one percentage point increase in female board representation corresponding to a 0.15 to 0.25 percentage point decrease in debt-to-capital ratios. This relationship is robust across multiple specifications and exhibits threshold effects, with stronger impacts when female representation reaches 20% or higher. The negative association is more pronounced for larger firms, consistent with enhanced governance benefits in complex organizations. Our findings suggest that gender-diverse boards exercise more effective oversight of financial decisions, leading to more conservative capital structures in emerging markets where governance mechanisms are particularly important for firm credibility and stakeholder confidence. Full article

As the region with the earliest housing stock market and the most advanced development in China, the Pearl River Delta has experienced extensive housing demolition and construction, leading to buildings having short lifespans. The environmental pollution generated during this process has brought attention to the concept of green buildings. However, whether due to previous patterns of demolition and construction or the significant impacts of social and economic changes in the current and future housing stock contexts, the comprehensive adaptability of human-centered living spaces remains a crucial issue. This focus is strongly related to the residents’ psychological responses, such as sense of belonging, safety, and atmosphere, across different scales of physical environment. However, most housing evaluation systems regarding sustainable issues are green building evaluation systems. And their concept and practice are often accompanied by a neglect of the interrelationship between people and the built environment, as well as a lack of an appropriate methodological framework to integrate these elements in the temporal dimension. This paper primarily tries to provide new answers to old questions about housing durability by reconceptualizing evaluation systems beyond ecological metrics, while simultaneously challenging accepted answers that privilege material and energy indicators over sociocultural embeddedness. Moreover, an effective housing evaluation framework must transcend purely technical or ecological indicators to systematically integrate the temporal and sociocultural factors that sustain long-term residential quality, particularly in rapidly transforming urban contexts. Therefore, theories closely related to building longevity, such as open building and the “level” strategy, were introduced. Based on this combined methodological framework, selected cases of local traditional housing and green building evaluation systems were studied, aiming to identify valuable longevity factors and improved evaluation methods. Furthermore, two rounds of expert consultation and a data analysis were conducted. The first round helped determine the local indexes and preliminary evaluation methods, while the second round helped confirm the weighting value of each index through a questionnaire study and data analysis. This systematic study ultimately established a preliminary long-term housing evaluation system for the Pearl River Delta. Full article