Search Results (117)

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

ESG has emerged as a key non-financial indicator, drawing significant investor focus. Disparities in ESG ratings may skew investor perceptions, potentially endangering stock values and financial market stability. This paper examines the link between ESG rating divergences and stock price crash risk, drawing on data from six Chinese and global ESG rating agencies. Focusing on Shanghai and Shenzhen A-share listed firms, it analyzes information from 2015 to 2022 within the theoretical contexts of information asymmetry and external monitoring. This study finds that ESG rating divergence markedly elevates stock price crash risk, a relationship that persists through a series of robustness checks. Specifically, the mechanisms operate through two key pathways: increased reputational damage risk due to information asymmetry and reduced external monitoring due to weakened external governance. The results of the heterogeneity analysis indicate that ESG rating divergence exacerbates stock price crash risk more significantly for non-state-owned firms, firms with low levels of marketization, and firms in high-pollution industries. This study provides clear actionable strategic paths and policy intervention points for investors to avoid risks, firms to optimize management, and regulators to formulate policies. Full article

In the past decade, agricultural biogas plants have become one of the key tools driving the energy transition in rural areas. Nevertheless, their development in Poland still lags behind that in Western European countries, suggesting the existence of barriers that go beyond technological or regulatory issues. This study aims to examine how Polish farmers perceive the risks and expected benefits associated with investing in biogas plants and which of these perceptions influence their willingness to invest. The research was conducted in the second quarter of 2025 among farmers planning to build micro biogas plants as well as owners of existing biogas facilities. Geographic Information System (GIS) tools were also used in selecting respondents and identifying potential investment sites, helping to pinpoint areas with favorable spatial and environmental conditions. The findings show that both current and prospective biogas plant operators view complex legal requirements, social risk, and financial uncertainty as the main obstacles. However, both groups are primarily motivated by the desire for on-farm energy self-sufficiency and the environmental benefits of improved agricultural waste management. Owners of operational installations—particularly small and medium-sized ones—tend to rate all categories of risk significantly lower than prospective investors, suggesting that practical experience and knowledge-sharing can effectively alleviate perceived risks related to renewable energy investments. Full article

This paper examines the moderating role of corporate social responsibility (CSR) on the relationship between environmental, social, and governance (ESG) dimensions and investor decision-making in Jordan. Data were collected using a structured questionnaire designed for institutional investors and financial analysts, capturing perceptions of ESG, CSR, and investment behavior. A stratified random sample of 350 professionals across the financial, industrial, and service sectors was surveyed. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings show that environmental and social dimensions have positive effects on investor decisions, with governance dimensions having a negative effect. Notably, CSR has a negative moderating effect on the governance dimensions and investor decision, with no observed statistical moderating effect for environmental or social dimensions. This research unravels the multidimensional role of CSR in building the ESG-investor decision interface and identifies a counterintuitive negative moderating impact of CSR on governance, contributing to the existing literature on sustainability alignment in emerging markets. The results offer practical implications for companies aiming to attract sustainability-oriented investors by indicating the necessity for an integrated and genuine CSR and ESG approach. Full article

Bank operations are contingent upon investor confidence, particularly during periods of economic distress. If investor confidence drops, a bank faces difficulties obtaining money, higher borrowing costs, and lower stock values. Non-performing loans (NPLs) potentially jeopardize a bank’s long-term viability and short-term profitability, and investors are naturally wary of institutions that pose a high credit risk. The purpose of the study was to explore how non-performing loans influence investor confidence in banks. A purposive sampling technique was used to identify 253 New York Stock Exchange banks in the Thomson Reuters Eikon DataStream that satisfied all the inclusion and exclusion selection criteria. The Common Correlated Effects Mean Group (CCEMG) and Generalized Method of Moments (GMM) models were used to analyze the data, providing insight into the relationship between the variables. The study discovered that NPLs had a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. Furthermore, the bank’s age was found to have a positive and significant relationship with the P/E and P/B ratio. The moderating relationship between NPLs and bank age was found to have a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. The findings underscore the importance of asset quality and institutional reputation in influencing market perceptions. Bank managers should focus on managing non-performing loans effectively and leveraging institutional credibility to sustain investor confidence, particularly during financial distress. Full article

The sustainable supply chain management (SSCM) literature does not directly address Scope 3 emissions despite their role as primary drivers of greenhouse gas emissions. This study aims to provide an overview of the main themes through which the SSCM literature has considered Scope 3 emissions and identify further avenues for research. A systematic literature review (SLR) was conducted. Scopus and Web of Science were the databases considered. Sixty-one papers were included in the analysis. Most papers focus on assessing and estimating Scope 3 emissions, followed by papers that discuss the reporting of Scope 3 emissions. These papers shed light on how firms may not report Scope 3 emissions if the information is negative to improve investors’ perception of the firm. The last group of papers discusses practices and strategies to manage Scope 3 emissions. The main challenge identified in establishing strategies to manage Scope 3 emissions is engagement with stakeholders, as, generally, only one or two tiers of the value chain cooperate. This study is the first to organize the literature on Scope 3 emissions under the lens of SSCM. If supply chains are to become more sustainable, focal enterprise coordination must be effective and leverage practices such as Scope 3 emissions metrics and measurement, data sharing, and green product development for all stakeholders. Full article

As ESG practices become increasingly embedded in global capital markets, their impact on firm financing costs remains an open question in emerging economies, where regulatory divergence and rating inconsistency complicate investor perceptions, particularly in China’s rapidly evolving financial environment. This study examines the impact of Environmental, Social, and Governance (ESG) performance on financing costs among Chinese non-financial listed firms, with a focus on the moderating roles of financial regulation and ESG rating divergence. Using a panel dataset of 4493 firms across 33,773 firm–year observations from 2011 to 2022, we employ a two-way fixed effects model, along with Propensity Score Matching and Difference-in-Differences (PSM-DID) techniques, to address endogeneity concerns and enhance causal inference. The findings reveal that improvements in ESG performance significantly reduce financing costs, substantially affecting debt relative to equity. Moreover, the cost-saving benefits of ESG are amplified in industries with stronger regulatory oversight, while high ESG rating divergence undermines these benefits by increasing uncertainty. These results highlight the importance of standardizing ESG rating systems and enhancing regulatory consistency. Such efforts can lower capital costs and improve financial access for firms, particularly in capital-intensive and environmentally sensitive sectors, offering actionable guidance for policymakers shaping disclosure frameworks and corporate managers optimizing ESG investment strategies. Full article

In recent decades, the intensification of extreme events, such as floods, earthquakes, and hydrogeological instability, together with the spread of pollutants harmful to health, has highlighted the vulnerability of territories and the need to direct urban policies towards sustainable strategies. The built assets and the real estate sector play a key role in this context; indeed, being among the first ones to be exposed to the effects of climate change, they serve as a crucial tool for the implementation of governance strategies that are more focused on environmental issues. However, the insufficient allocation of public resources to interventions to secure the territory has made it essential to involve private capital interested in combining the legitimate needs of performance with the “ethicality” of the investment. In light of the outlined framework, real estate managers are called upon to take into consideration the environmental risks associated with real estate investments and accurately represent them to investors, especially in the fundraising phase. The tools currently used for the analysis of such risks are based on their perception measured by the “risk premium” criterion, reconstructed on the basis of previous trends and the analyst’s expertise. The poor ability to justify the nature of the risk premium and the uncertainty about future scenario evolutions make this approach increasingly less valid. The present work, starting from the aspects of randomness of the risk premium criterion, aims at its evolution through the inclusion of environmental risk components (seismic, hydrogeological, and pollution). Full article

This study investigates the causal impact of stock price crash risk on the cost of equity (COE) in China’s segmented A- and B-share markets with an emphasis on ownership structures and market regimes. Employing a bootstrap panel Granger causality framework, Markov-switching dynamic regression, and panel threshold regression models, the analysis reveals that heightened crash risk significantly increases COE, with the effects being more pronounced for A-shares because of domestic investors’ heightened risk sensitivity. This relationship further intensifies in bull markets, where investor optimism amplifies downside risk perceptions. Ownership segmentation plays a critical role, as foreign investors in B-shares exhibit weaker reliance on firm-level valuation metrics, favoring broader risk-diversification strategies. These findings offer actionable insights into corporate risk management, investor decision making, and policy formulation in segmented and emerging equity markets. Full article

This study investigates the impact of Environmental, Social, and Governance (ESG) certifications on the performance of Class A office buildings within Madrid’s Central Business District (CBD). Employing a Multi-Criteria Decision Making (MCDM) methodology, the research evaluates 21 office properties, analyzing the influence of ESG certifications on key performance indicators, including green building certifications, valuation, market perception, and financial outcomes. The findings reveal that ESG-certified buildings demonstrate superior performance, commanding higher valuations, mitigating brown discounts, and achieving increased rental rates, thereby enhancing their investment attractiveness. These results underscore the importance of ESG certifications in the Spanish office market and provide valuable insights for investors, developers, and policymakers navigating the integration of sustainability and commercial real estate. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

In the conditions of modern market dynamics, corporate social responsibility (CSR) is increasingly evolving from a formal ethical principle into a powerful strategic instrument, key to achieving sustainable growth and long-term competitive advantage. Companies that integrate CSR into their business not only affirm the values of social responsibility, but also position themselves as reliable and ethically oriented actors, thereby winning the trust of investors, motivating employees and building a stable base of loyal consumers. Hence, the aim of this research is to determine, through empirical analysis, to what extent and in what way individual aspects of corporate social responsibility influence employee perception and satisfaction, as well as to develop a predictive model of their mutual connection. The specific hybrid SEM-ANN methodology in the CSR field was applied to obtain more precise results than standard data analysis methods, which fulfilled the literature gap in this research field. The detailed hypotheses designed were empirically tested using SEM methodology and indicated a positive association between the social and stakeholder aspects with employee satisfaction. These outcomes were confirmed by the results of the ANN models. The findings obtained are not only theoretical, but also have a useful application in the real business environment, which is reflected in the development of strategies that can serve as a road map for organizations in achieving employee satisfaction. This could lead to a change in organizational culture with an emphasis on ethical business and greater responsibility towards society and stakeholders. Full article

This study explores the intricate interconnections between greenwashing, environmental performance (ESG), firm-specific characteristics, board composition, firm age, size, leverage, carbon emissions (CO₂), and financial performance. By applying a combination of panel VAR/GMM estimation, robust least squares regression, and Granger causality tests, the research draws upon comprehensive data spanning from 2009 to 2022 sourced from the Chinese Research Data Services Platform (CNRDS), Bloomberg, and Refinitiv. The dataset comprises 312 listed Chinese firms, yielding a total of 5335 observations. The findings reveal that past return on equity acts as a reinforcing mechanism for both financial performance and ESG outcomes, as it positively affects subsequent returns and environmental engagement. However, its influence on firm size, board structure, and Tobin’s Q is statistically insignificant. Additionally, greenwashing demonstrates a dual character: while it reflects strong internal consistency, it also significantly shapes environmental outcomes and market perceptions. Firm size stands out as a pivotal determinant. It exhibits high persistence over time and plays a crucial role in shaping governance structures and capital allocation decisions. Moreover, board composition is positively associated with firm size. Leverage and return on assets show consistent temporal persistence and exert substantial influence on various firm attributes. Although leverage may contribute positively under favorable conditions, its overall impact on sustainability and governance practices appears limited. Higher carbon emissions are associated with increased ESG disclosures, whereas stronger ESG performance contributes to emission reduction and modestly enhances financial outcomes. Tobin’s Q also emerges as a critical factor, significantly influencing sustainability practices. This suggests that firms respond to investor expectations by improving their ESG performance. Results from the robust least squares regression underscore the dominant roles of firm size, Tobin’s Q, and leverage in driving financial performance. In contrast, ESG scores, CO₂ emissions, and greenwashing do not exhibit any statistically significant direct effects on financial performance. Granger causality tests confirm unidirectional relationships from financial performance to key structural variables such as size, leverage, firm age, and Tobin’s Q. A notable bidirectional causal link is observed between return on assets and return on equity. However, sustainability and governance-related variables show no causal impact on financial performance. Overall, the study acknowledges limitations and offers policy recommendations along with directions for future research. Full article

The acceleration of global climate change presents unprecedented challenges to market stability and sustainable social development. Understanding how market dynamics are impacted by perceptions of climate risk is essential to creating risk management plans that work. Current research frequently concentrates on static evaluations of how climate risk is perceived, ignoring its dynamic influence on clean energy markets and the intricate channels via which these risks spread. To examine the dynamic influence of climate risk perceptions on clean energy markets, this study builds a spillover network model. We determine the main risk transmission pathways and their temporal variations by looking at changes in market connection over time. Our results demonstrate that climate risk perceptions have a substantial direct and indirect impact on the volatility of clean energy markets. Specifically, the ‘Risk Concern Index (GCTC and GCPC) → Clean Energy Market Index → Climate Policy Uncertainty Index (CPU) → Risk Indices (GCTRI and GCPRI)’ pathway highlights how public and policymaker concerns about climate risk significantly influence market behavior and overall dynamics. Furthermore, the dynamic analysis demonstrates that market spillovers are significantly amplified by economic and geopolitical events, highlighting the necessity of taking external shocks into account when designing policies. This study offers fresh perspectives on how climate risk perception affects clean energy markets, serves as a useful resource for investors and policymakers, and encourages the creation of robust risk management plans and market mechanisms. Full article

The UK’s National Security and Investment Act 2021, which came into effect in January 2022, marks the establishment of a foreign investment security review mechanism unique to the UK. This article examines the Act’s text and identifies several key features of the mechanism, including its broad review scope, ease of activation, flexible standards, and instrumental tendencies. Applying a constructivist framework, this article argues that the establishment of this mechanism is primarily driven by a shift in the UK’s identity, reinforcing its alignment with the United States while positioning China as a “competitor”. The NSI Act’s broad scope and discretionary powers disproportionately impact Chinese investors, given their concentration in high-risk sectors and geopolitical tensions. In response, this article proposes that China can mitigate the potential negative impact of this mechanism on its investors by adopting both conceptual and normative strategies, contributing to the reconstruction of the UK’s perception of China within the broader social context. Full article