The sensitive nature of interest rate has spurred empirical interests on the factors that determine it. This paper examined the joint impact of exchange rate and foreign direct investment inflows on interest rate in Nigeria over the period from 1981-2022. The study made use of the auto regressive distributed lag (ARDL) bounds test and results of findings reveal that in both the short-run and the long-run the impact of the interaction between FDI inflows and exchange rate on real interest rate was negative and significant. Also, while credit to the private sector was found to have significant positive impact on real interest rate in both the time horizons, the impact of consumer price index was negative only in the short. Oil revenue was found to impact positively on real interest rate only in the long-run, but the impact of broad money supply was negative and significant in both the short-run and the long-run. The study suggests that policies to regulate domestic interest rate should focus on joint regulation of both exchange rate and FDI inflows and this requires a synergy between monetary and fiscal policies.

Excessive volatility in financial markets can disrupt economic activity, affect investor and consumer confidence, and potentially lead to financial crises in an economy. Due to this backdrop, this study examined the link between asset class volatility and the output gap in Nigeria. The asset classes were categorized into stock, crude, gold, and bitcoin. The study adopted the GARCH and Bayesian VAR approach and found that all share index has an initial negative impulse with output gap while other asset classes have a positive impulse on output gap. The outcome of this study revealed to both policymakers and economists the potential risks and vulnerabilities of asset class volatility in the economy. Based on this result, recommendations are made amongst which is the strengthening of the Nigerian stock market to help with the inflationary pressures this is because the Nigerian stock market hurt the output gap also, the government should prioritize investing in crude, gold, and bitcoin to push the actual output to full capacity, which brings about employment.

This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as Oil and Financials provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

We explore the connectedness and portfolio implications between Islamic and conventional bonds in global and GCC regions. We also compare which bonds performed better during our sample period. Unlike previous studies, we focus on Islamic bond markets compared to their conventional counterparts and highlight the GCC bonds (Islamic and conventional) in respect of global bonds. We apply the DCC-GJR-GARCH (1,1) method, the Sharpe ratio, and the portfolio implications strategy over the period from September 1, 2013, to February 23, 2022. Our time-varying results suggest that the relationship among all variables varies over time, but most of them are positive, suggesting that there are fewer diversification opportunities between Islamic and conventional bonds. Hedging and diversification benefits are found only in the limited period among these variables, especially between GCC bonds and global bonds, and global Sukuk and GCC Sukuk. The findings of risk-adjusted returns reveal that Islamic bonds outperform their conventional counterparts. Moreover, mixed results are found in the case of hedging costs, and the majority of the fund, based on optimal weights, should be invested in Islamic bonds. Our study endows investors and regulators in the global and GCC markets with new insights on how to shield their investments and the financial system from financial crises through a hedging strategy with Islamic finance.

On the basis of the background of the global green development wave, this paper studies the mechanism between ecological value transformation and urban-rural common prosperity as well as selects 24 data indicators in relation to ecological value transformation, i.e., green development level from 2008 to 2017, calculate the comprehensive score of China's green development level by entropy method, measures the gap between the rich and the poor between urban and rural areas by urban-rural income ratio, and using correlation analysis, she examines the relationship between green development level and the urban-rural income ratio. Moreover, through empirical analysis, there is a significant negative correlation between the level of green development and the ratio of urban to rural income, according to the findings. The improvement of the level of green development will assist narrow the income gap between urban and rural residents as well as achieve common prosperity.

Historically, increased credit union competition in Idaho and Montana has caused commercial banks to offer higher deposit rates to savers and lower loan rates to borrowers. Data are collected for the second quarter of 2018 to examine whether that pattern still holds true. Unlike prior studies, empirical results indicate that credit union competition no longer exerts statistically reliable impacts on deposit rates or loan rates in this northern Rocky Mountain region of the United States. Potential contributing factors include bank and thrift consolidation in recent years, the low interest rate environment prevailing during the late 2010s, and greater emphasis on non-interest forms of intermediary competition in the banking markets that comprise this regional economy.

In the context of the continuous deepening reform of the foreign investment negative list system and the increasing pressure on financial risk prevention and resolution, it is of great significance to study the impact of the foreign investment negative list system on banking risks. This study is conducted on the basis of systematically sorting out the mechanism of the foreign negative list system affecting the risks of domestic banks. Based on the panel data of 27 provinces (cities, districts) in China from 2006 to 2016, and comprehensively adopting the synthetic control method, the difference-in-difference (DID) model, mediating effect model, and other methods, this article systematically examines the impact of the foreign negative list system on Shanghai banking risks. The study found that the implementation of the foreign investment negative list system has significantly reduced the credit risk of the Shanghai banking industry, and this reduction effect has gradually strengthened over time. Additionally, the implementation of the foreign investment negative list system will also significantly reduce the liquidity risk of the Shanghai banking industry; However, the effect of this positive influence is short-lived and subsequently weakens. The analysis of the mechanism of action indicates that the foreign investment negative list system primarily reduces the risk of Shanghai's banking industry by stimulating the inflow of foreign capital and reducing government intervention. However, the effect of improving the level of the rule of law to mitigate commercial bank risk is not apparent. Regarding other influencing factors, the growth of local government financial resources and the increase in new loans have significantly reduced banking risks, while the rise in residents' leverage has substantially increased the level of banking risks.

This study investigates the impact of inventor CEOs on firm performance using a manually collected database of firms' inventors. Our findings reveal that, on average, firms with inventor CEOs experience a one- and two-percentage-point increase in ROA and ROE, respectively, compared to firms with noninventor CEOs. To address potential endogeneity issues, we employ turnover analysis, an instrumental variable approach, and propensity score matching. The estimation results suggest that inventor CEOs significantly enhance firm performance by fostering innovation and total factor productivity. This research contributes novel evidence on the relationship between inventor CEOs and firm performance.

Overall development of a country largely depends on the economic policy instruments particularly fiscal and monetary policy to streamline the development and continue the developmental progress. These two policies have significant effects on long-term growth. It is noticed that policy adoption and reforms in both fiscal and monetary policies undertaken by Southeast Asian nations during the 1960s through 1990s have contributed to their advancement. This paper discusses the strategies for flourishing as emerging economies. Examples from Singapore, Thailand and Vietnam are highlighted in this study. It is found that prudent fiscal and monetary policy, effective discounting and interest rate; modernized tax system and most importantly policy regime are the contributing factors of these emerging economies. However, in spite of high-income growth and development because of supportive these policy initiatives, administrative and politico-economic constraints challenged the path of economies. Long-term development strategies are suggested to sustain the growth and continue the development pace.

Calculating the amount of regulatory capital to cover unexpected losses due to operational events in the upcoming year has caused problems because of difficulties in fitting probability distributions to data. It is consequently difficult to judge an appropriate level of capital that reflects the risk profile of a financial institution. We provide theoretical and empirical analyses to link the calculated capital to the sum of losses using appropriate statistical approximations. We conclude that, in order to reasonably reflect the associated risk, the capital should be approximately half the sum of losses, with a wide bound for the ratio of capital to sum.

How does democracy relate to the initial economic policy responses to Covid-19? Using a cross country analysis, we find that countries with a higher degree of democracy have stronger economic policy responses than their peers. However, when we separate monetary and financial policies from fiscal policy, democracy is not associated with the latter when we control for the income level of a country. Finally, for countries with lower levels of labor participation, high levels of income inequality are associated with weaker policy responses.

In the context of digital economy, Hebei Province, as an important province in central China, has a huge manufacturing base and potential, and the development of digital economy has driven the transformation and upgrading of manufacturing industry to a certain extent. In order to better play the driving role of digital economy in the transformation and upgrading of manufacturing industry, this paper establishes an intermediary effect model based on three intermediary variables: enterprise resource allocation ability, enterprise cost and enterprise innovation ability, and conducts an empirical study on the panel data of 11 prefecture-level cities in Hebei Province from 2017 to 2022. The mechanism of digital economy driving manufacturing upgrading in Hebei Province was discussed. The results show that: (1) there is a significant positive correlation between the development of digital economy and the transformation and upgrading of manufacturing industry, which indicates that the development of digital economy has a direct driving effect on the transformation and upgrading of manufacturing industry; (2) There is also a positive correlation between the development of digital economy and the resource allocation ability and innovation ability of enterprises, indicating that the development of digital economy can improve the resource allocation ability and innovation ability of enterprises, and is conducive to the stable development of enterprises in the future; (3) Enterprise resource allocation ability and enterprise innovation ability play a significant intermediary role in the relationship between digital economy development and the transformation and upgrading of manufacturing industry, indicating that digital economy can indirectly accelerate the transformation of manufacturing enterprises' R & D achievements by improving enterprises' resource allocation ability and innovation ability, thus improving enterprises' market competitiveness and increasing enterprises' earnings. Drive the transformation and upgrading of the manufacturing industry. Based on this research conclusion, the government should give full play to the role of guidance and support, and introduce relevant policies to help the digital economy drive the transformation and upgrading of manufacturing enterprises. Manufacturing enterprises should seize the tide of the development of the digital economy, use digital technology to improve their resource allocation ability and innovation ability, enhance core competitiveness, and accelerate the transformation and upgrading of enterprises.

Metadherin (MTDH), also known as LYRIC or AEG-1, is an oncogene that enhances tumor progression, metastasis, drug resistance, and immune escape in various cancers by modulating multiple oncogenic pathways, including NF-κB, PI3K/AKT, Wnt/β-catenin, MAPK, and AMPK. Due to the unknown of the complete structure of MTDH, the deep mechanisms of MTDH and selective inhibitors targeting MTDH remain to be explored. The Protein-Protein interaction (PPI) with the Staphylococcal nuclease domain containing 1 (SND1) is a crucial mechanism underlying the function of MTDH. Current studies have demonstrated that inhibitors, including antisense oligonucleotides, peptides, and small molecules targeting MTDH or MTDH-SND1 interactions, provide novel strategies to inhibit the oncogenetic effects of MTDH. This review summarizes and discusses the structure, function, and regulation of MTDH in cancers, providing the potential therapeutic perspectives of MTDH or MTDH-SND1 PPI for drug discovery.

Non-compliance with tax obligations, particularly by wealthy taxpayers and large corporations, continues to represent a source of social injustice that fuels social instability, especially in developing countries. Understanding the strategies of tax evasion and avoidance is of critical importance. This article highlights five main strategies used by large corporations and wealthy taxpayers to avoid taxes, including tax havens, the underground economy, aggressive tax optimization, alternative financial markets, and crypto currencies. We also propose several actions to tackle global tax non-compliance, including prevention, peer reporting, active monitoring of compliance indicators and inter- national cooperation. These actions can be combined to achieve optimal results in reducing tax evasion and avoidance.

Regulation of financial institutions has two key purposes: Solvency (prudential regulation) and consumer protection. Prudential regulation is implemented mainly by capital requirements, but governments also provide insurance for customer deposits, as a backup tool. In this article, we discuss the critical conditions for deposit insurance and capitalization to act as substitutes for each other, under cyclical economic environment. We make two assumptions. The first one is that deposit insurance is fairly priced and there is no moral hazard. The second one is that insurance creates incentives for moral hazard among insured banks, resulting in increased risk taking. We also discuss the critical conditions for deposit insurance and capitalization to be complementary under different proportions of deposit insurance.

With the improvement of people's living standards and the increasing emphasis on health, new and higher demands are also placed on the medical industry. The diagnosis and treatment methods combining artificial intelligence and medicine are bound to receive more and more attention and promotion. At present, drug design is stepping into the era of artificial intelligence, and academia and industry are actively applying artificial intelligence technology to all aspects of drug research and development, including drug screening, molecular characterization, drug activity and druggability optimization, chemical reaction design, etc. Relying on the big data information of millions of patients, the artificial intelligence system can quickly and accurately mine and screen out suitable drugs. Through computer simulations, artificial intelligence can make predictions about drug activity, safety, and side effects, and find the best drug that matches the disease. This technology will shorten the drug development cycle, reduce the cost of new drugs and improve the success rate of new drug development.

This article discusses the pressing need to integrate artificial intelligence (AI) into education to facilitate customizable, individualized, and on-demand learning pathways. At the same time, while AI has the potential to expand the learner base and improve learning outcomes, the development of NACE Competencies and durable skills – communication, critical thinking, creativity, leadership, adaptability, and emotional intelligence - must be purposefully integrated in curriculum design now more than ever. Recent studies have shown that AI-driven learning pathways can achieve outcomes more quickly, but this comes at the cost of the development of durable skills. Therefore, traditional student-to-student and student-to-teacher interactions must be prioritized. As such, this study proposes a balanced approach to curriculum design to ensure the best outcomes for learners, where durable skill development is prioritized alongside subject-specific skills and rote memorization. Additionally, the article highlights the need for a combination of Just in Time Training (JITT) approaches, facilitated by AI technology, to reach the implementation of durable skills. The article concludes by questioning how to develop human skills in an increasingly AI-driven education system and emphasizes the importance of curriculum design and traditional learning approaches in creating a cohesive learning experience that develops durable skills in students. It is necessary to recognize that AI-driven education cannot replace the development of human skills, and that traditional interactions play a crucial role in developing these skills.

Investment funds are increasingly focusing on ESG issues, seeking to contribute to improving environmental, social, and governance concerns. ESG-focused investing involves promoting good ESG performance and may result in a decrease in expected financial returns. This may lead to a conflict between solely profit-focused shareholders and ESG-focused investors, who may respond accordingly. As such, a firm's ESG commitment may trigger several events that can affect the stock price. A firm's ESG commitment may increase its stock price; however, the end of such commitment may also positively affect the stock price. Hedge funds may consider this in their activism which may reshape the way hedge funds target firms and influence their actions accordingly.

This study explores the cause and effect of endogenous risk aversion in land pricing, where state intervention through taxation remains a general practice. Using a consumption-based asset pricing model incorporating taxation, it is shown that high taxation, due to the indexation effect, supporting land prices and reducing individuals' risk expectations, could lead to an endogenous decrease in risk aversion, which could result in market dysfunction because risk aversion plays a key role in the market mechanism. China, with its wholly state-owned land and the general use of land sales to cover financial deficits, is a typical case for empirical tests. The tests confirm that there, the rise in land prices was driven by the increase in reserve prices set by local governments, a strong means of taxation, and not by the market, indicating the endogenous decrease in risk aversion.