In today's dynamic business landscape, the audit profession encounters numerous obstacles, particularly in adapting to the necessity of computer-assisted audits due to the immense volume of data requiring scrutiny. Despite the emergence of different digital auditing tools, there is a gap in research regarding the level of adoption, and its effects on the quality of audit services especially in the context of developing countries. This study seeks to investigate the impact of digitalization of audit tasks on the quality of audit services of accounting firms in Nigeria. The study, which has its foundation on the Technology Acceptance Model (TAM) integrated with the Technology, Organization, and Environment (TOE) framework, adopted the survey research design. The population of study was made up practitioners of accounting firms in Abuja and Lagos, Nigeria. A self-designed questionnaire was used as a tool for data collection for the study. Findings of the study indicate that automation of audit tasks enhances the quality of audit services suggesting that adopting IT infrastructures leads to more reliable audit procedures, improved efficiency and accuracy, as well as mitigating audit risks. Results also revealed that Big Four auditors are significantly ahead in the adoption of digital technologies compared to the non-Big Four auditors, confirming the dominance of larger accounting firms in application of emerging technologies in performing audit tasks.

Research on stock exchange markets is essential to stock market investors as it offers sensitivities to risk management. This research investigates the patterns of the volatility of stock market prices in ten African stock markets. We estimated the dynamic GARCH model of Engle using the method of maximum likelihood estimation. Daily time series from January 1, 2021 to December 30, 2022, were obtained from African Stock (Securities) Exchange database. The findings established the existence of a normally distributed Senegalese stock market as against time-varying volatility of stock prices in Nigeria, Ghana, Mali, Burkina Faso, Togo, Niger Republic, Benin Republic, Ivory Coast, and Gambian. Hence, the likelihood that an asset or stock is being overpriced (overvalued) or underpriced (undervalued) in the Senegal stock market is low. It is therefore easier for stock traders and investors in Senegal to pick entry and exit points. Unfortunately, this cannot be said of the investors in stock markets of other countries. In effect, the closing price of a stock is most often heavily deviated with significant outliers. This further infers that variations of stock prices in these markets are very wide, heavy, and unpredicted. Hence, it is a case of the volatility of volatilities.

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.

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 article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

Portfolio optimization is a widely recognized strategy for investing that involves selecting a combination of assets that offers the optimal balance between potential gains and volatility. Traditional portfolio optimization typically focuses on a single period, considering only the current market conditions. However, multi-period portfolio optimization takes a more comprehensive approach by incorporating the dynamic nature of financial markets over multiple periods. Hence in this study, we focus on multi-period portfolio optimization. We conduct a bibliometric analysis of articles on multi-period portfolio optimization in the Web of Science (WoS) database. Through quantitative methods and the utilization of the Bibliometrix R package, we analyze publication trends, key research sites, and historical output in this field. Our findings provide valuable insights into the current state of research on multi-period portfolio optimization. This bibliometric analysis contributes to the existing literature on multi-period portfolio optimization and serves as a valuable resource for researchers, policymakers, and practitioners in the field of finance.

The concept of fiscal transparency holds significant importance; however, its attainment poses considerable challenges. Current discourse suggests that the concept of transparency lacks sophistication. To tackle this hurdle regarding public expenditure, analytical approaches that pertain to the general concept of transparency can be utilized. The present paper aims to ascertain the determinants of the openness of the provincial budget and its subcategories by human development, technology access, as well as industrialization progress, in addition to other relevant indicators. This study aims to ascertain the fundamental principles that underpin the achievement of efficient transparency in public expenditure. In terms of outcomes, the overall model has limited relations to the hypotheses. For the elemental dependent variables, the impact of various forms of transparency on the attainment of public policy goals varies, but generally have similar results. The study has attempted to conclude the components for an optimal transparent budget system, thus providing a suggestion for effective schemes later on.

The aim of this study is to experimentally examine the relationship between supply chain finance (SCF) and the financial performance of Vietnamese listed non-financial enterprises. A conceptual framework was developed and tested using secondary data collected from 659 companies listed on the Vietnam Stock Exchange in 2022. The path method is used using Stata 18 software. The results highlight that SCF has a positive impact on the financial performance of listed non-financial enterprises in Vietnam. These findings will help businesses better understand how SCF implementations benefit their financial performance in a global environment, especially in emerging markets.

This study aims to evaluate the impact of transparency of budget structure on risk of getting into financial distress in Vietnam. The article uses data regarding equity proportion from the financial reports of business entities on Hanoi Stock Exchange (HNX), divided into four main categories, namely large ownership, institutional ownership, managerial ownership, and state ownership ratio, to find the relation between the allocation of equity and the chance a company having financial failure. From the mentioned information, the research attempts to explain the relations, as well as suggestions for companies to prepare and avoid financial distress from an equity structure perspective. Results from the study’s sample show insignificant correlations between the share of owners and financial distress situation of a company, which plays a part to help the overall estimation of risk in businesses as a whole.

In line with Sharia principles, this study explores the correlation between increased returns and heightened risks, aligned with Al-Ghunm bi Al-Ghurm. Daily data from the Jakarta Islamic Index 70, spanning December 2021 to November 2023, was analysed using quantitative methods. Four key trading strategies were assessed: buy-and-hold and three Ichimoku strategies (Conservative, Moderate, and Aggressive). The results indicate the effectiveness of a moderate Ichimoku strategy because of its well-balanced approach. Despite no significant performance differences between Ichimoku's trading rules and buy-and-hold, the study emphasizes nuanced decision-making for diverse investor preferences. The insights benefited Indonesian Sharia investors, suggesting enhancements in risk mitigation techniques, and considering external factors. Enriching technical analysis and Sharia-compliant investments, this study underscores the importance of aligning financial objectives with ethical considerations, contributing to the evolving landscape of the Indonesian Sharia stock market.