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.