In this policy insight, the author lays out the context of the BRI and its role in global development. He also explains why the US should consider working with China on the BRI. The author opines on China’s possible approach and strategy to get global private investors to come on board for the massive BRI projects. He suggests that the global players can establish a third-party market cooperation and coordination mechanism to turn the BRI into a platform for win-win global collaboration.

Pakistan is a leading emerging market as per the recent classification of the International Monetary Fund (MF), and hedging is used as a considerable apparatus for minimizing a firm’s risk in this market. In these markets, investors are customarily unaware about the hedging activities in firms, due to the occupancy of asymmetric environment prevailing in firms. This research paper adds a new insight and vision to the existing literature in the field of behavioral finance by examining the impact of hedging on investors’ sentiments in the presence of asymmetric information. For organizing this research, 366 non-financial firms are taken up as the size sample; all these firms are registered in the Pakistan Stock Exchange. A two-step system of generalized method of moments (GMM) model is implemented for regulating the study. The findings of empirical evidence exhibit that there is a positive relationship between investors’ sentiments and hedging. Investors’ sentiments are negative in relationship with asymmetric information. Due to the moderate presence of asymmetric information, hedging is positively related to investors’ sentiments although this relation is non-significant.

Islamic banking is one of the fastest-growing sectors of the financial industry. Several works have been written in this field, but none attempt to learn the entire Islamic banking and financial system. Furthermore, the study could not locate any publications investigating the conceptual and intellectual foundations of this emerging field of inquiry. The current study uses bibliometric methodologies to assess the current state of Islamic banking, financial research, and the upcoming trends. For the people who choose interest-free investments, the current research examines a conceptual research context on Islamic banking and finance at various planning and decision-making stages. One thousand research studies appearing in scholarly journals between 2005 and 2023 were reviewed for the purpose. In order to examine the works on Islamic banking and finance, bibliometric techniques were used, including analysis of citation network, content, co-citation, keyword, and publishing trends. By suggesting thirteen clusters, to enhance research on Islamic banking and finance to help interest-free investors learn more, the goal of the research is to promote the body of knowledge. The field of Islamic banking and finance has grown from a young lot to a prominent teaching and research tool. Investigating and identifying current research trends in this area is crucial. As institutions and society are placing more emphasis on Islamic banking to raise individual citizens’ responsibilities in developing interest-free investing strategies, the findings are crucial to the community of interest-free financiers. Further research urges with the studies not restricted to a thousand researches only.

Indonesia’s stock market has seen an increase in investment due to the ease of investing and the availability of information about stocks on different social media platforms. This research uses a social network approach to analyze overconfidence behavior in millennial stock investors. This research uses a descriptive quantitative method. The population used in this study are capital market investors in the Greater Solo area who are millennials (<30 years). The number of stock investors in the Greater Solo area is 60,542 investors. The sampling technique in this study was non-probability sampling using purposive sampling. This research uses the AMOS SEM (Structural Equation Model) analysis tool. The conclusion of this study is that millennial investors’ overconfidence behavior increases influenced by financial literacy. investor skills. family ties and friendship ties. The contribution of this research can be applied to understand and educate millennial investors in order to overcome overconfidence behavior so that they can anticipate the losses received. This research may have implications for improving Behavioral Finance Integration Incorporating insights from behavioral finance into investment strategies can help mitigate the negative effects of overconfidence. The limitation in this study is that the scope used in the study is only in the greater solo area.

This research aims to investigate the factors shaping the investment choices of individuals in Saudi Arabia concerning cryptocurrencies, particularly focusing on the influence of the Fear of Missing Out (FOMO) psychological phenomenon. This study employs a mixed-methods approach to comprehend the factors influencing Saudi investors' decisions in the cryptocurrency realm. Quantitative surveys are conducted to gauge perceptions of risk, return, regulatory factors, and social influence. Additionally, qualitative interviews delve into the nuanced interplay of these elements and the impact of FOMO on decision-making. Integrating the Theory of Planned Behavior and Behavioral Finance theories, this research offers a holistic understanding of cryptocurrency investment determinants. The combined quantitative and qualitative methods provide a comprehensive view, enabling an in-depth analysis of the subject matter. The study reveals that Saudi Arabian investors' decisions regarding cryptocurrencies are significantly influenced by multiple factors, including perceived risk, potential return, regulatory environment, and social dynamics. FOMO emerges as a crucial psychological factor, interacting with these influences and driving decision-making. This research underscores the intricate interplay between these factors and FOMO, shedding light on the dynamics of cryptocurrency investment choices in the Saudi Arabian market. The findings hold implications for policymakers, financial institutions, and investors seeking deeper insights into this evolving landscape. Drawing from the Theory of Planned Behavior and Behavioral Finance, it examines perceived risk, return, regulatory factors, and social influence in influencing cryptocurrency investment choices among Saudi investors, focusing on the influence of Fear of Missing Out (FOMO).  The research outcome provides insights for policymakers, financial institutions, and investors seeking to understand cryptocurrency investment dynamics in Saudi Arabia.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.