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The aim of this paper is to investigate and to measure the efficiency of Islamic banks through a comparative study with their conventional counterparts during the coronavirus period for the case of MENA region.

Indeed, this study will use the parametric method for a panel of 92 banks, including 27 Islamic banks and 65 conventional banks, over a ten-year period (2012–2021) and from eight MENA countries, namely, Bahrain, Egypt, Jordan, Kuwait, Qatar, UAE, Yemen and Tunisia.

The findings show that Islamic banks are more profitable than conventional banks before and during Covid-19, this result can be explained by the effectiveness of Shariah principles, differences in cost control, management and resource allocation. In addition, this study found that conventional banks outperformed Islamic banks after Covid-19.

This is a recent empirical study that investigates a timely and important topic.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

This study examines the impact of financial technology (FinTech) on bank performance employing data from the United Kingdom (UK) banking sector for a period spanning from 2010 to 2019.

This study employs static as well as dynamic panel data regression analysis to assess the impact of FinTech on the profitability of UK banks.

The results show that FinTech firms positively impact bank performance. For every new FinTech firm introduced into the UK market, net interest margin (NIM) and yield on earning assets (YEA) increase by 6.385 and 3.192% of their sample means, respectively.

Cooperating with FinTech firms, UK banks can broaden their portfolio of financial services offered to their customers and optimize their profit margins.

This is the first study that examines the impact of FinTech on bank profitability employing data from a developed market.

The rapid growth of Fintech presents a growing challenge for banking institutions, particularly those with more traditional, service backgrounds. This paper aims to examine the relationship between Fintech innovation and bank performance by exploiting novel Chinese market data.

Guided by the work of Dietrich and Wanzenried (2011, 2014) and Phan et al. (2019), the authors construct a regression model to investigate the effect of Fintech innovation on the profitability of Chinese listed banks. The authors include their measures of Fintech innovation in each of their selected structures.

Results indicate that Fintech innovation is negatively associated with bank performance and that state-owned banks, joint-stock commercial banks and long-established banks are more negatively impacted by Fintech innovation relative to city and rural commercial banks and younger banks.

Risk tolerance levels, internal structure and efficiency and recent debt repayment performance channels are each shown to be significant, robust explanatory factors underpinning such results.

This study aims to estimate the amount of money laundering (ML) with multiple proxy approaches and measure the effects of ML on various indicators of the economic and financial sectors. Theoretical justifications are recruited from the parasite theory of organised crime.

A quantitative research methodology was used on a balanced panel data set to test the study’s hypothesis through generalised method of moment (GMM). The study sample consisted of 77 countries, and the data was collected for 15 years (2005–2019).

A study has found that 1.23% of global gross domestic product is laundered yearly, and there is no noticeable decline in ML activities. Further study has also found that ML has devastating effects on countries, government revenue, foreign investment, economic development, political and peace conditions, bank liquidity, interest rate volatility and exchange rate volatility. The study has not witnessed the negative consequence of ML on countries’ inflation rates.

Estimates of the study guide policymakers about the volume of resources fleeing and helps them to decide the level of response needed. Further findings help them prioritise the response system according to the area most affected.

This study is an original contribution by the authors and has studied the effects of ML by computing the amount of ML by four different proxies.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

Sudden and unannounced policy changes by the government that provide banks with windfall deposits creates a challenge in terms of resource deployment. In the process, there is an impact on their risk and returns. Using data on domestic Indian commercial banks, this study aims to examine the impact of such an announcement – the 2016 demonetisation episode – on bank behaviour.

Using data on domestic Indian commercial banks during 2010–2020, the paper investigates the effect of a sudden and unannounced policy change on their risk and returns. Using the demonetisation undertaken in November 2016 as a natural experiment, the paper applies the difference-in-differences methodology to tease out the causal impact.

The findings reveal a decline in risk and an increase in returns of state-owned banks, consistent with a flight-to-safety. The response differed in terms of market and accounting measures and across state-owned banks with differing levels of capital and asset quality.

Although several aspects of the demonetisation episode have been well analysed, its impact on banks – the main conduits of the exercise – and in particular on their risk and returns, is an unaddressed area of research. Viewed from this standpoint, this is one of the early studies to undertake a comprehensive empirical analysis on this aspect.

The aim of this study is to conduct a comparative analysis between Islamic and conventional banks in terms of whether Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. It also examines the role of capital in improving the performance and stability within the two banking systems.

This study uses 82 banks from MENA (Middle East and North Africa) region for periods across 2011–2020, and employs a dynamic panel data approach to examine the resilience within both banking systems during the Covid-19 pandemic.

The results show that the Covid-19 pandemic has a negative impact on conventional banks' stability. However, Islamic banks performed better and were less risky than conventional ones. Banks with high-quality capital are more effective at controlling their risks and improving their performance during the pandemic.

The results offer important financial observations and policy implications to many stakeholders engaging with banks. Actually, the findings of this study facilitate to the stakeholders and bankers to have an alluded picture about determinants of risk and performance. The results can be used by bankers’ policy decision-makers to improve and enhance their consideration for risk management, taking into consideration the type of banking systems.

Compared to the various studies on the stability of Islamic and conventional banks, researchers have not sufficiently addressed the effect of the Covid-19 pandemic on risk and performance. Moreover, none of these studies has examined if Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. This leads the authors to identify the similarities and differences between two types of banks in the MENA region in a pandemic shock context.

The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance.

Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors.

The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter.

This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.

This paper empirically investigates the relationship between financial availability and crime by measuring it across five dimensions: banking, securities, insurance, private lending and digital inclusive finance.

The study utilizes 2011–2017 data from prefecture-level cities as a representative sample. Moreover, these findings remain robust after addressing endogeneity through the use of the historical distance between cities and the railroad network as an instrumental variable.

The findings demonstrate a significant negative relationship between financial accessibility and crime rates. Heterogeneity exists in the inhibitory effect of different types of financial accessibility on crime, with banking finance exhibiting a stronger inhibitory effect compared to private lending. Areas affected by natural disasters and infectious diseases exhibit a stronger inhibitory effect of financial accessibility on crime rates, particularly in areas with severe shocks of natural disasters and epidemics. This effect is attributed to the low financing threshold and easy access to private lending, which plays a more effective role than bank finance when people face extreme risks.

There should be stricter regulations imposed on private lending markets and the introduction of more rational legislation aimed at guiding a healthy development within these markets; such measures serve as effective and complementary means for individuals from all walks of life to access credit financing.

The regulation of financial resources by the government should always prioritize ensuring the accessibility of financial policies to cater to the needs of the majority population.

This study is for the first time in an emerging economy context, the causal relationship between financial accessibility and crime. To provide a more comprehensive measure of financial accessibility in a region, this paper proposes a five-dimensional methodology.