Search Results (196)

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article

This study examines the effects of the International Maritime Organization’s (IMO) market-based measures, with a particular focus on the greenhouse gas (GHG) levy and on the financial and operational performance of Korean shipping companies. The analysis estimates that these companies, which play a vital role in global trade, consume approximately 9211 kilotons of fuel annually and emit 28.5 million tons of carbon dioxide. Under the lowest proposed carbon tax scenario, the financial burden on these companies is estimated at approximately KRW 1.07 trillion, resulting in an 8.8% reduction in net profit, a 2.4% decrease in return on equity (ROE), and a 1.1% decline in return on assets (ROA). Conversely, under the highest carbon tax scenario, costs rise to KRW 4.89 trillion, leading to a significant 40.2% decrease in net profit, thereby posing a serious threat to the financial stability and competitiveness of these firms. These findings underscore the urgent need for strategic policy interventions to mitigate the financial impact of carbon taxation while promoting both environmental sustainability and economic resilience in the maritime sector. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

Microgrids and load shifting can improve resilience and lower costs for electricity customers. The costs to deploy each have decreased and helped accelerate their deployment in the U.S. and globally. However, previous research has focused minimally on the combined benefit or “stacked economic value” that these assets could provide jointly. This article evaluates the financial value when those assets are combined and optimized jointly. The methods are demonstrated for a U.S. government facility with an existing microgrid and building automation system, with optimizations that vary the percentage load shifted and the duration of time the load can be shifted. The economic benefits of load shifting are greater when combined with a microgrid and coordinated dispatch of loads and microgrid assets. The methods and case study results illustrate “stacked economic value” showing energy charge reductions are 56–252% greater and demand charge reductions are 96–226% greater when load shifting is combined with a microgrid as compared to load shifting without a microgrid. Increasing the amount and duration of load shifting improves the stacked economic value as more loads are scheduled coincident with on-site generation to offset or completely avoid utility purchases during peak pricing periods, an underlying behavior that enables stacked economic value and increased financial savings. The percentage reduction in demand charges is greater than energy charges—a generalizable finding—but the relative impact on utility expenditures is dependent on the utility tariff structure and composition of demand charges and energy charges in the utility bill. In this case study, demand charge reductions were four times greater than energy charge reductions, but the financial savings of demand charges are less due to their smaller proportion of utility charges. This suggests that the stacked economic value of microgrids and load control may be even more significant in locations with electricity tariffs that more heavily weight billing towards demand charges than energy charges. Full article

In this study, we examine banks’ sustainability orientations (SOs) in the Baltic region, focusing on how institutional, stakeholder, national culture, and leadership factors influence strategic alignment with the Sustainable Development Goals (SDGs). We assess how Baltic banks integrate sustainable development using a bibliometric review, financial performance analysis, Spearman’s rank correlation, and content analysis of sustainability-related disclosures for 2023, and interpret Hofstede’s cultural dimensions of the Baltic countries alongside these results. Our bibliometric review reveals limited research on SO and SD in banking, with a gradual annual increase of 14.8%. Our content analysis findings suggest that smaller banks are more broadly aligned with the SDGs; however, 36.4% of the largest banks in the region did not have a dedicated sustainability report a year before ESRS and CSRD requirements became mandatory. Notably, the reporting approach shows no statistically significant correlation with assets, size, global/local coverage, or the number of aligned SDGs. Furthermore, our content analysis findings reveal a persistent sustainability paradox: while economic and environmental goals are strategically prioritised, social SDGs are significantly underrepresented. We propose that this reflects a lack of demand for socially sustainable development rooted in regional contexts and national culture, which shape SO and organisational and leadership responses. Full article

Predicting and trading assets in the global financial market represents a complex challenge driven by the dynamic and volatile nature of the sector. This study proposes a day trading strategy that optimizes asset purchase and sale parameters using differential evolution. To this end, an innovative financial indicator was developed, and machine learning models were employed to improve returns. The work highlights the importance of optimizing training sets for machine learning algorithms based on probable asset behaviors (scenarios), which allows the development of a robust model for day trading. The empirical results demonstrate that the LSTM algorithm excelled, achieving approximately 98% higher returns and an 82% reduction in DrawDown compared to asset variation. The proposed indicator tracks asset fluctuation with comparable gains and exhibits lower variability in returns, offering a significant advantage in risk management. The strategy proves to be adaptable to periods of turbulence and economic changes, which is crucial in emerging and volatile markets. Full article

Exchange rate volatility poses significant challenges for emerging markets, influencing trade balances, inflation, and capital flows. South Africa’s Rand is particularly vulnerable to global risk sentiment, gold price fluctuations, and interest rate differentials, yet prior studies often analyse these factors in isolation. This study integrates them within an autoregressive distributed lag framework, using monthly data from 2005 to 2023 to capture both short-term fluctuations and long-term equilibrium effects. The findings confirm that higher global risk sentiment triggers immediate Rand depreciation, driven by capital outflows to safe-haven assets. Conversely, rising gold prices and favourable interest rate differentials stabilise the Rand, strengthening trade balances and attracting capital inflows. These results underscore the interconnected nature of global financial conditions and exchange rate movements. This study highlights the importance of economic diversification, foreign reserve accumulation, and proactive monetary policies in mitigating currency instability in emerging markets. Full article

This article examines the energy efficiency and climate impact of various heating methods commonly employed across industrial sectors. Fossil fuel combustion heat sources, which are predominantly employed for industrial heating, contribute significantly to atmospheric pollution and associated asset losses. The electrification of industrial heating has the potential to substantially reduce the total energy consumed in industrial heating processes and significantly mitigate the rate of global warming. Advances in electrical heating technologies are driven by enhanced energy conversion, compactness, and precision control capabilities, ensuring attractive financial payback periods for clean, energy-efficient equipment. These advancements stem from the use of improved performance materials, process optimization, and waste heat utilization practices, particularly at high temperatures. The technical challenges associated with large-scale, heavy-duty electric process heating are addressed through the novel innovations discussed in this article. Electrification and the corresponding energy efficiency improvements reduce the water consumed for industrial steam requirements. The article reviews new technologies that replace conventional process gas heaters and pressure boilers with efficient electric process gas heaters and instant steam generators, operating in the high kilowatt and megawatt power ranges with very high-temperature capabilities. Financial payback calculations for energy-optimized processes are illustrated with examples encompassing a range of comparative energy costs across various temperatures. The economics and implications of waste heat utilization are also examined in this article. Additionally, the role of futuristic, radical technical innovations is evaluated as a sustainable pathway that can significantly lower energy consumption without compromising performance objectives. The potential for a new paradigm of self-organization in processes and final usage objectives is briefly explored for sustainable innovations in thermal engineering and materials development. The policy implications and early adoption of large-scale, energy-efficient thermal electrification are discussed in the context of temperature segmentation for industrial-scale processes and climate-driven asset losses. Policy shifts towards incentivizing energy efficiency at the manufacturing level of heater use are recommended as a pathway for deep decarbonization. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article

This study investigates the relationship between disclosures related to Sustainable Development Goal 7 (SDG 7) and the financial profitability of Romanian commercial banks during the 2017–2023 period. Using an unbalanced panel dataset of 17 banks and applying fixed-effects regression models, the paper examines how transparency around energy-related sustainability practices influences various dimensions of bank profitability: recurring earning power (REP), loan yield (LY), return on assets (ROA), and return on equity (ROE). Macroeconomic energy indicators, such as the energy intensity level of primary energy (EnInt) and renewable energy consumption (REnC), are also controlled for. The findings indicate that SDG 7.1 disclosures are negatively associated with all profitability measures, except for LY, suggesting potential short-term trade-offs between sustainability transparency and financial outcomes. In contrast, SDG 7.2 disclosures positively impact REP, ROA, and ROE, underscoring the financial relevance of renewable energy financing. SDG 7.a disclosures show no significant relationship with profitability, indicating limited operational involvement in global energy cooperation. Additionally, higher energy intensity negatively affects REP and LY, supporting existing evidence that energy efficiency improves banking performance. These findings have implications for banking strategy, emphasizing the need to align sustainability disclosures with business priorities while recognizing the long-term benefits of green finance and energy efficiency. Full article

This study examines the impact of economic policy uncertainty (EPU) on agricultural land conversion. Using a newspaper-based index of EPU and a comprehensive panel dataset covering 270 prefecture-level cities in China, we estimate a city fixed effects model to explore this relationship. Our results indicate that a one-standard-deviation increase in EPU leads to a 22.2% increase in the conversion of agricultural land to urban residential, commercial, and industrial uses. This finding suggests that the surge in EPU triggered by the global financial crisis accounts for approximately 45% of the increase in agricultural land conversion. The adverse effect on agricultural land preservation mainly stems from intensified fiscal pressures and heightened demands on local governments to meet economic growth targets. To address potential endogeneity concerns, we employ the one-period lagged U.S. EPU index and its temporal variations as an instrument for China’s EPU, leveraging cross-country spillover effects. Our instrumental variable estimates confirm the validity of the land conversion effect and its underlying mechanisms. Furthermore, we find that the effects of EPU are particularly pronounced in cities located in non-eastern China and those that depend heavily on fixed asset investment for local economic development. Finally, our analysis of potential policy interventions to mitigate EPU-induced agricultural land loss suggests that strengthening market-oriented reforms and reducing province-level quotas on agricultural land conversion can effectively offset the impact of rising EPU. Full article

This study explores the relationship between firm-level carbon productivity (CRP), home country environmental performance (HCEP), and firm performance—both financial and international—in the global meat exporting industry. While prior research has examined these dynamics in manufacturing sectors, limited attention has been paid to the meat industry, which is both economically significant and environmentally intensive. Using a multiple case study approach, we analyze data from three leading meat-exporting firms—Agrosuper (Chile), BRF (Brazil), and Danish Crown (Denmark)—over the period 2020–2023. CRP is operationalized as the ratio of firm output to CO₂ emissions, while HCEP is measured by national emissions per million USD of GDP. Financial performance is assessed via return on assets (ROA), and international performance through export intensity. Our findings reveal a nuanced relationship between CRP and firm performance. Contrary to theoretical expectations, a higher CRP does not consistently translate into improved financial performance, suggesting potential trade-offs between sustainability investments and profitability. However, a positive association is observed between CRP and international performance, particularly in firms operating within environmentally advanced countries. These results highlight the importance of home country environmental contexts in shaping firms’ global competitiveness. This research contributes to the literature by introducing CRP as a firm-level metric in the meat industry and by emphasizing the moderating role of HCEP. The findings offer practical implications for policymakers and managers seeking to align environmental responsibility with economic and international performance goals. Full article

This study examines the performance and resilience of Islamic banks during the COVID-19 pandemic, a period marked by unprecedented global economic disruption. Drawing on empirical data and a comparative analysis with conventional banking institutions, the research evaluates key financial indicators—liquidity, profitability, asset quality, and capital adequacy—to assess how Islamic banks responded to the crisis. The unique principles of Islamic finance, including risk-sharing, asset-backed financing, and the prohibition of interest and speculative activities, provide a distinct framework for crisis response. By analyzing how these features influenced bank performance during the pandemic, the study offers valuable insights into the relative robustness of Islamic versus conventional banking models. The findings contribute to the academic discourse on financial stability and risk management, offering practical implications for policymakers, regulators, and stakeholders to strengthen financial systems against future global shocks. Full article