This study investigated the impact of corporate financial strategies-(CFSs) on the performance of companies listed on the Shanghai Stock Exchange-(SSE) from 2010-2023, analyzing data from 2,269 firms, yielding 31,766 balanced firm-year observations. Utilizing a mixed-methods approach with a quasi-experimental design grounded in pragmatism, the inquiry employed two-step System-GMM technique to address endogeneity, simultaneity, heteroscedasticity, reverse causality and Nickell bias. Fixed effects-(FE) and random effects-(RE) models were applied to handle unobserved heterogeneity, omitted variable bias and guarantee robustness. The results revealed that, total-debt-to-assets-ratio-(TDTAR) and dividend yield-(DY) significantly and negatively impacted firm performance-(FP), measured by return on assets-(ROA) and Tobin’s Q-(TQ). Contrary, cash conversion cycle-(CCC), current ratio-(CR), total-assets-turnover-(TAT), tangibility-(TANG), total-equity-to-total-assets ratio-(TETAR), dividend payout ratio-(DPR), firm size-(SIZE), and firm age-(AGE) had a significantly positive effect on FP-(ROA and TQ). The study emphasizes the importance of effective CFSs in improving FP and offers insights for policymakers, investors, and managers, highlighting the need for corporate deleveraging, capital structure optimization and efficient asset and working capital management. Although focused on China, the study’s framework is applicable to other emerging markets, providing valuable theoretical, conceptual, and methodological insights as it integrates CFS metrics into the resource-based view theory-(RBVT), extending the theory’s scope making it more robust and generalizable.

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.

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.

In this study, we examined the volatility trend of stock return in eight ASEAN stock markets. These includes the Singapore Exchange (SGX), Bursa Malaysia Stock Exchange (YSX), the Stock Exchange of Thailand (SET), Indonesia stock exchange, the Vietnam Stock Exchange (VNX), the Cambodia Securities Exchange (CSX), the Lao Securities Exchange (LSX), and the Philippine Stock Exchange. Secondly, we evaluated the factors that influence the level of return in those stock markets with exchange rate volatility as a control variable. By employing FIGARCH-DCC and ARDL models, the study aimed to provide a more robust understanding of stock market dynamics. The findings reveal significant negative returns effect of market volatilities and liquidity crisis in all the stock exchanges of all sample countries in the study. In Singapore, money supply variation, market volatility, liquidity risks, and exchange rate volatility significantly influenced stock returns positively. The short-run model explains 52.26% of the variation in stock returns. Only in Malaysia, we had significant positive returns from exchange rate volatility. Nevertheless, the Russian model explains just 22.22% of the variation in stock returns. In Thailand and Indonesia alike, returns significantly and positively responded to variation in money supply, while the volatility in the market and currency rate exchange adversely impacted returns. The short-run models explain 53.66% and 65.21% of the variation in stock returns for Vietnam and Indonesia, respectively. The variation in money supply does not significantly affect stock returns and has no significant contribution to returns in Cambodia. The Cambodia model explains around 48.34% of the variation in returns. For Lao Stock Exchange, return effects of liquidity risk, and exchange rate instability were significant and negative. Market volatility had insignificantly impacted stock returns in Nigeria. The Lao model explains 50.38% of the variation in stock returns. In the Philippine Stock Exchange, the returns effect of exchange rate volatility and liquidity crisis are adverse and significant. Money supply variation and market volatility had insignificant influence on returns. The model explains 68.11% of the variation in returns. In the Philippines, market volatility, liquidity risks, and exchange rate volatility adversely impacted returns. Money supply variation had no such significant influence on returns. The panel model of the Philippines explains 62.9% of the variation in stock returns. The research accentuates the need for governments to stabilize exchange rates, boost liquidity, through targeted policies aimed at managing stock market dynamics especially as it relates to stock volatility in order to foster meaningful growth and development of the financial market.

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.

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.