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.

The Indian Pharmaceutical Industry has gained tremendous momentum during the last few decades. Considering its importance both in the social sector and in the economy of our country a study has been endeavored to analyze the nature and movement of Return on Equity (ROE) of 9 selected companies listed in National Stock Exchange (NSE) in India during a period of 15 years from 2006-07 to 2020-21. This analysis has been conducted using DuPont. Step Regression has been used to measure to explain ROE by its predictors such as Operating Profit Margin (OPM), Interest Expense Ratio (IER), Assets Turnover Ratio (ATR), Tax Retention Ratio (TRR) and Equity Multiplier (EM). Study shows a substantial relationship between ROE and OPM in case of large cap companies. But most of the mid and small cap companies have shown a different relationship where other predictors such as ATR, TRR and EM are proved to be significant to explain ROE.

This study investigates the effects of stakeholder relations quality, and the social and moral capital of CEOs on the board processes and performance of socially and environmentally responsible companies. Data is collected from 40 companies listed on the Sri-Kehati Index of the Indonesia Stock Exchange and evaluated under the PROPER program by the Ministry of Environment Indonesia. Using GeSCA for analysis, results show that CEO’s relational and moral capital significantly impact board processes and performance. The quality of stakeholder relationships is more pronounced at the individual CEO level than the company level. Further analysis indicates that CEO’s relational capital strengthens the relationship between their moral capital and stakeholder relationship quality at the company level. Additionally, while the CEO’s relational capital significantly affects both board processes and performance, the CEO’s moral capital and the company’s responsible status only significantly impact board performance. Mediation analysis reveals that the CEO’s relational capital significantly mediates the relationship between the CEO’s moral capital and the company’s responsible status, affecting board processes. The findings underscore the importance of CEO’s relational capital at both individual and company levels for socially and environmentally responsible companies.

This article introduces an innovative fuzzy-based approach for developing a comprehensive portfolio optimization model that effectively accounts for inherent uncertainty while incorporating the investor's unique perspective on the dynamic stock market. The multi-objective optimization framework employs Conditional Drawdown at Risk to enhance investor flexibility in determining risk tolerance and optimal investment strategies tailored to specific needs. The research is notable for its pioneering use of intelligent methods to systematically collect valuable data from the Tehran Stock Exchange under fuzzy uncertainty. It incorporates important constraints such as cardinality and ceiling and floor limits for each investment period, allowing for a detailed analysis of various stock market scenarios and potential future outcomes. A case study is conducted with 25 diverse assets from the top five industry groups based on profit per share, from which five shares are thoughtfully selected to effectively demonstrate the model's unique effectiveness. The analysis rigorously assesses the model's performance in real-world conditions, highlighting the importance of accurately understanding the current stock market outlook and trends. To validate the model, the research compares results with a portfolio constructed under similar conditions of certainty and risk. The findings indicate that portfolios created under certainty yield significantly higher values, suggesting that successful portfolio construction is heavily influenced by the prevailing market conditions experienced by investors.

This study aimed to examine the effect of raw material inventory and production costs on company net profit. The dependent variable is the net profit of manufacturing companies, while the independent variables are raw material inventory and production costs consisting of raw material costs, direct labor costs, and factory overhead costs. The population of this study was manufacturing companies in the consumer industry sub-sector that were listed on the Indonesia Stock Exchange (IDX) during the period 2018–2020. Sampling was based on purposive sampling using the criteria of consumer industry companies listed on IDX during 2018–2020, which used the rupiah as the currency in their financial reports, and had complete financial report data. Multiple linear regression was employed as the data analysis technique. The results show that raw material inventory had no effect on company profits, raw material costs had a significant positive effect on company profits, direct labor costs had a significant positive effect on company profits, and factory overhead costs had no significant effect on company profits. The coefficient of determination (R2) shows that 14.4% of company profits in the consumer industry sub-sector for the period 2018–2020 can be explained by raw material inventories, raw material costs, direct labor costs, and factory overhead costs.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document- centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.