Search Results (4,274)

Understanding the relationship between green bond issuance and environmental performance is critical as governments, financial institutions, and investors seek to accelerate the transition toward a low-carbon economy. This study analyzes the relationship between green bond issuance and carbon intensity across 165 countries from 2015 to 2022. Two-way fixed-effects models reveal a negative and statistically significant association between green bond issuance and carbon intensity (GDP- and energy-based measures). Dynamic system GMM estimations confirm this relationship after accounting for persistence and endogeneity, with coefficients remaining negative and significant, while carbon intensity displays strong inertia (autoregressive coefficients: 0.864–0.928). Robustness checks—including the exclusion of the five largest issuers and the use of alternative dependent variables—sustain these findings, indicating a moderate, gradual impact of green bond markets on lowering carbon intensity. Full article

Against the backdrop of a new wave of scientific and technological revolution and industrial transformation, artificial intelligence has emerged as a pivotal technology for fostering new quality productive forces and advancing high-quality development, and is profoundly reshaping firms’ production organization and governance structures. Using data on Chinese A-share listed companies from 2016 to 2024, this study empirically examines the impact of AI on corporate internal control quality and its underlying mechanisms. The results indicate that AI significantly improves corporate internal control quality, mainly by enhancing firms’ human capital and reducing agency costs. Further heterogeneity analysis shows that the positive effect of AI on internal control quality is more pronounced among manufacturing firms, firms with higher levels of digital infrastructure, and firms with greater information transparency. From the perspective of internal corporate governance, this study extends the literature on the economic consequences of AI and provides empirical evidence on how AI, as embedded in a complex socio-technical system, empowers high-quality corporate development through institutional governance mechanisms. The findings also offer useful implications for governments seeking to refine AI-related policies and for firms aiming to promote the coordinated upgrading of intelligent transformation and internal control systems. Full article

Digital transformation is increasingly viewed as a strategic driver of operational efficiency, financial performance, and organisational resilience in rural transition economies. Existing research, however, largely assumes homogeneous digitalisation effects across firms while overlooking the structural conditions shaping integration efficiency. This study investigates the threshold relationship between supply chain integration and financial–economic performance using a threshold regression framework. The analysis is based on firm-level data from 80 agricultural, agritourism, and tourism-related firms operating in rural Northern Albania. Methodologically, the study combines Hansen’s threshold estimation with robust OLS and threshold logistic regression models, complemented by exploratory macro-level threshold analysis for Western Balkan economies. The findings reveal significant regime-dependent dynamics. Below the estimated socio-economic integration threshold, supply chain integration generates weak and statistically insignificant effects. Above the threshold, integration mechanisms produce substantially stronger financial and operational outcomes, indicating that digital transformation becomes economically productive primarily under sufficiently integrated organisational conditions. Additional diagnostics further show that highly integrated firms achieve superior coordination efficiency, resource allocation, and financial resilience. The study contributes to the literature by advancing a managerial-financial and coordination-based interpretation of digital transformation and its threshold performance effects in rural transition economies. Full article

This paper investigates the impact of Environmental, Social, and Governance (ESG) performance on the financial outcomes of 68 European and 60 U.S. banks over the period 2010–2022 using a Panel Smooth Transition Regression (PSTR) framework. Unlike traditional linear models, the PSTR approach captures the nonlinear, regime-dependent effects of ESG engagement on bank profitability, measured by ROA and ROE, and financial stability, measured by the Z-score. Our empirical findings reveal a critical ESG threshold in both regions, above which banks experience substantial improvements in profitability and resilience. Comparative analysis indicates that while ESG enhances stability slightly more in European banks, U.S. banks tend to achieve marginally higher profitability gains. Control variables, including bank size, capital adequacy, leverage, and macroeconomic conditions, also play a significant role in shaping performance. These results underscore the importance for banks to attain a minimum ESG maturity to fully realize the benefits of sustainable practices. The study provides valuable insights for bank managers, investors, and policymakers seeking to promote a sustainable and resilient banking sector across Europe and the United States. Full article

This study examines the impact of corporate environmental, social, and governance (ESG) practices on corporate stock prices, with a particular focus on the mediating role of board gender diversity (BGD). Using a dataset of 9543 firm-year observations from non-financial companies across 15 countries in the S&P 1200 global index between 2012 and 2020, the analysis evaluates ESG performance through the Refinitiv ESG Combined Score, which incorporates disclosures across ESG pillars and an overlay for ESG controversies. BGD is measured as the proportion of female directors on corporate boards, while stock prices are assessed using annual closing prices. The findings reveal a positive relationship between ESG performance and corporate stock prices, both at the aggregate level and across individual ESG pillars. Additionally, BGD is shown to enhance stock price performance and serves as a mediator in the ESG-stock price relationship. These results highlight the critical role of board diversity in amplifying the financial benefits of ESG practices. Further analysis suggests that the value relevance of ESG performance varies across institutional settings, with stronger effects observed in emerging/offshore markets and in the North American and European regions. The study offers important implications for companies, investors, and policymakers, emphasizing the need to integrate ESG strategies and promote gender diversity at the board level to enhance corporate valuation and long-term sustainability. Full article

We examine the measurement of country-level institutions in international accounting research. Despite a large body of literature linking national institutions to financial reporting outcomes, the construct validity and reliability of the institutional measures used in cross-country studies have rarely been assessed. We show that widely cited measures of investor protection, legal quality, and equity market development have been adopted without validity or reliability assessment and used inconsistently across published studies, with the same indicator labelled as a measure of different constructs in different papers. Using forty-eight candidate indicators from six international databases for seventy countries, we conduct an exploratory factor analysis followed by a confirmatory factor analysis and identify three latent constructs that pass conventional thresholds for convergent reliability and discriminant validity. The constructs are empirically distinct, indicating that measures routinely treated as interchangeable in published work measure different latent dimensions. We offer a validated three-factor measurement model that researchers can use to operationalise country-level institutions in future cross-country studies and discuss the implications for the interpretation of existing studies. Full article

This study investigates whether the March 2023 failure of Silicon Valley Bank (SVB) coincided with changes in market-risk exposure, abnormal returns, and return connectedness among five major Chinese financial institutions. Using daily data from January 2022 to December 2023, we apply CAPM and market-model regressions, structural-break tests, event-study methods, and generalized forecast-error variance decompositions. The revised design distinguishes 10 March from the first subsequent Chinese trading day, 13 March, and adds symmetric event windows, placebo tests, an alternative risk-free rate, return-series audits, significance tests, and VAR diagnostics. Full-sample Chow tests identify breaks for ICBC and CITIC Securities, but only ICBC remains significant within the ±60-day window, and none are significant within ±30 days. Only ICBC records a significant positive CAR over [−3, +3]. Overall connectedness falls from 55.53% to 47.98%, while CITIC Securities becomes the largest post-event net transmitter. The evidence therefore indicates selective repricing and reconfigured linkages rather than a system-wide effect, and does not support unique causal attribution to SVB. Full article

Purpose: Despite growing research on green fintech and sustainable finance individually, no systematic theoretical framework explains how AI-driven green fintech solutions can be adopted in banking for sustainable investment purposes. This paper addresses this demonstrated gap by developing the first bibliometrically grounded, TOE-based conceptual framework for AI-driven green fintech adoption in banking. Design/Methodology/Approach: A two-phase approach is employed. First, a bibliometric analysis of 79 Scopus-indexed documents (2020–2026) using bibliometrix in R provides quantitative evidence of the research gap through keyword co-occurrence networks, thematic mapping, and trend topic analysis. Second, building on this evidence, a conceptual framework integrating the Technology–Organization–Environment (TOE) framework with three mediating constructs, technological readiness, sustainability culture, and regulatory support is developed and five theoretical propositions are derived. Findings: The bibliometric analysis reveals an annual growth rate of 78.4% in the field and confirms that the TOE framework has never occupied the motor themes quadrant of the green fintech literature. The proposed framework theorizes three mediated pathways through which technological, organizational, and environmental conditions translate into improved sustainable investment outcomes including enhanced ESG transparency, increased green investment allocation, and SDG alignment. Practical Implications: The framework provides bank executives with three actionable intervention points: technological infrastructure investment, sustainability culture embedding, and regulatory engagement and offers policymakers evidence-based guidance for designing supportive green fintech adoption frameworks. Originality/Value: This study presents a conceptual framework that is, to the authors’ knowledge, the first to combine TOE theory, AI-driven green fintech, a banking context, an explicit three-mediator architecture (technological readiness, sustainability culture, regulatory support), and sustainable investment outcomes as the dependent variable, grounded in reproducible bibliometric evidence. Existing studies address subsets of these dimensions; none integrates all six simultaneously. Full article

Nigeria has pursued exchange rate unification as a deliberate macroeconomic policy objective since the Structural Adjustment Programme (SAP) of 1986, culminating in the full unification of June 2023. Despite the centrality of this policy to Nigeria’s economic governance, its poverty implications have received insufficient empirical examination. This study investigates the effect of exchange rate unification on poverty in Nigeria over the period 1986–2024, using the Autoregressive Distributed Lag (ARDL) bounds testing approach. Exchange rate unification is operationalised as the ratio of the official to the parallel market exchange rate, a continuous measure of the degree of rate convergence achieved at each point in time, which is mathematically the inverse of the conventional parallel market premium used to measure exchange rate distortion. While the parallel market premium measures the degree of exchange rate distortion, that is, how far apart the two rates are, the ERU ratio employed in this study measures the degree of exchange rate unification achieved, that is, how close the two rates are to convergence. Poverty is measured using the Multidimensional Poverty Index (MPI). The MPI is constructed using Principal Component Analysis (PCA) from per capita income, life expectancy at birth, agricultural value-added per worker, and household consumption expenditure per capita. The study finds that exchange rate unification significantly worsens poverty in both the short and long run, consistent with the sharp naira depreciation, inflationary pass-through, and deterioration in cost of living observed following Nigeria’s exchange rate reforms. GDP growth reduces poverty marginally in the long run, confirming a modest pro-poor growth effect. These findings establish that exchange rate unification, while necessary for long-run macroeconomic efficiency, imposes significant poverty costs that require deliberate complementary fiscal and social protection policies to mitigate. Full article

This study examines access to Indonesia’s subsidised credit programme, Kredit Usaha Rakyat (KUR), during the COVID-19 pandemic using signalling theory. Based on logit regression of survey data from 2361 micro and small enterprises (MSEs), the study analyses how borrower characteristics were associated with loan approval across the Super Micro, Micro, and Small KUR schemes. The results show that prior credit history was the strongest observed signal of creditworthiness during the crisis. Previous receipt of KUR was associated with a 5 percentage-point higher probability of loan approval. Scheme-level estimations indicate heterogeneous signalling mechanisms. Prior credit history was positively associated with access to Micro KUR (15 p.p) and negatively associated with access to KUR Super Mikro that targeted non-bankable SMEs, whereas access to Small KUR was more strongly associated with collateral-based signals, particularly land ownership. The findings further indicate that borrowers lacking prior credit experience or formal assets were less likely to obtain KUR during the pandemic, suggesting that crisis-period lending created a “signaling trap” that stabilised liquidity while perpetuating exclusion. The study recommends reforms to KUR that incorporate hybrid risk assessment mechanisms, capacity-building, decentralised guarantees, and pathways to formalisation. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

This study examines the association between exchange rate volatility, the use of externally-oriented and strategically-focused management accounting practices (i.e., Strategic Management Accounting, SMA), and competitive advantage among listed manufacturing firms in an emerging economy. Drawing on contingency theory and the resource-based view (RBV), the study investigates whether exchange rate volatility influences SMA usage and whether SMA usage enhances firms’ competitive advantage under volatile foreign exchange conditions. Panel regression techniques with firm and year fixed effects were employed using data obtained from the annual reports of 56 listed manufacturing firms over the period 2015–2024. The findings reveal a positive and statistically significant association between exchange rate volatility and SMA usage, including both customer accounting and competitor accounting practices, suggesting that firms intensify externally oriented strategic accounting activities in response to foreign exchange uncertainty. The study further finds that SMA usage intensity positively moderates the relationship between exchange rate volatility and competitive advantage, as firms with more intensive SMA usage exhibit stronger relative competitive advantage in return on assets and market valuation under volatile exchange rate conditions. The findings support contingency theory by demonstrating that firms adapt their management accounting systems to environmental uncertainty, while the resource-based view explains how SMA functions as a strategic organisational capability that enhances competitive resilience. Robustness tests using entropy balancing, Driscoll–Kraay estimations, and alternative measures of exchange rate volatility confirm the stability and reliability of the results. Overall, the study contributes to the strategic management accounting (SMA) and international business literature by providing evidence that exchange rate volatility is associated with greater SMA usage and that more intensive SMA usage is, in turn, associated with stronger competitive resilience under conditions of economic uncertainty. The study also provides important practical implications for managers and policymakers in contexts where persistent exchange rate instability continues to shape business performance and strategic decision-making. Full article

This article discusses the influence of carbon emissions, both direct and indirect, on firm value. It also takes into account the moderating variable of green investment and whether governance mechanisms—like external assurance of greenhouse gas (GHG) emissions and CSR/sustainability committees—affect these relationships. The hypotheses of the study were developed using the lens of the natural-resource-based view, legitimacy theory, and agency theory. This paper leverages panel data spanning 2017 to 2024 on firms in the UK FTSE 350 to examine the moderating role of green investment on the linkage between GHG emissions and firm value. We then conduct sub-sample analyses for firms with and without externally verified GHG disclosures and CSR/sustainability committees, respectively. Firm value is captured using enterprise value, shareholder value, and the price-to-book ratio as alternative proxies for robustness. The results reveal that GHG emissions have a significant negative impact on firm value, while green investment mitigates this adverse effect. This impact is driven by both Scope 1 and Scope 2 emissions. However, green investments are more likely to be interpreted as genuine, durable, and value-creating when (a) the firm’s emissions data are externally verified and (b) an active CSR/sustainability committee guides and monitors implementation. This study adds to the environmental accounting and corporate governance literature by providing empirical evidence that external assurance and internal sustainability oversight strengthen the relationship between environmental responsibility and firm value creation. Full article

This paper examines whether quantile-based connectedness measures contain useful information for portfolio risk management. Using U.S. sector equity data, we estimate connectedness measures within a quantile vector autoregression framework and construct portfolios based on the cross-sectional distribution of net connectedness. In particular, sectors identified as extreme shock transmitters receive lower portfolio weights. Our results reveal substantial asymmetries across quantiles. Portfolios constructed using lower-tail connectedness measures exhibit smaller maximum drawdowns and lower expected shortfall relative to both equal-weight benchmarks and portfolios based on upper-tail connectedness. By contrast, median connectedness measures tend to provide more stable overall portfolio performance and lower turnover. The findings also suggest that the informational content of connectedness depends critically on the quantile considered. Lower-tail connectedness becomes particularly informative during crisis periods, suggesting that downside spillovers play an important role in portfolio resilience and systemic risk transmission. Overall, the results demonstrate that quantile connectedness measures provide economically meaningful information for downside risk management and offer a simple and transparent framework for incorporating systemic risk into portfolio construction. Full article

Reliable accounting information is increasingly important for fiscal transparency and the assessment of sustainability-related public-finance issues. This study examines how harmonised Environmental Protection Expenditure Accounts (EPEAss) can support comparative analysis and preliminary risk screening in the EU public sector. Using EurostatEPEAsA, GDP and population data, the study applies a descriptive comparative design to analyse EU-level trends for 2018–2022, 2022 public-sector expenditure per capita across all 27 Member States, institutional-sector composition and CEPA-based functional allocation. Nominal EU environmental protection expenditure increased by 17.5% between 2018 and 2022, while its share of GDP remained close to 1.9%. Public-sector expenditure per capita varied substantially across Member States, with a median of EUR 314 and a coefficient of variation of 72.8%. Wastewater and waste management accounted for 69.7% of EU environmental protection investment. These patterns should be interpreted in light of accounting scope, institutional arrangements and infrastructure needs rather than as measures of environmental performance or fiscal risk. EPEA can enhance comparability, auditability and functional interpretation, but direct risk assessment requires additional evidence on liabilities, debt, asset condition, regulatory obligations and environmental outcomes. Full article