Unravelling the nexus between energy prices and exchange rate in Malaysia: Fresh insights from a non-linear perspective using threshold cointegration analysis.

This paper proposes a nonlinear threshold cointegration framework to study how energy prices affect Malaysia's nominal exchange rate, considering the money supply, income, and interest rate. The study employs a threshold cointegration approach utilizing threshold autoregressive and momentum threshold autoregressive models. The momentum threshold vector error correction model determines the short-run adjustment of exchange rate deviation from the long-run equilibrium level. The findings reveal that the nonlinear adjustment process to capture the short-run deviation in the long-run equilibrium path is primarily influenced by energy prices, money supply, and interest rates. These results highlight the importance of considering the impact of energy prices on exchange rate policies when formulating and implementing economic policies in Malaysia. The findings can also be valuable for decision-makers to comprehend the future dynamics of exchange rates and make well-informed decisions.

Health Recommendation System using Deep Learning-based Collaborative Filtering.

The crucial aspect of the medical sector is healthcare in today's modern society. To analyze a massive quantity of medical information, a medical system is necessary to gain additional perspectives and facilitate prediction and diagnosis. This device should be intelligent enough to analyze a patient's state of health through social activities, individual health information, and behavior analysis. The Health Recommendation System (HRS) has become an essential mechanism for medical care. In this sense, efficient healthcare networks are critical for medical decision-making processes. The fundamental purpose is to maintain that sensitive information can be shared only at the right moment while guaranteeing the effectiveness of data, authenticity, security, and legal concerns. As some people use social media to recognize their medical problems, healthcare recommendation systems need to generate findings like diagnosis recommendations, medical insurance, medical passageway-based care strategies, and homeopathic remedies associated with a patient's health status. New studies aimed at the use of vast numbers of health information by integrating multidisciplinary data from various sources are addressed, which also decreases the burden and health care costs. This article presents a recommended intelligent HRS using the deep learning system of the Restricted Boltzmann Machine (RBM)-Coevolutionary Neural Network (CNN) that provides insights on how data mining techniques could be used to introduce an efficient and effective health recommendation systems engine and highlights the pharmaceutical industry's ability to translate from either a conventional scenario towards a more personalized. We developed our proposed system using TensorFlow and Python. We evaluate the suggested method's performance using distinct error quantities compared to alternative methods using the health care dataset. Furthermore, the suggested approach's accuracy, precision, recall, and F-measure were compared with the current methods.

New trading strategy in investment and a new anomaly: A study of the hedge funds from emerging and developed markets.

This paper introduces a new trading strategy in investment: including the asset (Asset A) with the highest mean, the asset (Asset B) that stochastically dominates many other assets, and the asset (Asset C) with the smallest standard deviation in their portfolio to form portfolios in the efficient frontier for emerging and developed markets that could get higher expected utility and/or expected arbitrage opportunities. To test whether our proposed new trading strategy performs better, we set a few conjectures including the conjectures that investors should include any one, two, or three of Assets A, B, and C from emerging and developed markets. We test whether the conjectures hold by employing both mean-variance and stochastic dominance (SD) approaches to examine the performance of the portfolio formed by using hedge funds from emerging and developed markets with and without Assets A, B, and C, the naïve 1/N portfolio, and all other assets studied in our paper. We find that most of the portfolios with assets A, B, and C++ stochastically dominate the corresponding portfolio without any one, two, or all three of the A, B, and C strategies and dominate most, if not all, of the individual assets and the naïve 1/N portfolio in the emerging and developed markets, implying the existence of expected arbitrage opportunities in either emerging or developed markets and the market is inefficient. In addition, in this paper, we set a conjecture that combinations of portfolios with no arbitrage opportunity could generate portfolios that could have expected arbitrage opportunity. Our findings conclude that the conjecture holds and we claim that this phenomenon is a new anomaly in the financial market and our paper discovers a new anomaly in the financial market that expected arbitrage opportunity could be generated. We also conduct an out-of-sample analysis to check whether our proposed approach will work well in the out-of-sample period. Our findings also confirm our proposed new trading strategy to include Assets A, B, and C in the portfolio is the best strategy among all the other strategies used in our paper and gets the highest expected wealth and the highest expected utility for the emerging and developed markets. Our findings contribute to the literature on the emerging and developed markets of hedge funds and the reliability of alternative risk frameworks in the evaluation. Our findings also provide practical experience to academics, fund managers, and investors on how to choose assets in their portfolio to get significantly higher expected utility in emerging and developed markets.

The relationship between sovereign credit rating changes and firm risk.

Sovereign credit ratings, extensively studied for their influence on macroeconomics and country risk, have been less explored in the context of their impact on individual firms. This research delves into the effects of sovereign credit rating changes on firm risk. Our findings suggest that an upgrade in sovereign credit ratings decreases firm risk, while a downgrade amplifies it. Furthermore, the magnitude of a country's rating shift positively correlates with changes in firm risk. We also discern a contagion effect between trade-dependent countries: an elevated rating in one country diminishes the firm risk in its trading partner, and vice versa. When categorizing our data into developed and developing markets, we observe that firm risk in developed markets reacts more acutely to rating upgrades. Conversely, rating downgrades, whether domestic or in trade-associated countries, intensify firm risk in developing markets. A robustness check, which evaluates sovereign credit rating fluctuations outside of financial crises, corroborates our core findings.

Unlocking the secrets of exchange rate determination in Malaysia: A Game-Changing hybrid model.

Nominal exchange rate determination is a puzzling phenomenon throughout the literature. Thus, the study aims to analyze the nominal exchange rate determination with a hybrid approach of macroeconomic and microstructure determinants, i.e., interest rate differential, oil price, order flow, and bid-ask spread over the long- and short-run horizons in the context of Malaysia. The dataset consists of high-frequency daily data from 2010 to 2017, employing a nonlinear ARDL approach. The results indicate that the bid-ask spread and interest rate differential were found to be key determinants of exchange rate dynamics in the long and short run. The findings show strong evidence of long-run asymmetry in the interest rate differential, while short-run asymmetry effects exist between microstructure determinants and the exchange rate. In addition, it indicates that the bid-ask spread holds informative content to explain the dynamics of the exchange rate in Malaysia. Additionally, the negative changes in the oil price could potentially act as macroeconomic news announcements and the bid-ask spread as liquidity determinants in Malaysia, which play a significant role in exchange rate determination. The study concluded that the prominent short-run asymmetry effects captured in cumulative order flow and bid-ask spread While a long-run asymmetry exists between the oil price and exchange rate in Malaysia. The empirical results allow for long-run and short-run asymmetric pricing impacts of a hybrid approach on the nominal exchange rate in Malaysia. This study is helpful in providing policy direction and practical implications for monetary authorities and market dealers. The bid-ask spread and oil price could be considered influential exchange rate determinants in the short run in Malaysia.

Carbon emissions and the rising effect of trade openness and foreign direct investment: Evidence from a threshold regression model.

The relationship between carbon emissions, foreign trade openness, and FDI has been studied in prior studies. The previous studies, however, did not examine the link by focusing on carbon emissions in India's industrial sectors. Using carbon emission intensity as a threshold variable and a threshold regression model, we add to the existing studies by assessing the influence of India's industrial sector on carbon emissions. According to the study's findings, there are three threshold effects of foreign direct investment and foreign trade openness on industrial carbon emissions. FDI harms industrial carbon emissions, as it has a characteristically declining and then rising effect coefficient on industrial carbon emissions. Foreign trade openness, however, affects carbon emissions both positively and negatively. Foreign trade openness encourages carbon emission in sectors of the economy with lower carbon emission intensity. However, it also partially constrains it for sectors with high carbon emission intensity. The number of employees, technological innovation, GDP per capita, and economic activity intensity significantly influence carbon emissions in India's industrial sector. This study can extend further in other countries using the recent innovative methodologies.

Role of geopolitical risk, currency fluctuation, and economic policy on tourist arrivals: temporal analysis of BRICS economies.

The tourism industry is vulnerable to a range of economic and political factors, which can have both short-term and long-term impacts on tourist arrivals. The study aims to investigate the temporal dynamics of these factors and their impact on tourist arrivals. The method employed is a panel data regression analysis, using data from BRICS economies over a period of 1980-2020. The dependent variable is the number of tourist arrivals, while the independent variables are geopolitical risk, currency fluctuation, and economic policy. Control variables such as GDP, exchange rate, and distance to major tourist destinations are also included. The results show that geopolitical risk and currency fluctuation have a significant negative impact on tourist arrivals, while economic policy has a positive impact. The study also finds that the impact of geopolitical risk is stronger in the short term, while the impact of economic policy is stronger in the long term. Additionally, the study shows that the effects of these factors on tourist arrivals vary across BRICS countries. The policy implications of this study suggest that BRICS economies need to develop proactive economic policies that promote stability and encourage investment in the tourism industry.

Analyzing the nexus between financial risk and economic risk in India: Evidence through the lens of wavelet coherence and non-parametric approaches.

The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the "wavelet power spectrum (WPS)" and "wavelet coherence (WTC)" approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the "quantile regression (QR)" and "quantile-on-quantile regression (QQR)". Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary.

Do oil, gold and metallic price volatilities prove gold as a safe haven during COVID-19 pandemic? Novel evidence from COVID-19 data.

The spreading COVID-19 outbreak has wreaked havoc on the world's financial system that raises an urgent need for the re-evaluation of the gold as safe haven for their money because of the unprecedented challenges faced by markets during this period. Therefore, the current study investigates whether different asset class volatility indices affect desirability of gold as a safe-haven commodity during COVID-19 pandemic. Long run and the short run relationship of gold prices with gold price volatility, oil price volatility, silver price volatility and COVID-19 (measured by the number of deaths due to COVID) has been analyzed in the current study by applying ARDL Bound testing cointegration and non linear ARDL approach on daily time series data ranging from January 2020 to Dec 2021. Findings of the study suggest that in the long run, oil price volatility and gold price volatility positively affect the gold prices, whereas the effect of silver price volatility on gold prices is negative in the long run. However in the short run, all the three indices negatively impact the gold prices. In contrast, the impact of COVID-19 is positive both in the short run and in the long run that proves the validity of gold as safe haven asset in the time of the deadly pandemic. The findings of this study have significant implications and offer investors with some indications to hedge their investments by considering the gold's ability of safe haven during this era of pandemic.

Tax competition, environmental regulation and high-quality economic development: An empirical test based on spatial Durbin model.

Studying economic development in China is a very important topic recently because China's economy is moving toward high-quality development and local governments face the dilemma of environmental governance and economic development. To contribute to the literature in this area further, this paper assesses the impact of tax competition and environmental regulation on high-quality economic development through the spatial Durbin model and instrumental variable and by using the data from 278 prefecture-level and above cities from 2007 to 2017 in China. Our empirical analysis shows that tax competition inhibits high-quality economic development and a positive spatial spillover effect, environmental regulation has a significant direct promoting effect on high-quality economic development and a negative spatial spillover effect, and local government tax competition inhibits the promotion effect of environmental regulation on high-quality economic development. Further heterogeneity analysis conducted in our study shows that both the direct and spatial spillover effects of tax competition and environmental regulation on high-quality economic development in large and medium-sized cities are significantly lower than those in small cities. Our empirical analysis infers that since the 18th National Congress of the Communist Party of China, the promotion effect of environmental regulation on high-quality economic development and the synergistic effect with tax competition has become more and more significant. The findings in our paper are useful for both the central government and the local governments in making better decisions for economic development in China as well as in other countries.

Editorial Statement and Research Ideas on Using Behavioral Models in Environmental Research and Public Health with Applications.

Behavioral models are very important in the development of both environmental research and public health because much of the evidence of empirical findings cannot be explained by using the traditional theories in environmental research and public health; behavioral models play a key role in the analytical apparatus of contemporary approaches to overcome the difficulty in all areas of both environmental research and public health [...].

Sustainability of Nursing Leadership and Its Contributing Factors in a Developing Economy: A Study in Mongolia.

The sustainability of nursing leadership is a very important problem. Every country continually strives to find the best ways to advance in nurse management and patient care services. Nursing leadership is most desirable in the delivery of health care services. Since there is limited information about leadership skills in Mongolia, to solve the problem of the sustainability of nursing leadership, we carried out this study to explore factors contributing to the sustainability of nursing leadership and their correlation relatively to nurse managers in healthcare institutions. A sample of 205 nurse managers from all forms of health facilities participated in this study. The data were analyzed by descriptive, correlation, and multiple linear regression models using SPSS 19 version. The linear combination of the five independent variables was significantly related to the dependent variable (nurse leadership). Both the behavior and problem-solving are significant regressors of the dependent variable. The correlation analysis significance of the independent study variables, two were found to have a significant effect on nursing leadership: behavior and performance of nurses significantly and positively effect nursing leadership. The transformational role and nurse leadership produced a significantly positive Correlation coefficients give a direction of causation in the relationships of variables, and the multiple linear regression analysis says that two of the variables, namely, behavior and problem-solving, positively contribute to nursing leadership, two of the variables namely, work environment and performance nurse manager do not support; however, variable transformational ability majorly contributes to the sustainability of nursing leadership.

Economic and tourism growth impact on the renewable energy production in Vietnam.

High economic and tourism growth demand extensive energy production that needs the energy creation industry's attention and demands the researchers' and policymakers' emphasis. Hence, the present study examines the impact of economic and tourism growth on renewable energy production (REP) in Vietnam. The present research has taken the gross domestic product (GDP), exports, human capital, and employment rate to measure the economic growth, while international tourism receipts and expenditures have been used to measure the tourism growth. The secondary data have been extracted from 1983 to 2020 using World Development Indicators (WDI) database. The Nonlinear Autoregressive Distributed Lagged (NARDL) model has been applied to investigate the linkage between the constructs. The findings indicated that GDP, exports, human capital, employment rate, international tourism receipts, and expenditures have a significant and positive relationship with REP in Vietnam. These results guide the regulators while making regulations related to the extensive energy production in return for high economic and tourism growth.

A static and dynamic copula-based ARIMA-fGARCH approach to determinants of carbon dioxide emissions in Argentina.

This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (EC) and non-renewable (REN) energies), economic globalization (GLO), and economic growth (GDP) on carbon dioxide (CO2) emissions in Argentina over the period 1970-2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs EC-CO2 and GDP-CO2 are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO2 emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO2 emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO2 emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina's environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO2 emissions on the non-renewable energy and economic growth are time-varying, while the pairs REN-CO2 and GLO-CO2 possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO2 emissions.

Habit-Does It Matter? Bringing Habit and Emotion into the Development of Consumer's Food Waste Reduction Behavior with the Lens of the Theory of Interpersonal Behavior.

The immense food waste, generated by restaurants is not only a serious burden for the foodservice business but also a cause of anguish for the emerging nations in which eating out is becoming increasingly trendy. Consumers' food wastes account for a significant portion of restaurant food waste, indicating the need for a change in consumers' behavior to minimize food waste. To examine this problem, our study sought to identify the elements that influence restaurant consumers' behaviors on food waste reduction, reuse, and recycling. The influence of anticipated positive emotions, awareness of consequences, environmental knowledge, and social norms on waste reduction intentions were examined by using a quantitative technique in the investigation. Furthermore, the influence of habits, waste reduction intentions, and facilitating conditions on food waste reduction, reuse, and recycling behaviors have also been investigated. The study collected 1063 responses and employed the PLS-SEM approach to verify the hypotheses. The results suggested that anticipated positive emotions, awareness of consequences, environmental knowledge, and social norms all have substantial impacts on waste reduction intentions. In addition, habits, waste reduction intentions, and facilitating conditions have noteworthy influences on consumers' behaviors towards food waste reduction, reuse, and recycling in restaurants. Understanding these elements could help in correcting customers' waste behaviors in restaurants. The findings in this study are useful for managers, policymakers, and researchers who want to solve the problems of food waste. The implications, limits, and suggestions for further studies have also been discussed in our study.

Antecedents of Consumer Food Waste Reduction Behavior: Psychological and Financial Concerns through the Lens of the Theory of Interpersonal Behavior.

This study sought to investigate the role of consumers' emotional, cognitive, and financial concerns in the development of food waste reduction, reuse, and recycling behavior among restaurant patrons. Food waste in restaurants is a major problem for the food service industry, and it is a growing source of concern in developing countries, where eating out is becoming increasingly popular. A large portion of restaurant food waste in these markets originates from the plates of customers, highlighting the importance of consumer behavior changes in reducing waste. The current study has used a quantitative approach to analyze the impact of anticipated negative emotion of guilt, awareness of consequences, habit, and financial concern on food waste reduction behaviors, i.e., reduce, reuse, and recycle. The study collected 492 responses and data is analyzed for hypotheses testing through Partial Least Square-Structural Equation Modelling. The findings showed that anticipated negative emotions of guilt, awareness of consequences, habit, and financial concern have a significant impact on restaurants' consumer food waste reduction behaviors. Managers, policymakers, and researchers interested in resolving the food waste problem will find the study useful. Other topics discussed include the implications and limitations as well as possible future research directions.

Sustainability of Household Food Waste Reduction: A Fresh Insight on Youth's Emotional and Cognitive Behaviors.

The sustainability of food waste is one of the most important contemporary economic, social, and environmental issues that encompasses useful academic, practical, and policymaking implications. Under the domain of sustainability, food waste is a serious global challenge with a growing public, political, and corporate concern. Existing literature regarding the sensitization of consumers and the promotion of waste cautious behaviors still has much room for improvement in household waste. To bridge the gap in the literature, this study identifies and examines determinants of young consumers' food waste reduction behavior in households. Using a sample size of 391 young consumers of household food products from Pakistan, a full-scaled administrative survey is conducted, and our hypotheses are empirically tested by using the PLS structural modeling equation. Our findings reveal significant impacts from both cognitive and emotional aspects on sustainable food waste reduction behavior. Our results have several important implications for policymakers and all the stakeholders, especially for marketers, including advertising strategies, policies to mitigate the impact of food waste, and the development of educational programs related to food waste.

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features-the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets-that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.

Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model.

In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.

Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality.

Most authors apply the Granger causality-VECM (vector error correction model), and Toda-Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO2 emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO2 emissions, and fossil fuel consumption in China from 1965-2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO2 emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO2 emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO2 emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.