This study examines how environmental, social, and governance (ESG) occurrences relate to firm performance and how these relationships depend on firms’ investments in brand capital. Using firm-level data spanning more than two decades, we analyze the effects of positive and negative ESG events on market-based (sales) and accounting-based (return on assets; ROA) performance for firms with and without brand capital investment (BCI). Using panel data on U.S. firms from 1995 to 2019, we compare firms that invest in brand capital through advertising with firms that do not. The results reveal an interesting asymmetric pattern. Specifically, BCI firms experience greater sales gains following positive ESG occurrences but incur significantly larger losses following negative ESG events. Interestingly, non-BCI firms benefit less from positive ESG activities but face smaller penalties from negative ESG occurrences. This study contributes to the marketing literature by examining brand capital investment and how ESG activities translate into performance gains versus when they impose performance costs for firms.

This paper examines the impact of fiscal policy responses on unemployment across EU countries from 2019 to 2024, a period marked by the COVID-19 pandemic as a shock event. A detailed monthly panel data set is used in this study, employing a fixed-effects estimation model with government spending, revenue, and debt as core variables, along with the COVID-19 dummy as a control variable. The findings reveal a strong association between government spending and revenue in reducing unemployment, aligned with countercyclical fiscal policy support. Conversely, increasing government debt is strongly linked to higher unemployment, indicating a risk of excessive borrowing that could hinder future labor market recovery. Moreover, uncertain external economic conditions, such as the COVID-19 pandemic, have further intensified labor market distortions. Finally, the results highlight that fiscal policies can effectively mitigate unemployment in the short term; however, excessive debt may pose challenges to long-term fiscal sustainability. This study underscores the importance of well-structured and timely coordinated fiscal policy frameworks that promote employment stabilization, while ensuring long-term debt sustainability.

This Special Issue, entitled “Financial Reporting and Auditing,” presents a collection of contributions that reflect a dynamic and rapidly shifting landscape in which financial information, managerial judgment, regulatory expectations, and technological innovation intersect in increasingly complex ways [...]

The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. Although Artificial Intelligence (AI) is widely promoted as a lever for profitability, the mechanisms through which AI adoption translate into financial outcomes remain insufficiently specified in the oil and gas literature. Grounded in the Resource-Based View and Technology Adoption Theory, this study combines bibliometric mapping of 201 Scopus-indexed publications (2010–2025) with a focused comparative case analysis of important players (BP and Shell), based on publicly reported operational and financial indicators (e.g., operating cost, uptime-related evidence, and return on average capital employed—ROACE). Keyword co-occurrence analysis identifies five thematic clusters showing that efficiency-oriented AI use cases (optimization, automation, predictive maintenance, and digital twins) dominate the research landscape. A thematic synthesis of five highly cited studies further indicates that AI-enabled operational improvements are most consistently linked to measurable cost, productivity, or revenue effects. Case evidence suggests that large-scale predictive maintenance and digital twin programs can support capital efficiency by reducing unplanned downtime and structural costs, contributing to more resilient ROACE trajectories amid price swings. Overall, the findings support a conceptual pathway in which operational efficiency is a primary channel through which AI can create financial value, while underscoring the need for future firm-level empirical mediation tests using standardized KPIs.

This study investigates the return and volatility transmissions between petroleum prices and stock sector indices of 7 net petroleum-exporting and 19 net petroleum-importing countries over the period from January 2005 to September 2018. Given that indices representing sectors of most considered countries are not available, a unique approach is implemented to manually construct sector indices using daily data of 5768 stocks listed in 10 sectors. The VAR-GARCH model is applied that allows to capture bilateral volatility interactions. Furthermore, the estimates of the model are employed to analyse optimal portfolio holdings and hedge ratios. The findings reveal significant volatility transmissions between petroleum prices and stock sector indices of exporters and importers. However, the direction and magnitude of spillover effects are country- and sector-specific. The optimal portfolio weights and hedge ratios indicate that sector indices of Saudi Arabia (net exporter) and China (net importer) offer better opportunities with respect to hedging petroleum price risks.

The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.

While domestic green finance is widely recognized for its role in fostering green innovation and supporting climate change mitigation, the impact of international green finance (IGF) remains critical, particularly for developing economies where external finance inflows can catalyse transitions toward low-carbon development. This study investigates the long-run and short-run effects of IGF on green innovation and further examines the influence of green innovation on carbon dioxide (CO2) emissions across a panel of 76 developing countries from 2000 to 2019. Using second-generation panel cointegration and the vector error correction mechanism, our findings reveal a nonlinear long-run relationship between IGF and total innovation, indicating that IGF must exceed a threshold before significantly boosting total innovation in developing economies. We also identify an inverted U-shaped relationship between IGF and green innovation, in which the positive effects of IGF diminish beyond a certain point. Crucially, IGF emerges as a significant driver of CO2 emissions reduction in both the short- and long-run. While total innovation is associated with increased emissions over the long term, green innovation contributes to a substantial and sustained decrease in CO2 emissions. These results emphasize the need to design targeted policies that prioritize green innovation and scale up IGF to support sustainable growth in developing countries.

Exchange rate dynamics in OECD economies have been increasingly shaped by geopolitical tensions and systemic crises. Between 2010 and 2025, a sequence of major events including the European sovereign debt crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict has amplified uncertainty and volatility in global financial markets. Using a Bayesian Time-Varying Parameter Vector Autoregression (TVP-VAR) model, this analysis investigates how geopolitical shocks are transmitted to exchange rate movements against the US dollar, capturing structural breaks, stochastic volatility, and heterogeneous time-varying relationships across countries. The empirical evidence reveals that exchange rates respond significantly but asymmetrically to geopolitical shocks, with more pronounced effects during periods of global turmoil and weaker reactions in stable phases. Furthermore, the sensitivity of exchange rates to geopolitical risk differs across economies, depending on institutional quality, trade exposure, and macroeconomic resilience. These findings highlight important asymmetries in the transmission of geopolitical uncertainty and underscore the heterogeneity of policy responses among advanced economies. From a practical perspective, the results provide valuable guidance for policymakers and international investors seeking to integrate geopolitical risk into monetary, fiscal, and risk management frameworks.

Leveraged exchange traded funds (LETFs) magnifying popular index daily returns by up to ±3.0× are often assumed to belong in the domain of sophisticated traders with short-term horizons. This study shows why the standard method used by LETF providers to inform investors of long-run expected returns relative to an underlying index produces estimates that significantly deviate from statistically correct returns given a particular index return and standard deviation. Current methods underestimate LETF expected returns by over one hundred percentage points annually for bullish LETFs during high-return/high-volatility environments and equally overstate bearish LETFs’ performance during negative-return/low-volatility environments. These errors are magnified with higher leverage. The statistically correct method is also applied to monthly and quarterly calendar LETFs, showing they outperform daily LETFs in average- to high-volatility environments while daily LETFs tend to outperform in high-return, low-volatility environments. Results have implications for portfolio management, fund providers, regulators, and investors using LETFs for longer-term horizons while challenging the idea that LETFs are purely short-term trading vehicles.

This study examines the determinants of food price inflation in Morocco using a comprehensive econometric framework based on an Autoregressive Distributed Lag (ARDL) model. Relying on monthly data and controlling for major structural shocks, the analysis captures both the short-run dynamics and long-run equilibrium relationships between food prices and key macroeconomic, external, and climatic variables. The estimation results reveal strong inflation inertia, indicating that past food prices are the most significant driver of current price changes. External cost variables, including the nominal effective exchange rate, world oil prices, and international cereal prices, are mostly insignificant in the short run, suggesting a muted and delayed pass-through. Import volumes exert a marginal but lagged effect, while rainfall emerges as a consistent determinant, highlighting Morocco’s structural vulnerability to climatic variability. The error-correction term is negative and significant, confirming the existence of a stable long-run relationship. Long-run estimates show that oil prices and precipitation remain relevant drivers of food price dynamics, whereas the exchange rate appears largely neutral, reflecting the impact of subsidies, managed exchange rate arrangements, and domestic supply-chain characteristics. Nonlinear NARDL estimations provide no evidence of asymmetric exchange rate pass-through. The findings underscore some policy recommendations to enhance agricultural resilience, strengthen climate adaptation, and improve supply-chain efficiency for food price stability.