Search Results (710)

This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures. Full article

The global landscape of sustainability challenges has become increasingly complex, characterized by varying regulatory frameworks and market maturity across different nations. The financial significance of environmental, social, and governance (ESG) factors is influenced by industry and firm-specific attributes. Therefore, this study employs an integrated decision support framework that combines grey relational analysis (GRA) models including Deng’s GRA, absolute GRA, and a second synthetic grey relational analysis (SSGRA) with firm-level panel regressions to compare ESG and financial performance linkages across 11 Middle East and Africa (MEA) countries and industrial sectors. Furthermore, the study utilized a sensitivity analysis to check the robustness of SSGRG. Results indicate considerable variability in the relationships between ESG and financial performance across the region. The economies of the Gulf Cooperation Council (GCC) showed the most robust positive relationship between ESG factors and financial performance based on SSGRG, with Kuwait (0.82), Qatar (0.81), and Saudi Arabia (0.80) predominantly influenced by the social and governance dimensions. Conversely, a weak correlation was demonstrated in Egypt (0.54), Nigeria (0.53), and Kenya (0.56). Moreover, financials, communication services, and materials sectors exhibit the greatest integration of ESG factors into financial performance, with composite SSGRG values ranging from 0.75 to 0.78. In contrast, the information technology and energy sectors demonstrate weak association, with composite SSGRG values falling below 0.60. Furthermore, a conservative maximin analysis reveals that corporate governance in Kenya and environmental performance in Oman are identified as the weakest relationship at the country level, while governance in the information technology and energy sectors, environmental management in real estate, and social performance in consumer discretionary sectors are highlighted as weak connections. This study addresses a gap in the literature by developing a novel decision-support framework, providing fresh empirical evidence from emerging markets, and offering theoretical insights into the into influence of stakeholder and institutional factors on ESG value creation. This study provides implications for investors, corporate managers, and policymakers on sustainable finance in emerging markets and presents a decision-making framework that emphasizes ESG initiatives to enhance financial performance. Full article

This study employs a sample of 25 active and 22 passive AI ETFs to examine several issues surrounding their performance, risk, pricing efficiency, and persistence in pricing discrepancies and their impact on ETFs’ performance combined with the respective impact of intraday volatility. The relationship between AI ETFs’ performance and market factors concerning size, value, profitability, investment and momentum is evaluated too. The results indicate that the passive AI ETFs have outperformed active ones over their entire trade history, without, however, shouldering their investors with materially higher volatility. Moreover, both AI ETF groups trade at a persistent premium to their NAV. The concurrent premium positively affects return, while the one-period lagged premium is negatively related to return. In addition, a negative relationship between return and concurrent intraday volatility and a positive (but less strong) relationship between return and one-period lagged intraday volatility are found. Moreover, the majority of AI ETFs do not generate significant alphas. Finally, market factors effectively explain the performance of AI ETFs. Full article

Firms’ attention to physical climate risks arising from extreme weather events and long-term climate change has become a crucial strategic orientation that shapes how firms perceive, interpret, and respond to climate-related uncertainties. However, despite growing scholarly interest in climate risk and corporate sustainability, limited research has systematically examined whether and how firms’ climate risk attention (CRA) translates into value chain upgrading (VCU). Using panel data on Chinese A-share listed companies from 2008 to 2024, this study investigates the relationship between CRA and VCU. The empirical results show that CRA significantly promotes firms’ VCU, and that this effect is more evident among firms in climate-sensitive industries. Mechanism analyses further reveal that CRA facilitates firms’ embedding into green R&D networks, green investor networks, and green governance networks, which in turn enhance VCU. Further analyses indicate that green governance capability, green subsidies, and green outcome transformation ability strengthen the positive effect of CRA on VCU. These findings deepen the understanding of how climate-related strategic attention shapes firms’ sustainable transformation and provide evidence that proactive attention to physical climate risks not only improves environmental governance, but also serves as an important catalyst for firms to move toward higher value-added segments of the value chain. Full article

Current accounting standards do not allow recognition of intangible assets for indigenously created properties, resulting in a discrepancy between the book value and market value of firms operating within digital economies, where investments like cybersecurity and data governance are grossed up immediately on the statement of financial position as they are considered to be expensed under IFRS. This paper investigates whether voluntary Digital Reputation Risk Disclosure (DRRD) rectifies this valuation gap for the non-financial firms listed on the Saudi Exchange. Based on an automated bilingual dictionary-based textual analysis of 891 corporate documents and a two-step System GMM estimator run on an unbalanced panel of 619 firm-year observations from a sample of 132 firms for the period 2020–2024, we show that DRRD is statistically significantly negatively related to firm value at conventional levels, implying that investors perceive such disclosures as indications of higher risk exposure rather than stronger governance capabilities. While statistically insignificant, the moderating effect of firm size shows that negative valuation effects are concentrated on large firms according to sub-sample analysis. These findings are confirmed across several alternative specifications in the robustness checks. The findings demonstrate that voluntary digital risk disclosure, in the absence of standards-based frameworks, is not effective at bridging this valuation gap, and may instead activate functional fixation among investors. These findings highlight the importance of IASB’s standardization agenda regarding intangible assets and present relevant empirical data for developing capital markets. Full article

Corporate sustainability programs in agri-food systems have expanded dramatically, yet emissions, deforestation, hunger, and land concentration intensify. Why does corporate sustainability systematically fail to deliver transformation? This paper applies Commonist Value Theory (CVT) to show that this failure is structural, not contingent. CVT distinguishes between True Value, the life-supporting qualities that sustain human and more-than-human flourishing, and Fetish Value, abstracted forms oriented toward capital accumulation. CVT traces how corporate sustainability programs convert the former into the latter through ‘decommonization’: the perversion and enclosure of shared life-supporting relations. Drawing on investor analyses, carbon market assessments, and critical scholarship, this paper demonstrates that corporate sustainability programs function as civilizing meta-mechanisms. Rather than transforming food systems, they stabilize existing arrangements by absorbing critique and redirecting transformative energies into regime-compatible forms. Farmers’ knowledge is captured as proprietary data, living ecosystems are reduced to tradeable metrics, collaborative relationships are fragmented by corporate platforms, and movements for genuine alternatives are channeled into supply chain optimization. The analysis concludes that corporate sustainability cannot deliver genuine transformation because its structural function is to stabilize rather than supersede the current value regime. Genuine transformation requires commons-based alternatives from below and political–legislative shifts from above that structurally constrain decommonization. Full article

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

Small-scale prosumer installations are playing an increasingly important role in the Polish electricity sector. These primarily include photovoltaic systems and heat pumps installed for internal use. Noticeable losses for individual investors, generated by the power flow mechanism during peak production hours (connection to the grid) and peak demand (drawback from the grid), as well as the issue of fluctuating grid capacity and the observed redispatch procedures for photovoltaic installations, are driving increased interest in equipping home energy installations with energy storage systems, strengthening the aspect of sustainable energy development in this dimension. The impact of energy storage on investment motivation and the actual effects of incorporating it into home energy installations have not yet been sufficiently researched, particularly in Poland. Therefore, the aim of the study was to assess the use of energy storage in home installations as a socio-technical direction of power development at the micro level, in light of the constantly increasing energy demand observed worldwide in line with the challenges of sustainable development. The results of a survey of 206 individual users of power installations equipped with energy storage systems in Poland were used for this study. The research was qualitative and quantitative in nature, with descriptive statistics and a logistic regression model used in the in-depth section, and the findings were supported by PQStat software. The research revealed that the selection of energy storage systems in home power grids is related to the potential for prosumer optimization. On the other hand, they are seen as a path towards increasing energy security at the household level. Supporting this direction of installation development at the micro level is a justified concept for the development of green energy in Poland, socially and environmentally beneficial as well as economically justified, i.e., in line with the trend of sustainable development. The information campaign, combined with financial support for this type of investment, should be continued and strengthened in Poland. Full article

Strengthening transparency has emerged as a pivotal issue in promoting the responsible development of artificial intelligence (AI). As the prevailing framework for corporate information disclosure, Environmental, Social, and Governance (ESG) reporting shares an inherent synergy with AI governance; both are rooted in the pursuit of sustainable development and the disclosure of specific matters to investors and broader stakeholders. This study analyzes the status of artificial intelligence (AI) information disclosure in the ESG (Environmental, Social, and Governance) reports of listed companies across the United States, Europe, and China, finding that: (1) ESG reports have emerged as a primary channel for business organizations to disclose AI-related information; (2) significant disparities exist in disclosure levels across four key AI-related domains—development, application, manufacturing, and consumption; and (3) disclosure density varies considerably across E, S, and G dimensions, with the Governance (G) pillar exhibiting the most comprehensive information. Based on an empirical analysis of the ESG-AI disclosure framework, this study proposes an optimization scheme for ESG-AI reporting, clearly defining mandatory ESG-AI disclosure obligations for listed companies and employing the “comply or explain” mechanism to balance corporate transparency with operational efficiency while adhering to the “Double Materiality” principle by disclosing model training energy consumption and ecological impacts under Environmental (E) matters, addressing employment, employee training, marketing labeling, and customer privacy under Social (S) matters, and elaborating on corporate AI strategies, risk management protocols, and governance policies under Governance (G) matters. Regarding procedural safeguards, taking China as a case study, centralized disclosure could be implemented through the National Enterprise Credit Information Publicity System, complemented by an assurance system for listed company reports to enhance the accessibility and accuracy of information disclosure. Full article

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform. Full article

This study examines the impact of the Russia–Ukraine war on crude oil tail risk using the Conditional Autoregressive Value at Risk (CAViaR) framework. We analyzed 2364 daily observations of West Texas Intermediate (WTI) crude oil futures spanning 1 January 2015 to 11 December 2023, thereby capturing both the pre-war period and the conflict regime. To operationalize the geopolitical shock, we identify four theoretically grounded event dates (21 February, 24 February, 11 May, and 15 June 2022) associated with military escalation and energy-supply disruptions, and incorporate them as exogenous dummy variables. Methodologically, we implement a two-step approach. First, we estimate 1-day Value at Risk (VaR) at the 5% and 1% levels using four alternative CAViaR specifications (Adaptive, Symmetric, Asymmetric, and Indirect GARCH(1,1)) within a rolling-window framework to capture the dynamic evolution of tail risk. Second, we regress the resulting VaR series on geopolitical-event indicators to quantify the marginal effect of war-related developments on downside risk. The empirical results show tail risk increases in oil-market after the most important geopolitical events in all the model specifications across the market characteristics. The Indirect GARCH(1,1) CAViaR model exhibited the highest sensitivity, producing event coefficients of 0.795 (5% VaR) and 0.710 (1% VaR), both significant at the 1% level. Our adaptive specification has magnitudes that are even higher at the extreme tail (2.002 at 1% VaR), further supporting increased vulnerability during periods of escalation in conflict. Evidence from the asymmetric model would also indicate stronger market response to unfavorable news, in line with loss-sensitive investor behavior. In sum, the outcomes indicate that the Russia–Ukraine war considerably elevated the downside risk of crude oil markets and that geopolitical events have economically and statistically significant effects on the tail dynamics. Incorporating event-based geopolitical indicators in the framework of CAViaR, contributes to the literature in energy-market risk modeling and applies practical information to investors, risk managers, and policymakers operating under a dynamic environment characterized by geopolitical uncertainty. Full article

This study investigates the impact of board attributes (board size, board independence, gender diversity, and nationality diversity) on corporate board diligence through employing panel data of listed firms in Muscat Securities Market from 2014 to 2024. Through the application of multiple regression analysis, the paper determines predictors for board diligence and offers an agency theory-based and resource dependence theory-based perspective on this construct. The findings reveal positive relations between board independence and board diligence, which suggests that the independent director has monitoring function. On the other hand, board size and nationality diversity are negatively related to diligence levels indicating a lack of coordination and communication. However, board gender diversity does not seem statistically related to board diligence. Several robustness tests, such as lagged independent variables, fixed industry effects, alternative estimation techniques, and instrumental variable approach, support the validity of our findings. This research helps investors and policymakers to better understand the extent to which board structure is related to meeting activity and director engagement in emerging markets. The study contributes to the literature on board diligence in emerging markets and evidence the impact of gender and nationality diversity on corporate board performance in Oman. Full article

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study ($N=25$, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is $\beta =0.0036$ ($p=0.495$) and the Alphabet Rank coefficient is $\beta =-0.0027$ ($p=0.642$), with the results robust to all tested parameterizations ($\lambda \in [0.05,0.20]$; $p>0.50$ throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. Full article

Assessing the investment attractiveness of companies is essential for effective capital allocation under conditions of uncertainty and heterogeneous risk–return profiles. Investors typically face multiple financing alternatives, making comparative evaluation impossible without robust and specialized assessment methodologies. This study proposes a refined conceptual model for assessing the investment attractiveness of production companies, with a specific focus on the manufacturing sector of Kazakhstan. The research is based on a modeling-oriented methodological framework that integrates a modified discounted cash flow (DCF) approach with elements of environmental controlling. The proposed model incorporates sector-specific characteristics, including resource utilization patterns, regulatory requirements and the potential “green” premium observed in capital markets. To capture investment-related uncertainty and risk, the study employs material flow cost accounting, scenario-based modeling and probabilistic decision tree analysis. Particular attention is given to improving the determination of the discount rate, recognizing its critical influence on present value-based investment assessments. The model accounts for macroeconomic and sectoral factors specific to Kazakhstan’s production industry and offers alternative discount rate estimation scenarios under different initial conditions. The study contributes to the literature on investment attractiveness assessment by integrating financial, environmental and risk dimensions into a unified framework. The proposed model enhances transparency in investment decision-making and provides new insights into investment evaluation practices in emerging industrial economies. Full article

This paper explores how climate risks affect the spillover between cryptocurrency and precious metals markets, given the increased interplay between climate-related threats and financial markets. The dynamic spillovers of the cryptocurrency and precious metals markets are analyzed initially by the TVP-VAR-DY model. Subsequently, it investigates how transition risk and physical risk affect these spillovers using quantile Granger causality (QGC), quantile–quantile regression (QQR), and wavelet quantile regression (WQR), with a particular focus on the differences in the results across the pre- and during-COVID-19 periods. The results show that climate risks significantly affect the spillovers in the cryptocurrency and precious metals markets, and these effects are heterogeneous in nature. Specifically, it is found that, under normal market conditions, both TRI and PRI have the effect of strengthening the spillovers. However, in extreme market states, their influences weaken because of investor distraction. In addition, at extremely low levels of climate risk, both TRI and PRI tend to intensify spillovers, and the impact of PRI is more pronounced. Moreover, during the COVID-19 crisis, climate risks seemed to have a limited effect in the short run, while they were more sustainable in the long run. These findings offer crucial implications for mitigating climate-related systemic risks and fostering a resilient, sustainable financial ecosystem amidst global decarbonization efforts. Full article