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This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

The purpose of this study is to investigate the presence of psychological barriers both in the main stock market indices of the Baltic states and the most actively traded individual stocks. A psychological barrier refers to a specific price point, often at round numbers (i.e. powers of 10), that investors believe is challenging to breach, influencing their behavior and trading decisions.

We conduct uniformity tests and barrier tests, such as barrier proximity tests and barrier hump tests, to evaluate the presence of psychological barriers. Additionally, we explore variations in means and variances near these potential barriers using regression and GARCH analysis.

The findings reveal that psychological barriers do exist in the Baltic stock markets, particularly within market indices. The Estonian market index stands out with the most pronounced indications of psychological barriers. Individual stocks also display significant changes in means and variances related to potential barriers, albeit with less uniformity.

Collectively, our findings challenge the traditional assumption of random returns within the Baltic stock markets. For practitioners, the finding that psychological barriers exist opens up opportunities for investment strategies that can capitalize on them.

This study is the first to comprehensively investigate psychological barriers in the Baltic stock markets. Our results provide a valuable contribution to understanding the impact of that phenomenon on pricing dynamics, which is particularly pertinent in less-researched frontier markets like the Baltic states.

The inclusion of esports as an official event in the Hangzhou Asian Games is an important step towards the institutionalisation of esports. The significance of this event marks that Asia once again takes a lead in the global esportisation. This chapter investigates a series of history events in the inclusion process of esports into the comprehensive Games in Asia using process sociology and actor network theory (ANT). This study will analyse the type characteristics of esports events in Hangzhou Asian Games, whilst examining how key stakeholders' interact and balance in the network composed of international sports organisations, host of the event, emerging esports organisations and esports game companies. The chapter also examines the functions of global game industrial economic geography, local cultural politics, esports geopolitics and Olympic values in esports sportization, aiming to reveal the implications of esports inclusion in the Asian Games on the debate of whether esports meets the criteria to be classified as a ‘sport’ and its enlightenment of digital strategy to the inclusion esports in the Olympics.

This research aims to investigate the disciplinary functions of depositors and subordinated debt holders within Indonesia's dual banking system, examining the impact of regulatory changes on market discipline.

The study employs a comprehensive analysis of the dual banking system in Indonesia over 15 years. Utilizing a non-public dataset from the Financial Services Authority and the Indonesia Deposit Insurance Corporation, the study employs propensity score matching and difference-in-differences analysis.

The findings reveal distinct patterns in the exercise of market discipline by depositors over different regulatory regimes. During the blanket guarantee regime (2002–2005), depositors lacked the incentive to monitor banks but resumed their disciplinary role under the limited guarantee regime (2005–2017). Islamic banks faced simultaneous market and regulatory discipline, with market discipline prevailing.

This study contributes to the literature by providing novel insights into the interplay between regulatory changes, market discipline and depositor behavior within Indonesia's dual banking system. The utilization of a comprehensive non-public dataset from regulatory authorities adds to the originality of the research.

The purpose of this review paper is to investigate the consequences of tourism development and economic growth within the Association of Southeast Asian Nations (ASEAN) countries, focusing specifically on Lao PDR post-pandemic. The adverse effect of COVID-19 on tourism and economic sectors has been pervasive across the ASEAN region, with varying degrees of impact. Some of these difficulties are set to continue, though there are positive signs of recovery and of the resilience of the tourism industry. Utilising case material from Lao PDR in Southeast Asia – an area frequently neglected in tourism studies – the paper sheds light on the post-pandemic landscape to address existing gaps in the current literature.

A case study approach was taken in this review paper, utilising secondary data such as media reports, official reports from Tourism Laos and international governing bodies like United Nations and the World Bank to form a viewpoint discussion in the Lao PDR post-pandemic condition.

This paper reveals that contrary to a long period of recovery post-pandemic, there has been a degree of continuity from the pre-pandemic period. Considerable numbers of backpackers have returned to Vang Vieng, along with Vientiane and Luang Prabang. While the pre-pandemic emphasis on mass tourism persists, there is also an increased focus on regional and domestic markets. Laos, with its strategic location and cross-border connections, aims to take advantage of this shift.

The paper highlights a detailed exploration of the Lao tourism industry post-pandemic. It goes beyond the initial expectations in literature of a complete transformation post-pandemic, highlighting the continuity in visitor sources and traditional tourist attractions. It emphasises the Lao PDR strategic position for market reorientation, providing insight into the nation’s adaptive strategies and a nuanced perspective on the evolving landscape of Lao tourism.

Capitalism is under siege (Porter & Kramer, 2011), and business schools are under fire (Amann et al., 2013). So, management and leadership education in higher education institutions should be reinvented under the more challenging era of capitalism. How then can business schools cope with these challenges and contribute to global endeavor for making sustainable capitalism? In this context, there is thus reason for the following three core concerns that new understanding of management and leadership education is required. First, shortcomings of contemporary capitalism lead to failures of responsible management. Second, ethical failure of management leadership is a pressing issue. Third, academic responsibility under the new capitalism remains unexamined. Based on these three core concerns, we seek to generate inclusive insights into the educational embeddedness of management and leadership members and the consequences of such embeddedness on managerial processes, structures, and outcomes under contemporary capitalism.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.