In this study we examine the time-varying causal effect of the novel COVID-19 pandemic in the major oil-importing and oil-exporting countries on the oil price changes, stock market volatilities and the economic uncertainty using the wavelet coherence and network analysis. During the period of the pandemic, we explore such relationship by resorting to the wavelet coherence and gaussian graphical model (GGM) frameworks. Wavelet analysis enables us to measure the dynamics of the causal effect of the novel covid-19 pandemic in the time-frequency space. Regarding the findings displayed herein, we first found that the COVID-19 pandemic has a severe influence on oil prices, stock market indices, and the economic uncertainty. Second the intensity of the causality effect is stronger in the longer horizon than in the short ones, suggesting that the causality exercise continues. Our findings also provide evidence that the COVID-19 pandemic and oil price changes in oil-importing countries mirror those in oil-exporting countries and vice versa. Further, the COVID-19 pandemic has a profound immediate time-frequency effect on the US, Japanese, South Korean, Indian, and Canadian economic uncertainties. A better understanding of oil and stock market prices in the oil-importing and oil-exporting countries is vital for investors and policymakers, specially since the novel unprecedented COVID-19 crisis has been recognized among the most serious ever happened. Thus, the findings suggest that the authorities should strongly take efficient actions to minimize risk.
Financial markets are exposed to extreme uncertain circumstances escalating their tail risk. Sustainable, religious, and conventional markets represent three different markets with various characteristics. Motivated with this, the current study measures the tail connectedness between sustainable, religious, and conventional investments by employing a neural network quantile regression approach from December 1, 2008 to May 10, 2021. The neural network recognized religious and conventional investments with maximum exposure to tail risk following the crisis periods reflecting strong diversification benefits of sustainable assets. The Systematic Network Risk Index spots Global Financial Crisis, European Debt Crisis, and COVID-19 pandemic as intensive events yielding high tail risk. The Systematic Fragility Index ranks the stock market in the pre-COVID period and Islamic stocks during the COVID sample as the most susceptible markets. Conversely, the Systematic Hazard Index nominates Islamic stocks as the chief risk contributor in the system. Given these, we portray various implications for policymakers, regulatory bodies, investors, financial market participants, and portfolio managers to diversify their risk using sustainable/green investments.
The sustainability issues have been surmounted in the last decades. The digital disruption caused by blockchains and other digitally backed currencies has raised several serious concerns for policymakers, governmental agencies, environmentalists, and supply chain managers. Alternatively, sustainable resources are environmentally sustainable and naturally available resources which are employable by several regulation authorities to reduce the carbon footprint and attain energy transition mechanisms to support sustainable supply chains in the ecosystem. Using the asymmetric time-varying parameters vector auto-regressions approach, the current study examines the asymmetric spillovers between blockchain-backed currencies and environmentally supported resources. We find clusters between blockchain-based currencies and resource-efficient metals, highlighting similar-class dominance of spillovers. We portrayed several implications of our study for policymakers, supply chain managers, the blockchain industry, sustainable resources mechanisms, and regulatory bodies to emphasize that natural resources play a significant role in attaining sustainable supply chains servicing the benefits to society at large and to other stakeholders.
We examine the connectedness of the COVID vaccination with the economic policy uncertainty, oil, bonds, and sectoral equity markets in the US within time and frequency domain. The wavelet-based findings show the positive impact of COVID vaccination on the oil and sector indices over various frequency scales and periods. The vaccination is evidenced to lead the oil and sectoral equity markets. More specifically, we document strong connectedness of vaccinations with communication services, financials, health care, industrials, information technology (IT) and real estate equity sectors. However, weak interactions exist within the vaccination-IT-services and vaccination-utilities pairs. Moreover, the effect of vaccination on the Treasury bond index is negative, whereas the economic policy uncertainty shows an interchanging lead and lag relation with vaccination. It is further observed that the interrelation between vaccination and the corporate bond index is insignificant. Overall, the impact of vaccination on the sectoral equity markets and economic policy uncertainty is higher than on oil and corporate bond prices. The study offers several important implications for investors, government regulators, and policymakers.
Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.
There has been substantial research on megaprojects in project management literature. However, there is dearth of studies empirically investigating performance of new launched megaproject of Thailand that named as "Phuket sandbox". The core purpose of this project is to normalize covid-19 situation and resuming tourism in Thailand. Therefore, the evaluation of project performance is essential to achieve the targeted goal for success. The purpose of this study is to investigate the factors that affect project performance (Phuket sandbox) in Thailand. This study used quantitative approach based on structured questionnaire and the data was collected from Phuket, Thailand. The survey conducted from team members which are tourism stake holders' team, immigration team and public service teams including hospitals and hotels who were supposed for the management of Phuket tourism sandbox operations. The study got 222 valid responses only as the members were so busy and partial lockdowns in Thailand hindered the data collection process. The proposed hypothetical model tested by partial least square structural equation modelling. The results of the study found mix findings. The independent variables are team knowledge management, interpersonal conflict, organizational trust, and as significant and dependent variable as project performance through the mediation of psychological capital. The all relationships found to be significant except problem solving competence which have insignificant relationship with project performance as well as problem solving competence and organizational trust have insignificant relation with psychological capital.
Sustainable finance is a rich field of research. Yet, existing reviews remain limited due to the piecemeal insights offered through a sub-set rather than the entire corpus of sustainable finance. To address this gap, this study aims to conduct a large-scale review that would provide a state-of-the-art overview of the performance and intellectual structure of sustainable finance. To do so, this study engages in a review of sustainable finance research using big data analytics through machine learning of scholarly research. In doing so, this study unpacks the most influential articles and top contributing journals, authors, institutions, and countries, as well as the methodological choices and research contexts for sustainable finance research. In addition, this study reveals insights into seven major themes of sustainable finance research, namely socially responsible investing, climate financing, green financing, impact investing, carbon financing, energy financing, and governance of sustainable financing and investing. To drive the field forward, this study proposes several suggestions for future sustainable finance research, which include developing and diffusing innovative sustainable financing instruments, magnifying and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, devising and unifying policies and frameworks for sustainable finance, tackling greenwashing of corporate sustainability reporting in sustainable finance, shining behavioral finance on sustainable finance, and leveraging the power of new-age technologies such as artificial intelligence, blockchain, internet of things, and machine learning for sustainable finance.
Researchers have mentioned the importance of digitization in improving efficiency and productivity in Small and Medium Enterprises (SME). Fortunately, there is no proof that Digitization can be used to deal with the outcome of severe incidents like COVID-19. The research paper suggested that the increased rate of SMEs has increased significantly. This was entirely due to the advent of Digital Technology (DT). In this way, both product and the process become more automated in digitalization, resulting in increased quality and demand. Considering the high scope for higher development, India's SME sector still has much space for new digital technologies to be integrated. This paper addresses the main scenario of SMEs in India and their benefit in GDP. Also, the research includes a brief analysis of CRM applications and digital payment options in SMEs.
Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.
COVID-19 led restrictions make it imperative to study how pandemic affects the systemic risk profile of global commodities network. Therefore, we investigate the systemic risk profile of global commodities network as represented by energy and nonenergy commodity markets (precious metals, industrial metals, and agriculture) in pre- and post-crisis period. We use neural network quantile regression approach of Keilbar and Wang (Empir Econ 62:1-26, 2021) using daily data for the period 01 January 2018-27 October 2021. The findings suggest that at the onset of COVID-19, the two firm-specific risk measures namely value at risk and conditional value of risk explode pointing to increasing systemic risk in COVID-19 period. The risk spillover network analysis reveals moderate to high lower tail connectedness of commodities within each sector and low tail connectedness of energy commodities with the other sectors for both pre- and post-COVID-19 periods. The Systemic Network Risk Index reveals an abrupt increase in systemic risk at the start of pandemic, followed by gradual stabilization. We rank commodities in terms of systemic fragility index and observe that in post COVID-19 period, gold, silver, copper, and zinc are the most fragile commodities while wheat and sugar are the least fragile commodities. We use Systemic Hazard Index to rank commodities with respect to their risk contribution to global commodities network. During post COVID-19 period, the energy commodities (except natural gas) contribute most to the systemic risk. Our study has important implications for policymakers and the investment industry.