This study examines the impact of capital structure on firm value within the food manufacturing sector of South Africa, addressing a critical gap in the literature on emerging markets. Using a balanced panel dataset of eight listed firms from 2007 to 2018, the research utilizes panel regression models—Common Effect (CEM), Fixed Effect (FEM), and Random Effect (REM)—with the Hausman test indicating REM as the optimal choice. Key findings demonstrate that profitability (RA), debt-to-equity ratio (DE), and firm size (FS) significantly enhance stock prices at a 1% significance level. In contrast, liquidity (CR) negatively affects stock prices (10% significance), while asset growth (AG) shows no significant impact. These results challenge traditional capital structure theories, emphasizing that South African firms strategically use debt for tax advantages despite market volatility, a stark contrast to developed economies where liquidity is typically prioritized. The study highlights the contextual significance of macroeconomic factors, such as energy shortages and regulatory policies (e.g., Black Economic Empowerment), in influencing financing decisions. By bridging the gap between classical theories and emerging market dynamics, this research provides actionable insights for policymakers to encourage sustainable capital structures, for investors to reconsider the role of liquidity in volatile environments, and for the government to develop better policies to support businesses. This research is novel; it is among the first to investigate the link between firm value and capital structure specifically for food manufacturing companies in South Africa over 12 years. It is distinctive because it frames capital structure choices within the unique industrial and economic environment of South Africa, contributing a framework for optimizing firm value in similar emerging markets.

In today's investment climate, the integration of Environmental, Social, and Governance (ESG) factors into strategic decision-making is essential, particularly in industry performance analysis. The article employs Data Envelopment Analysis (DEA) to calculate and contrast ESG efficiency for a broad variety of industries represented in companies in the Dow Jones Industrial Average. Through adopting three other DEA methods—the Constant Returns to Scale (CCR) model and input- and output-oriented Banker, Charnes, and Cooper (BCC) models—we provide a comprehensive framework to analyze how ESG inputs are allocated across different industries to achieve stock price appreciation. The results have important variations in different sectors. For example, the Technology & Telecom, Financial Services, and Retail & Consumer Goods industries have efficiency scores calculated much higher using the input-oriented BCC approach (INBCC) compared to when the scores are derived from the CCR model. This indicates very efficient management of resources that is masked under the constant return assumption. In contrast, industries like Media and Entertainment have efficiency scores that are high across different models, while others like Aerospace and Defense perform better once, they change their priority to output maximization. The results show that the selection of DEA methodology has a strong impact on efficiency scores and that the impact differs by industry. These findings provide industry-specific benchmarks for corporate practitioners, investors, and policymakers in return for fostering sustainable practices and enhancing portfolio selection strategies.

In developing economies like Nigeria, where government expectations on firms intensify amid underdeveloped institutional frameworks, the performance implications of fiscal obligations and voluntary transparency remain poorly understood. This study investigates whether government expectations influence firm performance and whether non-mandatory disclosure moderates this relationship among 80 listed Nigerian firms from 2011 to 2023. Using panel data regression techniques—specifically fixed and random effects models, the study analyzes how fiscal pressure and voluntary environmental, social, and governance disclosures jointly shape firm performance. The findings reveal that higher government expectations are significantly and negatively associated with firm value, suggesting that increasing tax burdens diminish corporate performance. Contrary to theoretical assumptions, non-mandatory disclosure was also negatively associated with firm performance under fixed effects estimation, indicating that voluntary ESG transparency may be perceived as costly or symbolic rather than performance-enhancing in Nigeria’s capital market context. More critically, the interaction between government expectations and non-mandatory disclosure shows a significant negative moderating effect, implying that the combination of tax pressure and voluntary disclosure jointly exacerbates performance erosion rather than mitigating it. These results suggest that without institutional support, investor maturity, and stakeholder awareness, even well-intentioned disclosures may backfire. The study recommends that firms embed ESG practices into core business strategy rather than treat them as compliance rituals, and that policymakers harmonize tax and disclosure policies to avoid disincentivizing transparency. Investors are encouraged to evaluate the strategic substance behind disclosures rather than their volume alone. Future research should explore sector-specific dynamics, stakeholder interpretations of voluntary disclosures, and cross-country comparisons to uncover when and how ESG transparency translates into sustainable firm value under fiscal constraint.

This study examines the relationship between short-term and long-term stock returns of companies after initial public offering by considering financial variables and financial and ownership characteristics of companies on the Tehran Stock Exchange. The research sample includes 4560 companies that were publicly listed on the stock exchange in the period from 2013 to 2024, which constitute a total of 4560 company-years. Econometric methods and vector regression models have been used to test the hypotheses. First, the statistical description of the data has been discussed and then various tests including ADF and PP unit root tests to examine the stationarity of the data, Durbin-Watson test to examine autocorrelation, Chow test, F test and Hausman test have been used to select the appropriate model. The results of these tests show that the main hypothesis of the study is that there is a significant relationship between short-term and long-term stock returns of companies after initial offering is confirmed. Finally, the results of this study can be generalized with 95% confidence to the entire statistical population of the study, namely active investors in the Tehran Stock Exchange.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.