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.

This study examined the causal relationships between inflation, Gross Domestic Product (GDP), domestic savings, and investment in Zimbabwe using Toda-Yamamoto causality tests and the Autoregressive Distributed Lag (ARDL) approach with secondary data spanning from 1990-2022. The Granger causality analysis revealed a bidirectional causal effect between inflation and GDP, indicating that inflation significantly impacts the country's economic growth. Additionally, the analysis showed a unidirectional causal relationship from inflation to domestic savings, suggesting that high and persistent inflation can erode the value of existing savings and discourage individuals from saving. Furthermore, the study found a distinct causal flow from savings to investment, without feedback in the opposite direction, highlighting the crucial role of a robust savings culture in providing the necessary foundation for sustained investment and economic growth. The ARDL approach provided further insights into the dynamic relationships between these variables. In the short run, lagged GDP and current and lagged savings positively influenced GDP, while the second lag of savings had a negative impact, supporting the Carroll-Weil hypothesis that savings typically follow, rather than precede, economic growth in the short run. The analysis also found a positive short-run and long-run relationship between investment and GDP, supporting the view that investment is an important factor of economic growth. The study recommends that the policy makers can leverage the synergies between savings, investment, and inflation management to foster sustained economic growth and development in line with the government development policies. Developing policies to attract savings and reduce the cost of savings, as well as promoting long-term savings over transactional savings, can increase the country's overall savings base.

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.

This study evaluates exchange rate dynamics between the Naira and global currencies, utilizing weekly data from 2008 to 2024. The exchange rates, NGN/USD, NGN/CAD, NGN/AUD, NGN/EUR, and NGN/JPY were analyzed to explore the impact of macroeconomic determinants such as interest rate differentials, market volatility, and inflation rate differentials on exchange rates. The study employed ARIMA regression, and the wavelength techniques. The results climax the nuanced interplay between global financial flows and local economic conditions in determining exchange rates of the Naira against global currencies. The result on inflation differential aligns with the purchasing power parity (PPP) theory, which suggests that currencies adjust to offset inflation disparities. The market volatility result partly implies that periods of heightened market turbulence tend to favor the EUR, driving up the demand for the Euro and subsequently appreciating its value against the Naira. This finding aligns with traditional financial theories that associate risk-aversion behavior with movements towards stronger, less volatile currencies during times of market stress. The results of the study validate the interest rate parity theory, which posits that higher interest rates in one country attract foreign capital, influencing exchange rate dynamics. The detail level estimates of the decomposition component of wavelet results underscore the relevance of micro-level factors and short-lived fluctuations that may be influenced by weekly market activities, speculative trading, or sudden external shocks. These findings imply that the dynamics of exchange rates are complex, with both prolonged patterns and transient oscillations influencing the currency's overall movement. So, a comprehensive understanding of exchange rate behavior requires consideration of both the broader macroeconomic environment and the micro-level, weekly variations that induce volatility in the currency markets. The research findings emphasize the importance of stable monetary policies to manage currency fluctuation risk. These findings have significant implications for policymakers, financial institutions, and investors in the context of managing exchange rate risk in volatile markets.

This research tries to find a relationship between audit quality and audit software, impacted by the latter. In this study, multiple sentiments of audit professionals and finance executives on the relation of audit software and quality of audit in Kuwait are examined where, on the basis of agreed perspectives of professionals, it was found that audit software positively influences audit quality. This particular article tries to extend the previous works and emphasizes on the observation of audit professionals and their perspectives through a well-structured survey and semi-structured interviews. This study is to identify the distinctiveness of the audit industry in Kuwait comparing market size and available inadequate local auditors. The authors try to establish the relationship between audit quality and audit software considering the fact that acceptance of audits software will definitely give a more effective and robust audit process to cover market needs. The paper also considers the auditors’ training and experience as a moderating factor for the adoption and usage of audit software in auditing practices in Kuwait, resulting in useful insights on the effects of the adoption and use of auditing software in enhancing the quality of audit reports as well as suggesting resources for the use of technological developments in auditing practices. Thus, the study contributes to the extant literature on the dynamics for the adoption and usage of computerized systems in auditing practices to improve the quality of audit reports.

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 research aims to enhance portfolio selection by integrating machine learning regression algorithms for predicting stock returns with various risk measures. These measures include mean-value-at-risk (VaR) variance (Var), semi-variance mean-absolute-deviation (MAD) and conditional value-at-risk (C-VaR). Addressing gaps in existing literature. Traditional methods lack adaptability to dynamic market conditions. We propose a hybrid approach optimized by genetic algorithms. The study employs multiple machine learning models. These include Random Forest, AdaBoost XGBoost, Support Vector Machine Regression (SVR) K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). These models are used to forecast stock returns. Utilizing monthly data from the Tehran Stock Exchange, the results indicate that the genetic algorithm prediction model combined with mean-VaR, Var semi-variance and MAD, produces the most efficient portfolios. These portfolios offer superior returns with minimized risk compared to other models. This hybrid strategy provides a robust and efficient method for investors aiming to optimize returns while managing risk effectively. To implement this approach successfully it is crucial to balance investments. This involves both traditional and alternative asset classes, ensuring diversification. It also capitalizes on market opportunities. Regular review and adjustment of fund allocation are essential. Maintain an optimized strategy for maximum returns and minimal risk.

This research was undertaken to examine the effect of low corporation tax rate on Foreign Direct Investment (FDI) inflow and Gross Domestic Product (GDP). Investors and multinational firms are very rational and therefore prefer to invest in countries where the cost of taxation will be at the barest minimum to maximize their profit. The study aimed to critically analyze the Corporate Income Tax (CIT) rates of the randomly selected countries of the world, and their respective impacts on FDI and GDP. The study is descriptive in nature, based on quantitative data, sourced from various reports of Statutory Corporate Income Tax Rates of Tax Foundation, World Bank and UNCTAD World Investment Report of 2022. Ex-post Facto research design was deployed; while data were analyzed with a General Linear Model of Multivariate Analysis of Variance (MANOVA) with the aid of SPSS version 25. The study found that low rate of corporation tax has a positive and significant effect on FDI as well as on GDP. That is, CIT rate is a dominant determinant for FDI and GDP of countries.

This paper analyses the causal relationship between Foreign Portfolio Investment (FPI), Equities Market Volatility, Exchange Rate and Inflation in Zimbabwe using a monthly time series data between October 2018 and November 2021. The granger causality model was used to present the link between the variables, and EGARCH was used to account for volatility and asymmetric effects on the variables. To incorporate innovations and responses into the Granger model, impulse response functions were used. Links between exchange rate and foreign portfolio investments were found. This only suggests that exchange rate volatility will vary when overseas investors purchase and sell financial securities on the Zimbabwe Stock Exchange (ZSE). In contrast, foreign investors sell local financial securities when local stock market returns are negative, leading to a significant outflow of foreign portfolio investment thereby reducing demand for currency. A significant causal relationship was found between the volatility of the exchange rate and stock market returns. It is assumed that stock market returns, and foreign portfolio investments are caused by fluctuating currency rates. The relationship between exchange rate and ZSE returns, and inflation was found based on Granger causality. This implies that stocks are not suitable for long-term investments that compensate investors for their diminished purchasing power. Policy makers should advise the Zimbabwe Stock Exchange to recommend a reduction in capital gains tax and withholding tax and this encourages investors to hold local equities for a long time.

The objective of this research is to study the reasons for the communication of societal accounting information in Tunisian companies. In fact, we have discussed the questions of the convergence of sustainability accounting models, the questions of the communication of accounting data or even questions of the participation of the actors. The results of qualitative research on a sample of 16 companies show a certain homogeneity in the assessment of the communication policy of societal accounting information of each type of company. It turns out that the social and societal activities identified come under the initiative of companies, their organizational culture and their extra-economic concerns. The social and societal concerns of these companies remain at the heart of the concerns of managers.