Modeling method of local financial dependence: Evidence from Mongolia.

Many countries have been striving to equalize the balance between the central government and sub-government financial disparities without considering political interference. This paper aims to summarize the theory of local financial federalism and propose an unprecedented model for identifying the factors that affect local financial dependence. The proposed model is based on a theoretical framework incorporating five assumptions and is applied through panel regression analysis. Utilizing panel data from 2013 to 2022 in 21 provinces of Mongolia, a total of nine variables have been identified and econometrically tested within the proposed model. The findings from the panel regression analysis reveal that local budget investment, local personal income tax, local property tax, and other local taxes positively impact the reduction of local financial dependence. However, it is observed that an increase in local budget expenditures and GDP leads to an escalation in local financial dependence.

Implications of corporate social responsibility on the financial and non-financial performance of the banking sector: A moderated and mediated mechanism.

This study aims to investigate the influence of corporate social responsibility (CSR) on both financial and non-financial performance in the Pakistani banking sector by applying a quantitative research approach to the primary data from 405 branch managers in the Pakistani banking sector. Both financial and non-financial performance indicators are analyzed in relation to CSR practices. The CB-SEM findings of this study reveal a significant and positive correlation between CSR and both financial and non-financial performance. Our results demonstrate that CSR initiatives contribute to improved financial performance, reputation, and innovation within the sector. These findings confirm the importance of prioritizing social responsibility initiatives to enhance overall performance in the banking industry. This study provides valuable insights into the impact of CSR on performance in the context of the Pakistani banking sector. The research contributes to the existing literature by highlighting the positive relationship between CSR and financial/non-financial performance. The findings underscore the relevance of CSR practices for banks in Pakistan and emphasize the potential benefits of incorporating social responsibility initiatives into their operations. This study serves as a foundation for future research, encouraging further exploration of CSR's role in the banking industry and its implications for sustainable business practices.

Capital and profitability: The moderating role of economic freedom.

Using the GMM framework, this paper examines the nexus between capital and profitability in the presence of economic freedom using annual data of US banks ranging from 2002 to 2022. consistent with both the financial stability and regulatory hypotheses, the present study's empirical findings reveal that economic freedom exerts a positive moderating influence on the relationship between bank capital ratio and profitability. The findings are heterogeneous across banks' specific characteristics and market conditions. Furthermore, the results are robust to alternative proxies of profitability. The study's finding is beneficial for policymakers and regulators as liberalization is not always adversely impact the economy; rather, it's a double-edged sword, and they must maintain that delicate balance. The conclusions of this study have meaningful implications for senior bankers and policymakers when formulating strategies for preserving bank value. Specifically, it highlights the importance of considering the interactive role of economic freedom in their decision-making process.

m-Polar interval-valued fuzzy hypergraphs and its application in decision-making problems.

This study introduces the novel concept of m-polar interval-valued fuzzy hypergraph (m-PIVFHG), an advancement that combines the strengths of fuzzy theory and hypergraph models to improve decision-making processes. m-PIVFHGs allow vertices to have degrees of membership across multiple polarities within sub-interval values of [ 0 , 1 ] . This offers better adaptability and precision than traditional models. This paper systematically explores the theoretical foundations of m-PIVFHGs, detailing their unique characteristics and presenting duality concepts with illustrative examples. It also defines various cut and level types specific to m-PIVFHGs and examines their properties. The practical utility of m-PIVFHGs is demonstrated through a real-world application aimed at optimizing decision-making in a university setting, showcasing significant improvements over existing fuzzy graph methodologies.

Evaluating the moderating role of governance on the relationships between social inclusion, ICT infrastructure, and financial inclusion.

In this paper, we examine the Moderating Role of Governance on the Relationships between social inclusion (SI), Information and communication technology infrastructure (ICT), and financial inclusion (FI) in 46 countries representing a global sample span from 2010 to 2020. We collect the data from the IMF's financial access survey and construct a multidimensional FI index. Based on the FI index, we divide the sample into two sub-samples (med-high level and low-level FI countries). For the empirics, we employed panel-corrected standard errors, fully modified ordinary least squares and dynamic ordinary least squares techniques. We find that SI is negatively related to FI. ICT infrastructure positively influences FI. Further, we find that governance with sound ICT infrastructure and socially inclusive communities enhances FI. The findings of sub-samples are similar to the full sample results except for a promoting effect of SI and governance in the case of med-high financially inclusive economies. Moreover, the Interaction term of governance and ICT infrastructure is insignificant in med-high financially inclusive economies and negatively significant in low financially inclusive economies. Our study reports novel findings which have significant implications for policymakers and financial institutions to effectively develop and implement new policies which strengthen the institutional base, develop digital banking infrastructure, enhance SI to boost up FI and ensure sustainable economic growth.

Effect of geopolitical risk on energy consumption policy: New empirical evidence from BRICS.

This study investigates the impact of geopolitical risk (GPR) on energy consumption. For empirical analysis, we utilize the dataset of BRICS nations spanning 25 years from 1998 to 2022. We employ three econometric models, namely fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and autoregressive distributed lag (ARDL), to analyze the relationships between GPR and energy consumption. Our empirical findings reveal several significant insights. Firstly, we observe a substantial negative influence of GPR on both fossil fuel energy consumption (FEC) and total energy consumption (TEC). This suggests that higher levels of GPR are associated with reduced utilization of fossil fuels and overall energy consumption within the BRICS countries. Conversely, we identify a significant positive effect of GPR on renewable energy consumption (REC). This implies that, as GPR rises, there is a corresponding increase in the adoption and usage of renewable energy sources. Furthermore, our analysis uncovers the presence of asymmetric effects pertaining to other key determinants of energy consumption, including FDI inflow, economic growth, banking sector development, and inflation rate. This study offers fresh empirical evidence on the intricate interplay between GPR and energy consumption in BRICS nations, shedding light on the significant impacts of GPR and the nuanced effects of various economic factors. These findings have important implications for policymakers and stakeholders seeking to navigate energy policy decisions in a geopolitically dynamic world.

Does economic complexity help in achieving environmental sustainability? New empirical evidence from N-11 countries.

In view of the SDGs argued by UNO, it is vital to address the pressing issues regarding sustainable development. The aim of current study is to investigate the impact of economic complexity (ECC) on environmental sustainability. To achieve this aim, we sampled the 25 years of data of Next-11 countries over the period 1995 to 2019. The economic complexity was measured by the economic complexity index (ECI) while environmental sustainability was measured by two proxy variables including CO2 and greenhouse gas (GHG) emissions. The empirical analysis was established by utilizing the unit root test, cointegration test, FMOLS (fully modified OLS) and DOLS (dynamic OLS) models. The estimated coefficient values disclosed that ECC has a negative and statistically significant relationship with both CO2 and GHG emissions in the long run, implying that ECC ensured environmental sustainability. In addition, the analysis reveals that financial development has a negative while economic growth and energy imports have a positive and statistically significant association with both CO2 and GHG emissions. The findings of the current study suggested an important policy regarding the focus on ECC for achieving environmental sustainability in underlying economies. This study provides robustness to the existing literature in alternative data settings (N-11 countries) and by the unique objective of focusing on environmental sustainability.

Assessing the impact of macroeconomic uncertainties on bank stability: Insights from ASEAN-8 countries.

This research examines the effect of economic uncertainties on bank stability in ASEAN-8 countries. We use estimations with fixed effects for a panel dataset of bank stability, bank characteristics, banking system characteristics, and macroeconomic uncertainties from 2010 to 2020. Geopolitical risk (GPR), economic policy uncertainty (EPU), climate policy uncertainty (CPU), world pandemic uncertainty (WPU), global supply chain pressure (GSCP), and monetary policy uncertainty (MPU) are used as proxies for economic uncertainty (MU) in this research. We find that although an increase in macroeconomic uncertainties weakens bank stability, GPR is positively related to bank stability. An alternative measurement of bank stability, endogenous tests, and quantile regression corroborate our primary findings. Further analyses focus on the heterogeneous influence of macroeconomic uncertainties on bank stability with diversified bank specifics and the country's financial openness. Our paper supplies deep insights for bank managers and macro policymakers in the ASEAN region relevant to the effect of macroeconomic uncertainties on bank stability. Most crucially, experimental results from this research enrich a wealth of knowledge about the regional banking industry's stability; they are the basis for the establishment of bank strategies and macroprudential policies geared to managing the increased macro-unpredictability.

Does US monetary policy uncertainty affect returns of Asian Developed, emerging, and frontier equity markets? Empirical evidence by using the quantile-on-quantile approach.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.

Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models.

Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named "CovCXR-Net", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named "MDCXR3-Net", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named "MDCXR4-Net", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.

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.

Green innovation imperative for natural resource-driven sustainable economic recovery: Linking rights Structure, corporate social responsibility, and renewable energy contracts.

This study examines the complex relationships necessary for a sustainable economic recovery, focusing on the interplay between contracts for renewable energy, natural resource use, corporate social responsibility (CSR), and rights frameworks. Motivated by the increasing scrutiny of environmental practices, this research aims to highlight the need for sustainable business models during the transition to a more environmentally sensitive economy. The study area encompasses diverse sectors where CSR goals can be aligned with renewable energy project frameworks through natural resource utilization. Methodologies include a novel composite CSR evaluation indicator designed to complement industry rankings and a thorough analysis of CSR within the mining industry. Results demonstrate how aligning CSR with renewable energy initiatives can reshape profit models for stakeholders and emphasize the changing green product market as a catalyst for economic resurgence. Recommendations in the area of policies focus on the reasoned utilization of natural resources and the application of innovations following the principles of CSR. This research provides critical guidance to relevant authorities and institutions charged with ethical responsibility, ensuring the proper utilization and implementation of renewable energy sources to create a more ecological future based on green technology and sustainable resource management.

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.

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.

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.

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.

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.