Loan Self Control (LSC) can be defined as the ability of self-control related to behavioral control, cognitive control, and decisional control in managing loans or debts for financial welfare. The study aims to determine the factors of loan self-control in Kredit Usaha Rakyat (KUR) banking customers of Micro Small Medium Enterprise (MSMEs) in Indonesia so that they can obtain three-time financing from banks. This study used 87 samples collected by purposive sampling technique. Primary data was collected using an online survey. Factor analysis formed 3 loan self-control factors, namely Behavioral control, Cognitive control and decisional control from 14 indicators tested.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

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 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.

In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.

An increase in investment is required to support the growth and expansion of the industrial sector in a given country. But the planning and ranking of investments must consider financial resource constraints, high investment risk, the frequency of needs and goals, as well as an unfavorable pattern of investments in the production sectors and industries. To ascertain ranking and economic viability for the investment sectors, this study used optimization techniques called TOPSIS, VIKOR, and COPRAS. From 2020 to 2022, this study was conducted for the Debre Berhan City Administration. The study's findings include investment criteria and outline the importance of certain investment areas. The final findings of this study show that, according to TOPSIS, VIKOR, and COPRAS, the current industrial investment pattern is not ideal investment priorities need to be changed. As a result of the above three optimization techniques; spinning, weaving, and finishing of textile fabric’s sector have been ranked first.

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.

The introduction of derivatives at the financial market of the United Arab Emirate (UAE) is to enhance liquidity and broadens the range of securities. This is because it brings exciting opportunities for investors to diversify their investment in an efficient and cost-effective way. Evidence from previous studies has shown that financial market derivatives help to reduce risk. Even though trading losses produced by unsuitable derivative activity are frequently big enough to create financial problems and even bankruptcy, there is minimal research on how bank profitability and performance are affected. The study examines the determinants of financial derivatives on the performance of commercial banks in UAE and the financial risk exposure between derivatives financial assets and derivatives financial liabilities. The research employs Pecking order theory, panel ARDL and data from 30 commercial banks’ financial statements in 2020 in UAE. The study found that an increase in the level of return on assets will create an increase in traded financial derivatives that will enhance bank performance by a high level of percentage. Stability of the banking sector in UAE is recommended to enhance better performances of commercial banks on financial derivatives in UAE.

Supply chain integration is a key factor in improving business performance. Despite this growing interest, little research has highlighted this issue that will determine the survival of many companies. The purpose of this study was to examine the relationship between the supply chain integration practices (SCMP) and the financial and operational performance of Tunisian manufacturing companies. To achieve this, a questionnaire was used as a research tool for data collection. Multiple regression analysis using SPSS26 software was used to answer study questions and examine study hypotheses. According to research, SCMP includes customer relationship management (CRM), supplier relationship management (SRM), buyer-supplier collaboration (BSC), joint knowledge creation (JCK), level of information sharing (IS), goal congruence (GC) , risk management (RM), internet usage (IU), quality management (QM) , except information quality (IQ), collaborative planning, forecasting and replenishment practices (CPFR), and just-in-time (JIT), are all significantly related to the financial and operational performance of the companies surveyed. Some recommendations are proposed to help managers better manage knowledge throughout the supply chain.

The COVID-19 outbreak has illustrated the wide range of issues that supply networks confront when they are subjected to major interruptions. The supply networks for vaccines are no exception. To get out of this pandemic, it's critical to identify and address problems with the COVID-19 vaccine supply chain (VSC). This work identifies 13 challenges and prioritizes those. The findings provide stakeholders and government policymakers with realistic advice for developing a better VSC. This paper proposes a methodology based on fuzzy analytical hierarchy process (Fuzzy-AHP) with the use of triangular fuzzy numbers for prioritizing VSC barriers. It has been found that the impact of poor health worker training facilities becomes maximum with the highest weightage. Moreover, the managerial implication of the results is also provided, which will be useful for VSC sectors to take suitable decisions to overcome these obstacles.