Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)