The current global financial sector is dedicated to providing customers with more advanced and strategic services. With channels like internet banking, mobile phone banking, and telephone banking, the industry delivers multi-channel banking services to satisfy its clients. The COVID-19 pandemic has directly impacted the global economy, as well as the financial sector, with the banking sector facing lockdown initiatives to curb the virus's spread. As a result, mobile payment methods have been favored by people over physical money for transactions. This study analyzes the factors that influenced mobile banking usage during the pandemic in Sri Lanka.. Convenience sampling was used, comprising of 200 online questionnaires conducted among customers in Sri Lanka. The collected data was analyzed using descriptive analysis, exploratory factor analysis, and multiple linear regression through IBM SPSS 25. Research findings revealed that trust and risk were significant factors affecting mobile banking usage in Sri Lanka. Trust had a positive effect while Risk negatively effects to the mobile banking usage. Ease of use and Usefulness were irrelevant factors during the COVID-19 pandemic in Sri Lanka.

Clarifying the impact mechanism of digital transformation of enterprises on the M&A efficiency of listed companies can provide a factual basis for improving capital market regulatory policies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing of stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market.

This study investigated the impact of sustainability reporting on financial performance, with a focus on companies in the Turkish food, beverage and tobacco and textile, wearing apparel and leather sectors. The sustainability reports of 48 companies listed on the Istanbul Stock Exchange for 2022 were studied, and the quality of sustainability practices was determined by using a general index (Sustainability Reporting Disclosure Quality Index (SRDQI)) and three partial indices (Environmental Disclosure Quality Index, Social Disclosure Quality Index, and Corporate Governance Disclosure Quality Index (CGDQI)). To analyze the relationships between financial performance and sustainability practices two types of regression models were developed, based on which eight models were directly examined. The results indicate the complete absence of a statistically significant impact of SRDQI on all financial performance measures used. Among the partial indices, only CGDQI has a significant positive effect on the Assets Turnover Ratio. An analysis of the influence of control variables shows a multidirectional dependence of individual financial performance measures on the size of companies, their age, industry affiliation, as well as on the structure of capital used. Finally, this study provides directions for improving the institutional environment of sustainability reporting for Turkish companies.

Using annual panel data of 46 listed commercial banks in China from 2012 to 2021 and constructing a two-way fixed-effects model, this study empirically analyzes the competition and technology spillover effects of fintech on the profitability of commercial banks. The results show that (1) in the early stages of fintech development, the competition effect is larger than the technology spillover effect; thus, it is negatively correlated with commercial banks’ profitability. However, with the spread of innovative fintech, the technology spillover effects and commercial bank profitability will gradually improve. (2) The influence of fintech on the profitability of commercial banks differs. Compared to large commercial banks, fintech has more significant negative effects on small- and medium-sized commercial banks in the short run. However, the role of fintech for such banks will also grow in the future. The results of this study provide practical guidance for how commercial banks can respond to the fintech wave. To realize sustainable development of the banking industry, commercial banks should change their business philosophy and revenue model, vigorously improve their fintech innovation capability, differentiate their choice of fintech development routes, develop personalized customization with a focus on users, and ultimately realize digital transformation and upgrading.

Having effective and efficient financing is one of the most important steps in accelerating the development of public infrastructure, including toll roads. This study aims to identify Critical Success Factors (CSFs) for the implementation of toll infrastructure financing in Indonesia. Thirty-three CSFs have been identified from literature review. A Delphi survey involving a panel of experts working in the infrastructure industry was conducted. Based on the survey, it is known that internal rate of return, affordability, investment decision, commercial banks, financing costs, interest rate risk, control of cash flow, contract scope, and principles of risk transfer are important factors for the implementation of toll infrastructure financing in Indonesia. This study fills research gaps by developing a CSF model for successful toll road infrastructure financing in Indonesian PPPs, considering private perspectives and aiming to provide insights for investors and enhance understanding of country profiles in developing countries. The focus on toll road implementation in Indonesia contributes to a comprehensive understanding of CSFs for PPPs in the country.

This study examines the relationship between firm-level and macroeconomic variables and stock prices (SP) in Iran, Saudi Arabia (KSA), and Iraq, where economic conditions are uncertain. The sample consists of all firms listed on these countries' stock exchanges between 2015 and 2019, yielding a comprehensive dataset from 154 Iranian firms, 82 KSA firms, and 33 Iraqi firms. Using a fixed effect model, intriguing relationships are uncovered between firm-level and macroeconomic factors on SP. The findings indicate that, at a 1% significance level, there is a strong link between Iran and Iraq's GDP and their respective SP. In KSA, the association between GDP and SP is positive and significant at a 5% level. In addition, there is a positive and statistically significant correlation between the inflation rate and SP in all three countries. Moreover, the association between exchange rates and SP is significant and positive in KSA and Iraq but negative and significant at the 1% significance level in Iran. Furthermore, in KSA, the connection between oil prices and SP is positive and significant, whereas, in Iran, the relationship is negative at the 1% level. Surprisingly, there is no association between the price of oil and Iraqi SP. Regarding intangible assets, their impact on SP in Iran and KSA is negligible. In Iraq, however, there is a negative and significant relationship at the 5% level, indicating that intangible assets have a detrimental impact on SP. These findings highlight the complex link among macroeconomic factors, intangible assets, and SP in the examined countries. Policymakers and governments should prioritize implementing measures to foster sustainable economic growth, manage inflation, and stabilize exchange rates. These actions can boost investor confidence and contribute to the stock market's overall development.

Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return μ, implied natural upturn probability p, and implied daily volatility σ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders.

The growing concerns on sustainability urge insurance companies to incorporate Environmental, Social, and Governance (ESG) policies in order to remain competitive. As all dimensions of sustainability involve taxation, it is important to establish if this association reflects on financial performance. Our analysis of worldwide property and casualty (P&C) insurers during 2013-2022 reveals that high ESG insurers pay more taxes, while are less profitable compared to low ESG insurers. This pattern is confirmed using instrumental variable regressions and simultaneous equations systems. We argue that sustainable insurers are less tempted to avoid taxes, and don't shift their tax burdens on policyholders and investors. However, the interplay between taxes and sustainability seems to harm insurers' profitability, potentially sorting negative consequences on investment and economic growth. This is an important insight for tax authorities and insurance managers.

This study aims to identify the model that best approximates the credit spread that should be fixed on debt instruments issued by both public and private companies, considering the particularities of the Mexican market. Five models were analyzed: Merton's [1], those proposed by Denzler et al., the one presented in this paper, which includes the conformable derivatives, taking as a reference the change of variable made by Morales-Bañuelos et al., and the Corporate Default Risk Model (DRSK) for Publics Firms of Bloomberg [4]. The required financial information was obtained from the Bloomberg platform, from which the probabilities of default, credit spreads, and credit ratings for each company in the sample under the model (DRSK) were extracted directly. Likewise, the program developed by Moody's K.M.V. was used to obtain the EDF (Expected Default Frequencies). It was concluded that the Modified Merton model approximates to a greater extent the credit spreads that fix on a prime rate on the loans granted to the Mexican non-financial companies.

With the deepening of the diversification and openness of financial system, financial vulnerability, as an endogenous attribute of financial system, becomes an important measurement of financial security. Based on network analysis, we introduce network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with previous network curvature analysis method, CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages.

The rapid ageing of populations around the World is creating complex challenges for national governments. The establishment of sustainable and equitable long-term care systems for old and dependent people is one of the main issues of social policy in developed countries. The aim of this work is to define a cost model for residential and day care centres for dependent persons in Cantabria (Spain). The cost model will make it possible to establish the theoretical cost of attending to the needs of the different types of dependent persons in the different types of care centres, and the methodology used could be extrapolated to other regions. The daily cost per user for elderly residential care is €53.72. The cost per user in elderly day centres (5 days) is 32.56 euros. In residential centres for people with disabilities, the values range between €47.41 and €75.25 depending on the category of the centre. In three categories of centres the public price is not enough to cover the cost (physical disability, intellectual disability, mental illness – low care), and therefore the administration should reconsider their public prices for these kind of centres if they want to really contribute to the sustainability of these residential care centres. This research will have important implications for policy-makers in a context of fulfilment of SDGs and where better support for old and disabled people and their carers, as well as fair and efficient financing of social care services, are essential to address the current and future challenges of dependency.

The perceived poor performance of publicly traded companies on their sustainability commitments and the quality of sustainability reporting has prompted stakeholders to consider the economic, environmental, and social impacts of corporate activities. Economic activities have led to various threats in the form of climate change, pollution, greenhouse gas emissions, natural disasters, and other issues that have negatively impacted the environment and stakeholders. Companies are expected to report to stakeholders on their sustainability performance, but reality proves that present reporting falls below stakeholders’ expectations mainly due to its still voluntary nature. The present study aims to provide a literature review of the relationship between sustainability reporting and the role of companies governance, especially observing if climate change requirements and energy-needed changes are being accounted. Results highlight mixed evidence for the influence of board governance attributes, providing interesting insights for research advancement. The study has practical implications for businesses, regulators, governments, and other stakeholders in their policy deliberations and investment decisions. Further empirical studies are recommended to re-examine sustainability reporting using the variables identified as important factors and gaps in this study and other board characteristics to improve the generalizability of the results.

Inflation in 2021 and 2022 grew much faster than the Federal Reserve expected. The Fed downplayed inflation in 2021 and then increased the federal funds rate by 500 basis points between March 2022 and May 2023. This paper investigates how this unprecedented tightening impacted the stock market. To do so it estimates a fully specified multi-factor model that measures the exposure of 53 assets to Bauer and Swanson (2022) monetary policy surprises over the 1988 to 2019 period. It then uses the monetary policy betas to gauge investors’ beliefs about monetary policy between 2020 and 2023. The results indicate that changing perceptions about monetary policy multiplied uncertainty and stock market volatility.

This study delves into the integration of Decentralized Finance (DeFi) within metaverses, probing its implications and potential. It elucidates DeFi protocols and the intricacies of metaverses before inspecting their intersection and the resulting economic prospects. The analysis further appraises inherent risks such as financial volatility, security, and regulatory issues. Case studies provide tangible insights into DeFi applications in metaverses. Future trajectories of DeFi in metaverses are projected, underlining the possible impact on the broader financial sector. This paper contributes to burgeoning research at the nexus of blockchain technology, finance, and virtual reality.

This study explores the correlation between payment statistics and sector-wise gross value added (GVA) in the Indian economy from 2011 to January 2023, shedding light on the importance of payment and settlement systems. Through regression analysis, the impact of payment statistics on GVA across diverse sectors is assessed. The results indicate that, during the specified period, payment statistics do not exhibit a significant influence on sector-wise GVA. These findings emphasize the need for further investigation into alternative factors driving sector-wise GVA, while underscoring the pivotal role of payment and settlement systems in the Indian financial landscape.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

Insurers act as institutional investors and underwriters of risk, therefore improving their own environmental, social, and governance (ESG) performance is important for the transmission of ESG values to all other economic sectors. We analyze ESG scores of worldwide Property and Casualty (P&C) insurers during 2012-2022, and show that more sustainable insurers have high operating leverage, although their combined ratios and z-scores reveal that they are financially stable. Additional results for the US subsample illustrate that stocks issued by sustainable insurers deliver positive excess returns. Overall, these findings suggest that sustainable practices are associated with the ability of insurers to execute business and create value. This is important for insurance managers, investors, and policy makers, as insurers play a prominent role in promoting economic growth and stability.

The current study explores the problem of illiquidity in the corporate bond market and examines a broad set of liquidity proxies through empirical investigation. Purpose: The purpose of this paper is to give solution to the global issue of illiquidity in the corporate bond market. The problem is identified by many researchers and this paper attempts to find out the viable solution of the "fungibility route" as an alternative to the "liquidity route". Design/ Methodology: An Analysis of a sample size of 2,34,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is done to identify the problem. Findings / Solution proposed: A mathematical model based on credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be swapped by a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated risk adjusted discount rate using modified duration and credit risk differential. With our solution, it is possible to enhance the liquidity of the bond not directly but through fungibility route. Originality: the research is first of its kind in which solution is given to the global problem of corporate bond illiquidity. To execute the trade in the fungibility route, proposed parties involved are the Fungibility platform, the initiator, and the bidder. The derived fair value would facilitate trade and serve as an exit strategy for illiquid bond holders worldwide.

Microfinance banks and Institutions must simultaneously provide micro financial loans to unprivileged and poor people as well as self-sustaining, which means covering enough costs to eliminate the need for subsidies. To ascertain if Micro-finance Institutions can successfully navigate the double challenge of financial self-sustainability (FSS) as well as outreach to poor clientele and women borrowers i.e. outreach. There is no other alternative but to analyse the balance between FSS and outreach indicators of MFIs. The research goal is to see whether there was any compromise between the self-sustainability and outreach towards poor and female customers. The study used data of 100 MFIs driven from the database of microfinance information’s exchange (MIX) market with the objective to determine trade off between financial sustainability and outreach. The study found financial performance variables are positively and significantly related with average loans size which shows a mission drift, in which MFIs serve wealthy clientele. However, Indian MFIs have a extremely high outreach with their female clientele, confirming MFI's social commitment to objective of women’s empowerment. The research recommends to the Policy-makers that MFIs are compromising their financial services to underprivileged people and women in order to be financially sustainable. To guarantee that institutions are focused on outreach to underserved people in rural areas, the government should reform the policies regarding governing MFIs.

Research background – In the conditions of a modern developed market economy, a rather important characteristic of the activities of a public company is its activity in the stock market (market activity), which involves the development of a dividend policy, which, on the one hand, should contribute to the achievement of the main goal of financial management - maximizing the material well-being of shareholders, and, on the other hand, take into account the interests of all other stakeholders interested in the activities of a public company (including potential investors) to ensure its sustainable development in the long term. Fulfillment of this requirement is possible only within the framework of a neutral approach to the implementation of the dividend policy of a public company.Purpose – The article is devoted to the consideration of the conceptual foundations for analyzing the sensitivity of key indicators of the activity of a public company in the stock market (market activity) to the main determining factors in the context of a neutral approach to the implementation of its dividend policy.Design/methodology/approach – The methodological basis of the study was the concept of a neutral approach to the dividend policy of a public company and the concept of sensitivity analysis, developed earlier by the author of this article.Findings – It is shown that the key indicators of the market activity of a public company include the dividend yield ratio, dividend coverage ratio, the expected price of an ordinary share, the dividend yield of an ordinary share and the quote ratio of an ordinary share (price and earnings ratio per ordinary share). An analysis of the sensitivity of these indicators to the main determining factors in the context of a neutral approach to the implementation of the dividend policy of a public company involves the construction of appropriate elasticity models that allow determining the change in key indicators of market activity depending on changes in the factors that determine them. The constructed elasticity models of the key indicators of market activity listed above can be used in predictive and analytical assessments of their changes, and also make it possible to identify the causes of these changes by calculating the impact on the specified elasticity of the determinants contained in their models, performed by the appropriate methods of factor analysis, under the conditions of a neutral approach to the implementation of the dividend policy of a public company.Originality/value – The author comes to the conclusion that the elasticity models developed by him for key indicators of the activity of a public company in the stock market in terms of the main factors determining them are a fairly effective tool for managing the market activity of a public company in the context of a neutral approach to the implementation of its dividend policy.

The insurance sector has undergone a transformative shift with the emergence of Insurtech, integrating technological innovations into the industry. This review aims to provide a comprehensive overview of the impact of Insurtech on the insurance sector, focusing on the case study of Lemonade. By placing the study in the context of the broader insurance landscape, the review explores the significance and relevance of this transformation. The article analyzes Lemonade's unique business model and its departure from traditional insurance practices. Furthermore, it examines the implications of Lemonade's approach on the industry and the challenges it faces. Through this analysis, the review sheds light on how Insurtech and the transformation of the insurance sector are redefining insurance services and consumer experiences. The future implications of these trends for insurance companies, consumers, and the industry as a whole are also explored. In summary, this review provides valuable insights into the transformative power of Insurtech, emphasizing the importance of technological advancements and innovative approaches in shaping the future of the insurance sector.

This paper delves into the dynamics of asset pricing within Bachelier’s market model (BMM), elucidating the representation of risky asset price dynamics and the definition of riskless assets. It highlights the fundamental differences between BMM and the Black-Scholes-Merton market model (BSMMM), including the extension of BMM to handle assets yielding a simple dividend. Our investigation further explores Bachelier’s term structure of interest rates (BTSIR), introducing a novel version of Bachelier’s Heath-Jarrow-Morton model and adapting the Hull-White interest rate model to fit BMM. The study concludes by examining the applicability of BMM in real-world scenarios, such as those involving environmental, social, and governance (ESG)-adjusted stock prices and commodity spreads.

The service sector, whose value has started to increase in recent years, has gained momentum with the acceleration of economic developments especially after the 2000s in Turkey. In a world where competition is increasing and social purchase perception is changing, companies started to differentiate in the service sector to create a competitive advantage. Companies that would like to grow in the industry and aspire to a greater proportion need credit to extend. There are many factors to consider in order to allocate the credit. Banks make financial and non-financial analysis to make credit decisions. This study has been prepared to assess the effect of financial and non-financial features of middle segment companies that need credit in the service sector on the credit decision to be allocated by banks. The group of variables that explain the credit decision at the highest level in these companies are non-financial variables.

This paper considers the theoretical framework of the consumption-based asset-pricing model and derives successive approximations of the modified basic pricing equation using Taylor series expansions of the investor’s utility function during the averaging time interval Δ. For linear and quadratic Taylor approximations we derive new expressions for the mean asset price, mean payoff, their volatilities, skewness and amount of asset that delivers max to investor’s utility. We introduce new market-based price probability determined by statistical moments of the market trade values and volumes. We show that the market-based price probability results zero correla-tions between time-series of n-th power of price pn and trade volume Un, but doesn’t cause statis-tical independence and we derive correlation between time-series of price p and squares of trade volume U2. The market-based treatment of the random trade price describes impact of the size of market trade values and volumes on price probability. Predictions of the market-based price probability at horizon T should match forecasts of statistical moments of the trade values and volumes at the same horizon T. The market-based price probability emphasizes direct dependence on random properties of market trades.

We present evidence of the anchoring bias and disposition effect (DE) in investor trading decisions across different asset markets (stable and volatile assets) and market scenarios (stable and volatile markets), as well as how these biases affect investors' portfolio performance. The study employs a quasi-experimental design, allowing subjects to engage in interactive trading with four securities, two with potential negative returns and two with potential positive returns, within a simulated asset market. Our findings reveal the presence of the disposition effect and anchoring bias among individual investors in India. Second, market scenarios or volatility can influence the investors’ behavioral biases. The disposition effect was more prominent in volatile markets and the total market, while the anchoring bias was significant in stable and total markets. Additionally, investors exhibiting the disposition effect exhibit lower portfolio performance, while those demonstrating anchoring bias perform relatively better. The study's findings can help individual investors understand their behavioral biases, avoid them in future trading decisions, and improve portfolio performance. Financial institutions and regulatory agencies could identify investment products and supportive investor interactions towards better market trade decisions.

This study assesses the dynamic relationship between macroeconomic factors and bank asset quality based on changes in the condition of stock market returns. A Generalized Method of Moments (GMM) model is employed, using panel data from 18 universal banks spanning the period 2007 to 2021. The analysis revealed that the real GDP growth rate, the average lending rate, and the real exchange rate represent a set of macroeconomic factors with a marked influence on banks' asset quality. In addition, a high inflation rate was found to exert an adverse effect on asset quality, as it affects borrowers' financial ability to meet loan repayment obligations. Furthermore, the study verified the existence of a positive relationship between market condition and asset quality, which implies that bank performance adapts to changes in market conditions as posited under the Adaptive Market Hypothesis (AMH). Bank managers should consolidate banks' asset bases during conditions of market stability to withstand periodic market fluctuations to boost trading momentum. Policy recommendations are suggested to foster a conducive business environment for bank stability.

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover the news sentiment and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors concern about the daily news by testing the movements of the market before and after the news is released. The analysis results show that there is an insignificant difference in the stock price as a response to the news. However, investors tend to overreact to negative and positive news in the Vietnam stock market.

Feedback trading theory is one of the most primitive theories about financial market. But for a long time，researches and modelings on this topic are rarely seen. The model in this paper shows the effects of sentiment shocks on asset prices in a market characterized by feedback trading in the long run. We find that，generally，feedback trading will lead to cognitive bias effect and trading inducement effect. Cognitive bias effect increases with the feedback trading parameter ( FTP) . In our model，the abnormal volatility of asset prices is captured by cognitive bias effect，sentiment shock effect and trading inducement effect．

Rising raw material prices can have a significant impact on the financial performance of the food and beverage (F&B) business. The purpose of this study was to determine the impact of rising prices of staples onfinancial resilience F&B business in Indonesia. This study uses a quantitative approach with bivariate correlation analysis and secondary data from the financial reports of F&B companies listed on the Indonesia Stock Exchange for the 2020-2022 period. The results showed that the increase in the price of basic commodities had a significant positive impact onfinancial resilience F&B company. This can be seen from the decline in the profitability and liquidity of F&B companies as a result of the increase in the price of basic commodities. Therefore, F&B companies must pay attention to and manage the risk of rising prices of basic commodities to maintainfinancial resilience and sustainable business continuity.

The research analysed the publications metric of literature regarding the impact of socially responsible investing (SRI) and environmental, social, and governance (ESG) on portfolio and financial performance. The study aimed to determine the latest trends, key themes, authors, and influential journals. Data from the SCOPUS database was used in this study. A total of 1377 research articles from academic journals related to this topic and published from 2013 to 2022, they data was cleaned to remove duplicate, and documents that do not meet the criteria of this study. For this bibliometric analysis, a total of 260 articles were chosen in order to summarise the collective knowledge and emerging patterns in ESG, SRI, ethical, and impact investing, a bibliometric analysis was employed. The thematic map classified the ESG, SRI, and performance relationship themes into four categories of themes: niche themes, motor themes (corporate financial performance, corporate social performance, ESG, ESG factors, sustainability, performance, integrated reporting, gender diversity, and board size), emerging or declining themes (social responsibility, environmental performance, socially responsible investment, ethical investment, and SRI), and basic or transversal themes (financial performance, corporate social performance, ESG performance, environmental, social, and governance). Social responsible investing, engagement, and ESG, implies a position between niche themes and highly developed topic/emerging or decreasing theme, and covid, ROA, while, tobin’s Q, implies a position between highly developed topic/emerging or decreasing theme and basic theme. The findings contribute to the enhanced understanding of ESG, SRI, ethical, and impact investing, which are crucial for an efficient capital market in promoting sustainability and sustainable development. The study offers vital practical implications and future research directions.

In real financial markets, the certainty equivalent rate is an important factor for investors to consider. From the value of a specific company’s certainty equivalent rate, investors can judge if this company is worth investing in. In this paper, we will explore how to obtain the value of the certainty equivalent rate so that we can evaluate a given company. First, we will introduce the theoretical support of this paper, which is put-call parity. From put-call parity, we will derive the mathematical expression for the certainty equivalent rate. Second, we will use the mathematical expression for the certainty equivalent rate to perform an empirical study. We will consider some extreme examples, including some of the best- and worst-performing stocks up to this point in 2023. Based on these stocks’ data, we will visualize the performance of these stocks and explain the meaning of the data. Finally, we will apply the results to a very popular industry—the electric vehicle industry—to explore the current electric vehicle market situation in the U.S.A.

This study presents a novel approach to analyzing the present value of total profit for non-participating and participating insurance policies in order to determine the optimal profitability of non-participating and participating insurance policies based on applying the approach used in operations research to the field of finance. As such, a comprehensive insurance product evaluation model was developed using both mathematical models and numerical analysis to evaluate the demand for non-participating and participating life insurance policies in response to changes in interest rates. The findings indicate that non-participating life insurance policies offer greater solvency for insurance companies compared to participating policies. The study also highlights the significance of spontaneous and induced demand in determining the total profit of both types of policies. The study concludes that life insurance companies should focus on generating spontaneous consumer demand, reducing induced demand, and implementing the optimal pricing strategy to achieve maximum profits.

The challenge of accessing finance by entrepreneurs from traditional financial sources is pervasive. The COVID-19 pandemic further exacerbated the problem of limited access to finance from banks. Against this backdrop, the objective of the study was to determine the factors driving crowdfunding success during the COVID-19 pandemic in Africa. The ordinary least squares (OLS) and probit regression models were estimated to analyse 216 crowdfunding projects in Africa. The results of the study documented that targeted amount (TA), and COVID-19 pandemic were negative and significant drivers of crowdfunding success. Furthermore, comments (CMM) and duration (DRN) were negative and significantly affected crowdfunding success. Conversely, images (IM), videos (VD), backers (BCK), and updates (UPD) were positive and significantly affected crowdfunding success. The study contributes to the body knowledge by investigating the drivers of crowdfunding success during the COVID-19 pandemic period which hitherto had not been researched on extenisvely.

Our study shows how the United States government can achieve its goal of Nationally Determined Contribution (NDC) in 2025, 2030, and 2050 by reducing energy consumption through a pure carbon tax. To achieve its emissions reduction goals, it is necessary for the US to impose a long-term carbon tax that balances taxes on labour, capital, energy, and carbon. Therefore, in this study, through the two-layer CGE Cobb-Douglas model, the carbon tax rate is set while balancing the production and profit functions of government, businesses, and households. This study concludes that the carbon price will increase from US$ 0.4391/kg CO2 in 2020 to US$ 2.5671/kg CO2 in 2050 when the CO2 emissions reduction target is increased from 17% reduction in 2020 to 83% reduction in 2050 for the US.

Money launderers prefer to use financial services as the ideal medium to launder. The study aimed to provide an overview of the global AML/CFT regulations, application and how they should evolve in this dynamic environment. To gather more insight, a qualitative study was undertaken with relevant documents analysed. The main findings were: country implementation of the global AML/CFT regulations differed due to political and economic factors amongst others. While the various AML/CFT enforcements done by sampled countries were mainly cease and desist orders and monetary penalties which were publicized; and drawbacks of global AML/CFT regulations centered on application of these regulations and emerging trends. These include among other definitions of money laundering, reference to the three stage of money laundering, link between penalty and violations, technological innovations and regulations paradigm shift, cyber-attacks and data privacy. The study contributes to application and growing body of knowledge in that the advent of technology has resulted in better consumer experiences, new payment platforms, products, and services. However, these innovations have broadened emerging money laundering risks and risks in general to the financial system. Hence the need to conduct research based FATF Recommendations as risk is dynamic and not static.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. The study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020, in addition to macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets, as well as its effect on Tobin's Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a greater Tobin's Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is found to be negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin's Q is positively correlated with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. Eventually, the findings suggest that consistent and transparent economic policies may increase the efficiency of corporate investment.

This paper proposes an optimal trading algorithm based on a novel application of the conventional Control Engineering (CE). We consider a fundamental CE concept, namely, the feedback control and apply it to the Algorithmic Trading (AT). The concrete feedback control strategy is designed in a form of the celebrated Proportional-Integral-Derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realisation of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above makes it possible the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

The purpose of this original study is to critically analyze the emergence and development of the national models of financial regulation; international standards and codes; regional and national financial regulation and supervision (the case of UK, USA, Sweden, EU and Finland). The research raises both academic and regulatory concerns. It aims at shaping changes (innovations) in the supervisory practices of national and international regulators in response to new developments in FinTech markets, digital products, financial instruments and risks. Secondly, it will stimulate more systematic work on regulatory databases, registration and reporting procedures in various economies in different financial markets. The author identifies five main systems of national financial regulatory markets: the multi-tiered, multi-agency US system; the Twin Peaks model (UK); the mega-regulatory model (Sweden). There is a thorough review of international standards and institutions that work for the stability of financial systems. The analysis of regional and national systems of financial regulation and supervision is based on the examples of the EU and Finnish institutions. National macro and microeconomic regulation and supervision have been examined with a focus on the US Federal Reserve and the US Treasury. National and international regulatory institutions have been evolving along several lines. First, minimum capital and credit risk requirements for banks (80s of the 20th century) are complemented in the 21st century by buffer reserves, liquidity and leverage standards. Second, regulation focuses on ensuring the sustainability of the national economy. The regulatory focus is on ensuring the sustainability of national and global financial systems. Third, there is an increase in the number of supervised institutions. Fourth, there is a division of functions between central banks (macroeconomic regulation) and one or two mega-regulators (microeconomic regulation and supervision). Fifth, there is a division of labour between the international financial institutions (BIS, IMF, WB) and national regulators. Sixth, the focus is on protecting consumers and investors, and countering money laundering and the financing of terrorism. Seventh, there is an understanding for a common approach by central banks to new financial technologies and cybersecurity.

This study examines whether and how carbon trading policy impacts the financialization of non-financial firms, using China emission trading scheme as a natural experiment. We show that the carbon trading policy has effectively long-term inhibitory effect on corporate financialization. Our findings are robust to possible result bias and more precise control group. In addition, we explore potential channels through which carbon trading policy can affect financialization, and find that it contributes to curb financialization by reducing financing constraints. Finally, we show that the relationship between carbon trading policy and financialization of non-financial companies is moderated by company’s ownership, region and industry competition.

In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

This study utilized panel data from 31 Chinese provinces over a period of nine years to investigate the impact of green finance on the upgrading of the agricultural industrial structure. A fixed-effect model was employed, and the findings indicate that green financing has a positive effect on the growth of China's agricultural industry. However, regional disparities exist, particularly in the uneven distribution of green financing across the eastern, central, and western regions. Moreover, it emphasizes the need to consider regional differences and tailor development strategies accordingly. To promote further development and transformation of China's agricultural industrial structure, the study recommends innovative green financial products, improved regulations and policies, and the integration of digital technologies.

Microfinance institutions (MFIs) play an significant role in financial inclusion and poverty alleviation activities. A critical challenge facing MFIs is how on all to build its own capacity for sustainable and healthy development. By using the data from the pilot survey of 65 microcredit companies in Zhejiang of China, this paper explores the factors affecting MFIs activities by principal component analysis and analyzes the key Influencing factors and sustainable development performance with the analysis of variance and multi-regression model.The results indicate that four key factors of operational technology, external environment, financial condition and institution size have a significant positive effect on the economic sustainability performance of MFIs, while the adjusted institution size is insignificant positive effect on operational sustainability performance,which means that key factors of MFIs sustainability have a greater impact on economic sustainability performance than operational sustainability performance.

China's gradually increasing assertiveness at the international level marked the beginning of several influential geopolitical projects. The biggest of these is the Belt and Road Initiative, which will be the subject of this post. The Chinese government initiated the BRI in 2013 to revive the historic Silk Road. The name of the new initiative was chosen based on well-known historical parallels, emphasizing the ideological component of China's current position. At the same time, neo-mercantilism prevails in China, and we can say that all roads lead to China, which means that the current nature of Chinese strategic planning corresponds to the increasingly important role of the state in international relations and strengthens its position in the world economy. The aim of the paper is to use the methods of analysis, synthesis, deduction, induction, comparison, and explanation to analyse Chinese investments that flow into various sectors worldwide, but especially into the countries participating in the Belt and Road Initiative from 2013 to the present. The largest such sector is the energy sector, which we will look at in more detail in the post. Finally, the contribution will be focused on predicting the development of further investments based on the available information as well as our own suggestions and recommendations to make them more efficient.

In the current socio-economic context, pension systems have become a crucial topic in the agenda of governments and international bodies. In this paper, an empirical study regarding the economic and social sustainability of pension systems in Central and Eastern European Countries was performed. The goal of the research is to establish the sustainability of pension systems with econometric modeling techniques on three dimensions as the research hypotheses are revealed: first, by strengthening the financial soundness of pension systems; second, consolidating the economic environment of pension systems; third, increasing the degree of social security systems with a direct effect of reducing poverty. The results led to relevant insights emphasizing the need for more inclusive economic, financial, and social reforms. From a policy perspective, these findings could be a starting point for the enhancement of the financial adequacy and economic and social sustainability of pension systems in Central and Eastern European Countries. The econometric techniques in this study highlight those modern pensions systems that need to be addressed in a more complex and empirical context that must encompass the main technical aspects. The conclusions suggest that in the future it needs judicial, economic, and social adjustments for the enhancement of the financial soundness of the pension systems.

Evaluation of market connectedness and asymmetric volatility spillover has recently seen a surge in financial risk analytics and portfolio diversification. We carried out a meta-literature review on connectedness and spillovers, providing solid insight into the research field and robust guidelines for future investigation. The review consists of a quantitative bibliometric analysis of 594 papers and a qualitative content analysis of 77 papers covering 1991 to 2021. The results of the meta-citation analysis shows that Diebold's Spillover index (2007) is the predominant method in most works as far as market connectedness and spillover is concerned. With an extensive review, we answered the following objectives (1) Analyze the most influential authors, journals, and publications, (2) Understand the research streams and most studied streams, (3) Understand the theme structure and thematic evolution, and keyword trends, (4) Examine the pattern of collaboration, and most productive affiliations, (5) Explore future research directions and untapped areas. The content analysis revealed the following important research streams in the current literature (1) Asymmetries in the market connectedness, (2) Influence of Macro Factors in the market connectedness and spillover, (3) the Role of Oil in market spillovers and hedging portfolios, (4) Dynamic cross-market connectedness and spillovers. The ongoing financial turmoil and market advancements make market connectedness a vital research topic; thus, our work would significantly contribute to macroeconomic policymakers, researchers and hedging investors.

This short paper reveals a simple methodology to create local-correlation models suitable for the closed-form pricing of multi-asset financial derivatives. The multivariate models are built to ensure two conditions, first, marginals follow desirable processes, e.g. we choose the Geometric Brownian Motion (GBM), popular for stock prices. Second, the payoff of the derivative should follow a desired one dimensional process. These conditions lead to a specific choice of the dependence structure, in the form of a local-correlation model. Two popular multi-asset options are entertained, a spread option and a basket option.

The authors developed a wealth curve (bell curve) that can predict a group of individuals’ wealth based on the composite intelligence score which is calculated based on the sum of the intelligence quotient and emotional awareness score. The intelligence quotient was defined as one's ability to perform, comprehend and learn, and emotional awareness was defined as the emotional ability to recognize and make sense of emotions. To move towards the right on the X-axis of the Kabir wealth curve (e.g., accumulate more wealth), individuals must improve emotional awareness and choose a professional career path that lands higher income, calculated risk-taking, and being connected with individuals that can lead to financial deals. Similarly, those facing social injustice can accumulate more wealth by improving emotional awareness, which will help them navigate challenging environments.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

Academic and corporate research has been focusing on analyzing stock performances, financial markets, and investment strategies for decades. According to Lynch and Rothchild (1989), certain investments can be described as plays. In this work, the authors use the term for investments in value stocks, spin-offs, and turnarounds to analyze the long-term stock performance and risk-adjusted excess returns of up to five years for those stocks. Additionally, stocks with a supe-rior return performance are identified within an investment play based on low P/E and P/B mul-tiples. The study finds that value stocks, spin-offs, and turnarounds outperform the S&P 500 on average on a one-, three- and five-year investment horizon. The analysis of the three investment plays confirms earlier studies that investment plays constitute a profitable investment opportunity. Moreover, the research exemplifies that private investors could achieve an excess return (alpha) through a passive buy-and-hold investment strategy. Overall, the analysis provides a deeper in-sight into the long-term performance of value stocks, spin-offs, and turnarounds.

The study sought to investigate the effect of Multi-choice Goal programming in project Portfolio Analysis, using Lagos State Project Portfolio from between 2017-2021 as a study case. Specifically, the objectives examined the effectiveness of multi-choice goal programming in analyzing the project Portfolio of Lagos State Government and investigated the possible lapses in the use of Multi-choice Goal programming in analyzing the project Portfolio of Lagos State Government. Data were gathered from the State financial Statements and analyzed using Lingo-17 Software. Based on the analysis, the study discovered that the goals formulated can be optimally and minimally attained as the positive deviational and negative deviational variables depict zero. This indicates that the cost incurred on the project portfolio was absolutely minimized during the period and at the same time; Lagos State Government derived the most preferred benefits from it. The study therefore concluded that the State minimized the project costs and at the same time maximized benefits while meeting its overall objectives.

The method propose in this study for stock market multi-objective mathematical model was linear programming using simplex algorithms to optimize the portfolio in the Nigerian Stock Market. The study selected five banks from the list of operators in the market and data were gathered from the banks to have individual market performances over a period of 5years. The data collected contains: Geometric Mean of Monthly Capital Gain Yields and Annual Capital Gain Yields from which Dividend Yields (%) were deducted to arrive Actual rate of return for the banks and the expected rate of return. Furthermore, the risk of semi-absolute deviation below the expected return is reduced. The data was analysed using Python programming because of some clauses in the data gathered. At first, the data assumes integer (-or +) and random in nature. As such, Python programming is one of the software suitable for such solution since the barrier for additivity is broken. Based on the analysis, the study therefore conclude that the potential investor(s) should invest in 13 units of investment x(GTbank Plc), 3 units of First Bank Plc`s Investment (y), 450 units of Zenith bank`s investment (z), and 8 units of Wema bank`s investment (m), and no units of investment of (n) Access bank. These investment quantities will result in the Optimal profit of p= 12797.902 billion naira.

This paper analyzes the potential strategies for a bank merger from a Sustainable Development Goals (SDG) perspective, focusing on three key criteria: profitability, market share, and service quality. In the short-term, the merged bank should prioritize optimizing financial performance through cost management, revenue stream identification, and risk management practices. Market share expansion can be achieved through targeted customer acquisition and retention efforts, market research, and competitive analysis. Service quality can be enhanced through improved customer service, efficient complaint resolution processes, and leveraging technology. These short-term plans align the merged bank's operations with the identified criteria and promote responsible banking practices that contribute towards the SDGs. In the long-term, the merged bank should focus on diversifying revenue streams, expanding its customer base, and optimizing cost structure. Long-term strategies should include establishing a strong brand presence, customer loyalty programs, and continuous improvement in service quality. The paper emphasizes the importance of monitoring progress, making necessary adjustments, and aligning with SDGs for sustained profitability and long-term success in the marketplace. The findings of this study provide valuable insights for banks considering a merger and highlight the significance of considering SDGs in their strategic planning.

This study explores the implications of merging two fundamentally different types of banks: Islamic and conventional banks. The research aims to provide insight into such a merger's unique opportunities and challenges and offer strategic guidance for future mergers. A balanced scorecard-based strategic analysis using Quantum Spherical Fuzzy Decision-Making Approach was used to develop the merged bank's short- and long-term strategic plans. The balanced scorecard included 12 key performance indicators (KPIs) in 4 groups, and the methodology also incorporated several questions to guide the analysis. The study results offer valuable insights into the potential opportunities and challenges of merging these two types of banks and strategic recommendations for stakeholders at all levels. The study is a valuable guideline for future mergers between similar or different types of banks. Overall, the findings suggest that a well-planned merger strategy is essential for avoiding challenges and maximizing the benefits of merging Islamic and conventional banks. By integrating the strengths of both types of banks, a merged entity could create a competitive advantage and potentially improve financial performance. However, this requires careful consideration of cultural differences, regulatory challenges, and other factors that could impact the merger's success.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

This research paper aims to examine the various aspects of cryptocurrencies, from their inception to their current status in the financial market, using a multidisciplinary approach that incorporates mathematical and psychological methods to explore the factors that contribute to the success of celebrity endorsements and the potential risks associated with them. The first section (1.1) of this research paper will provide an overview of cryptocurrencies, exploring their history, functionality, and impact on the global financial market. This will involve examining the technical details behind cryptocurrencies, such as blockchain technology, and the differences between various types of virtual assets. The research will also discuss the potential advantages and disadvantages of investing in cryptocurrencies, as well as the regulatory challenges they face. The second section (2.1) of the research paper will focus on the psychological aspect of cryptocurrency investing, analyzing the connection between personality traits and the likelihood of purchasing a cryptocurrency based on a celebrity endorsement. This will involve investigating Howard Gardner's theory of multiple intelligences to understand the qualities that make people more susceptible to investing in a cryptocurrency without prior knowledge (Gardner, 1983). The third section (3.1) of the research paper will delve into the mathematical side of cryptocurrency investing, examining the factors that contribute to the success of celebrity endorsements and the artificial growth of cryptocurrencies. This will involve developing software to calculate the artificial growth of a cryptocurrency over a 24-hour period and analyzing the data to understand the underlying factors driving its value. By taking a multidisciplinary approach, this research will shed light on the complexities of investing in virtual assets and help inform investors of the potential risks and benefits of investing in cryptocurrencies through qualitative and quantitative analyses and through the use of a Multi-Level Latent Class Analysis (LCA).

SMEs constitute the backbone of the Syrian economy which have suffered manifold challenges due to the continuous Syrian war. Covid-19 added further pressures on Syrian SMEs and force them to take certain adaptation strategies to survive. This paper aims to investigate the main challenges that face Syrian SMEs during the pandemic and illustrate how they respond to adversities emerged from governmental intervention to control the spread of the virus. It also discusses the measures initiated by the government to support SMEs during the pandemic. Through interviewing persons from the Syrian SMEs ecosystem, we find that high interest rates on SMEs loans, decline on demand as well as high inflation represent the main challenges. SMEs respond to these challenges by marketing products online, stock procurement, and strengthen connections with stakeholders. We recommend the Syrian authorities reduce lending rates and increase loan size available to SMEs to help them overcome the pandemic adversities. Moreover, SMEs will immensely benefit from training on digital tools to enhance their expansion and survival opportunities. Furthermore, bazars should be organized around the year to give SMEs the opportunity to gain continuous access to markets. In addition, incubation services should be revised, particularly, to SMEs with great potential to grow to create the suitable environment for them to scale and flourish.

The purpose of this article is to address two questions that will aid researchers in understanding FinTech’s inception, development, and potential impact on the stability of the financial system. First, it explains why financial technology is a current phenomenon. While many of the underlying technology for FinTech breakthroughs have been around for some time, it is only recently that financial institutions and entrepreneurs have begun to apply them to financial goods and services. Supply and demand factors in "conventional" financial innovation have been studied, and they have been found to converge, leading to a high rate of innovation. And second, this article explains why FinTech is being covered in greater depth than other types of innovation. This study introduces the concept of "depth" of innovation as a means of answering this question. The more fundamental the innovation, the more it will affect the financial sector. In this paper, we demonstrate that many recent developments in the field of financial technology (FinTech) are truly revolutionary advances, with far-reaching implications for the financial services industry. In addition to the benefits of more adaptability, higher transformational potential might bring increased risk to economic security.

Indonesian crypto asset owners are expanding despite the government's ban. Crypto assets are too risky for Indonesian investors. This study uses the Theory of Planned Behavior and exogenous variables like financial literacy, trust, and government regulation to determine the causes of the intention to invest in crypto assets. Exogenous variables and behavioral intentions were moderated by gender. The study employs a quantitative approach, collecting data through a questionnaire survey of 149 adults over the age of 17 who have invested in non-crypto assets. SmartPLS was used to analyze research data. Financial literacy and trust, according to the study's findings, have an impact on the intention to invest in crypto assets. Gender can moderate the relationship between belief and behavioral intention. Women are more likely than men to invest in crypto assets due to the block chain system's security, transparency, and ease of use. This research is expected to help the government address the risks associated with investing in crypto assets by increasing transaction security with futures clearing and monitoring by using third parties as depositors. It also governs the laws governing crypto exchanges' liability to their investors.

When sellers set the price for ex-ante unobservable and ex-post unenforceable quality, price signals credence quality. Hedge funds resemble incomplete long-term contracts for credence goods under buyer-determined auctions. I show that hedge funds' ability to solicit investments at higher management fees signals their capacity to generate higher net returns. This result is more pronounced during bust cycles and closer to financial hubs, i.e., when signaling quality is more valuable. The findings are relevant to understanding price and effort in the provision of credence goods like medical procedures and legal advice.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

Recent years have seen dramatic growth in the number of scholarly scientific works dedicated to the topic of Artificial Intelligence (AI), banking, and finance. With the development of scientometric tools, it is now possible to map, visualize, analyze, and assess scientific activities in many fields of engineering and social sciences including Islamic banking and finance. Based on data retrieved from the Scopus database and using a qualitative method, this study investigates, evaluates, and identifies significant development, trends, and players in the application of AI in Islamic banking and finance using the Visualization Of Similarities Method (VOS) between objects in VOSviewer. From 2006 to 2022, 387 documents were retrieved from the Scopus database; Results revealed that the top five most active countries in terms of publications are Malaysia (117 documents), Indonesia (89 documents), the United Kingdom (36 documents), the USA (24 documents), and Saudi Arabia and Bahrain (24 documents each). The International Islamic University Malaysia and Universitas Indonesia came out on top of active institutions while the top funding source came from the Ministry of Higher Education, Malaysia. The comprehensive findings and analysis reported in this study serve as a roadmap for future academics to create theory and practice for applying AI in Islamic banking and finance.

Artificial intelligence (AI) is a tool that financial intermediaries and insurance companies use in most cases or are willing to use it in almost all their activities. AI can have a positive impact on almost all aspects of the insurance value chain.: pricing, underwriting, marketing, claims management, after-sales services. While it is very important and useful, AI is not free of risks, including its robustness against cyber-attacks and so-called adversarial attacks. Adversarial attacks are conducted by external entities to misguide and defraud the AI algorithms. The paper is designed to provide a review of adversarial AI and discuss its implications for the insurance sector. The study starts with a taxonomy of adversarial attacks and presents a fully-fledged example of claims falsification in health insurance. Some remedies, consistent with the current regulatory framework, are presented.

This paper examines the influence of informal competition on SMEs innovation in the Eastern European transition economies. It investigates whether credit constraints mediate this relation. SMEs innovation is presented through four measures: Product innovation, Process innovation, Radical innovation and Green innovation. Using the BEEPs VI that covers the period from 2018-2020, we show that informal competition affects positively the product, process and radical innovation. Yet, it has a non-significant effect on green innovation. Besides, the informal sector increases SMEs credit constraints, which indirectly leads to less corporate innovation. The negative indirect effect restrains the positive direct effect. Hence, a partial mediation effect of credit constraints on the informal competition and the innovation proxies is reported with the exception of green innovation.

At the global level, trust funds (TF) have emerged from a portfolio of options as an alternative financing mechanism to help countries finance their sustainability agendas. Indonesia recently enacted wide-ranging legal arrangements on TF, including a law that encourages all sub-national governments to implement their own TF endowment model and a government regulation pertaining to special autonomy for sub-national jurisdictions in Papua for the implementation of TF – both of which enable TF to finance intended sustainability outcomes. Sustainability is of high-priority concern as the provinces of Papua and West Papua are responsible for stewardship of one of the world’s largest remaining rainforests, which is especially rich in biodiversity. These provinces operate under special autonomy, with special funds allocated from the central government and a decentralized arrangement that differs substantially to the unitary state arrangement applied nationwide; this poses challenges to implementing TF for sustainability in Indonesian Papua. In this paper, we examine TF challenges related to legality, finance, and capacity; moreover, in the context of these challenges, we assess three focus areas related to sources of funding, management, and distribution of earnings. We also discuss the implications these challenges have for operationalizing TF in Papua. This paper contributes to discussions on TF for sustainability by interlinking legal, financial, and capacity-related issues, demonstrated by a context-specific and globally relevant case study in Papua.

This research article provides criticism and arguments why the canonical framework for derivatives pricing is incomplete and why the delta-hedging approach is not appropriate. An argument is put forward, based on the efficient market hypothesis, why a proper risk-adjusted discount rate should enter into the Black-Scholes model instead of the risk-free rate. The resulting pricing equation for derivatives and in particular the formula for European call options is then shown to depend explicitly on the drift of the underlying asset, which is following a geometric Brownian motion. It is conjectured that with the generalized model, the predicted results by the model could be closer to real data. The adjusted pricing model could partly also explain the mystery of volatility smile. The present model also provides answers to many finance professionals and academics who have been intrigued by the risk-neutral features of the original Black-Scholes pricing framework. The model provides generally different fair values for financial derivatives compared to the Black-Scholes model. In particular, the present model predicts that the original Black-Scholes model tends to undervalue for example European call options.

This paper investigates whether currency risk is priced differently in the different sectors (industrial, financial, and basic materials) of equity markets in a sample of developed United States of America (USA) and developing economies (Brazil, India, Poland, and South Africa). The paper makes use of the following techniques: (i) Univariate Autoregressive Fractionally Integrated Moving Average and Exponential General Autoregressive Conditional Heteroskedastic (ARFIMA-EGARCH), (ii) the Markov-Switching method (MS), and (iii) the Canonical Vine Copulas (C-Vine) techniques. Using a sample of daily data made of the foreign exchange rate against the domestic currency and equity market sectors; our findings show that there is an asymmetry effect between equities markets and the foreign exchange rate: there is a heterogeneous, strong, and positive dependence between the two. Higher equities prices are associated with depreciation of local currencies, according to US dollar (USD) exchange rates. In addition, we find that the selected emerging economies are pricing a positive and considerable currency risk. The pricing of currency risk has a varied effect in both regimes representing the states of the economy. In fact, when currency risk pricing has a beneficial impact on certain sectors of the economy, investors predict better returns.

This research was carried out with the objective of analyzing the principles of social and solidarity economy in the community funds of the rural sector of Pichincha, Ecuador. Small organizations promote microcredits for local, social and economic development, representing an alternative to those managed by traditional banks. The research was descriptive, non-experimental field research. The population analyzed consisted of 220 community funds, and the size of the representative sample was 49 community organizations that practice solidarity finance. The data were collected through online questionnaires using a Likert scale, and the validity of this approach was judged by experts; the reliability of the instrument obtained was 0.95 using the Cronbach’s alpha method. The results highlight that in these organizations, the following traits prevail: associativity, self-management and organization. However, autonomy and solidarity have a negative valuation, which shows that strategies must be rethought to achieve the empowerment of the financial service. This will allow them to be sustainable and to expand with more benefits that promulgate financial activity and promote structures in rural community networks that promote local development and strengthen deficient principles as a basis for generating a greater benefit to the partners.

Cryptocurrencies are now the most popular investment instruments among millenials. Crypto offers great returns in a short period of time. Prior to COVID-19, Crypto experienced significant price fluctuations accompanied by an increase in the number of high transaction volumes. This situation was disrupted by the presence of the COVID-19 which made the world economy devastated, marked by the decline of stock prices in the world, especially in Indonesia. A paired test was conducted in this study to compare the state of Crypto before and during COVID-19 with the variables of Risk, Transaction Volume, Return, and Sharpe Performance. The results showed that there was a significant difference in the variables of Transaction Volume and Return. However, there was no significant difference in the Risk and Sharpe performance before and during COVID-19. This study shows that despite the COVID-19 pandemic, the enthusiasm of investors who transact crypto assets is not affected and they still get returns in accordance with the investments made. The high risk will be followed by a high standard deviation, so that the Sharpe Performance is small. Cryptocurrencies still have many gaps to research, such as regulation, so that many countries have not legalized Crypto transactions. If there is no regulation for Crypto, it is certain that an increase in cybercrime harms crypto investors and threatens global financial stability. Nevertheles, with or without COVID-19, investment transactions gain and lose based on confidence in the limited market. Therefore, the success of confidence fluctuations in crypto encourages the emergence of alternative coins created by investors to conduct an Initial Coin Offering (ICO).

Background/Objectives: Accurate prediction of stock prices is an extremely challenging task because of factors such as political conditions, global economy, unexpected events, market anomalies, and relevant companies’ features. In this work, the random forest has been used to forecast the prices of the four major Greek systemic banks Methods/Analysis: We make use of a set of financial variables based on intraday data: (i) Open stock price, (ii) High stock price, (iii) Low stock price, and (iv) Close stock price of a particular Greek systemic bank. Results/Findings: The variables used here are crucial in predicting systemic banks' stock closing prices. These provide a better prediction of the next day's closing price of the bank series. Novelty /Improvement: To our knowledge, this is the first study that employs machine learning techniques in Greek systemic banks.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

In this paper we focus on an implicit assumption in the BSM framework that limits the scope of market network connections to seeking gains in the currency basis, i.e., on trading strategies between the numeraire and the stock and between the numeraire and the option, separately. We relax this assumption and derive the equivalent of the standard BSM approach under a more general market network framework in order to assess its implications. In doing so, we find that it is not possible to hedge the implicit option that allows one to directly trade the option and stock. This represents a potential challenge to the BSM framework, since the missing market network connection provides a potentially useful mechanism for risk-bearing portfolio managers to alter their portfolios.

Since financial institutions faced to fatal scenario like subprime mortgage crisis and COVID-19, the factor-based asset allocation methodology is noticed. Asset-only approach which make to consider restrictive risk volatility as individual assets had limitation of macro factor risk. For instance, an institution which allocated assets by asset-only approach cannot deal with the inflation crisis. We review the problem of the traditional modern portfolio approach that is used by Korean financial institutions. For reasonable investment of institution, we notice improved factor-based allocation approach. The first result of this paper is that Mean-variance approach as considered only return of asset recorded lower performance than multi factor-based portfolio in macro factor crisis. Second, we notice allocation model which can minimize probability passing the liability risk exposed macro factors to investment risk exposed macro factors. There are three steps in multi-macro factor-based asset allocation approach: discovering macro factors and mapping asset classes to individual macro factor. Second, define liability account and mapping as considering income and pay out of institution. Third, minimize correlation of fac-tor-based asset risk with liability volatility. Furthermore, using covariance return of assets to allocate makes Pareto improvement and supports to break Home-bias problems.

Nowadays, determining the socioeconomic factors' influence on environmental quality is a crucial issue for policymakers. We aim to explore the impact of socioeconomic factors i.e., ethnic conflicts inform ethnic fragmentation, institutions quality effectiveness, and energy consumption on environmental quality by testing the various hypotheses (Pollution Halo Hypothesis, IPAT, and EKC) in 40 selected Asian countries throughout 1993-2019. We also use a set of control variables which are gross domestic product per capita, foreign direct investment inflows, and population growth to determine their impact on environmental quality. We use the Panel Quintile Regression Method of 0.25, 0.5, and 0.75 to analyze the results. We find ethnic conflict negatively affects the environmental quality at all quantiles. The institution's variables regulatory quality and rule of law negatively influence the environmental quality. Our result supports Porter's hypothesis because the effect of direct foreign investment on the amount of CO2 emissions is negative and significant at 0.25, 0.5, and 0.75 quantiles which states that foreign direct investment in the host country supports environmental quality. Furthermore, our results support the IPAT hypothesis in selected Asian countries.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

The study tries to find out the impact of investors demographic variables on the return from the stock market. The data has been collected from the brokerage and Marchant banks for individual investors investments, return and demographic characteristics. The research covers the period from 2009 to 2021 and have a decade long enough to identify the trend. The structural break in the data also been adjusted to avoid any kind of endogeneity. The regression results suggest that demographic variables affect the investment pattern and beta return for the investors. the demographic variables such as education, location and experience have positive links with the beta whereas age and income show negative association. The education, income and experience variables are also found to be significant in the empirical analysis.

This paper intends to examine whether a company’s policy of dividend would impact or influence the price fluctuation of respective common stock in the designated stock market particularly in Hong Kong. Hence, there are 354 companies in Hong Kong which are all listed company on the Stock Exchange of Hong Kong (HKEX), were chosen as sample data for analysis with their audited financial information ranged from 2001 to 2020. Two proxies, the yield and the ratio of pay-out of dividend were widely adopted for the measurement of the effect of dividend policy and the possible influence on the volatility of respective common stock price by employing two statistical models, which are the model of fixed and random effects to provide accurate regression coefficients. With the results from the model of fixed effects, the yield and the pay-out ratio of dividend have been found statistically associated to the price volatilization of respective common stock in Hong Kong. The statical results in this study also find the yield and the ratio of pay-out of dividend are apparently and negatively associated to the price volatilization of respective common stock in Hong Kong. The research outcomes will be a reference for further study on different variables may have effects or volatilize the price of common stock price particularly in Hong Kong capital market.

Independent Learning Independent Campus (MBKM) is a program to develop an educational ecosystem that is in harmony with learning in higher education and the needs of industry. The limited link and match between universities and the business and industrial world in Indonesia are obstacles in realizing Indonesia as a developed country in the Industrial Revolution 4.0 era. The purpose of this study is to analyze the impact of the Independent Learning Independent Campus (MBKM) program on improving the performance of Ibn Khaldun University Bogor. The method used in this study is descriptive statistics with quantitative analysis. The sample that participated in this study consisted of all active students, lecturers, and administration staff. The research data was obtained through distribution to 6100 students, 233 lecturers, and 150 education staff. The research stages include socializing the understanding of MBKM, filling out the survey, calculating the distribution of respondents' filling, and concluding the assessment category using the weighted mean score (WMS) method. Based on the result, the implementation of MBKM in University of Ibn Khaldun Bogor based on all indicators can conclude that MBKM improve the performance of Ibn Khaldun Bogor University in the good category with an average percentage assessment criteria above 75%

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

The paper proposes a mechanism for the impact of changes in the key rate on the volume of newly issued loans. The volume depends on the price (interest rates on loans), and the price depends on the key rate and the actual consumption of loans in the previous period (generalized cobweb cycle). The model was estimated by a Kalman filter, adequacy was confirmed by simulation. It is possible to forecast the average rate on loans for a month in advance according to the information published by the Central Bank of the Russian Federation (CB). By playing various scenarios for changing the key rate, it was found that in quiet periods of economic development, the usual laws of supply and demand operate in the loan market and by raising the key rate, you can reduce inflation. In the turbulent (overheated) state of the economy, an increase in the key rate can, on the contrary, provoke an increase in the issuance of loans and unconventional manipulations with the key rate are required.

The main purpose of study is to identify the impact of COVID-19 pandemic on the green financing of banks and non-bank financial institutions (NBFIs) in an emerging economy such as Bangladesh. Also, this study shows the green banking activities of the banks and NBFIs during the pandemic. To analyze the impact of the pandemic on green financing, secondary data were obtained from the quarterly and annual reports of Bangladesh Bank (BB) on green financing as well as the annual reports and websites of 61 banks and 34 NBFIs in Bangladesh for the period 2021–2019. Subsequently, the study deployed dependent t-test statistics, growth rate (year-on-year), descriptive statistics, relative percentage changes, and varying tables and graphs to analyze the obtained secondary data. The empirical findings revealed that during the COVID-19 pandemic, there was an increase in green finance for all banks and NBFIs compared to before the epidemic, indicating that the pandemic had no negative impact on the total green finance growth of all banks and NBFIs. On the other hand, compared to the pre-pandemic period, bank-wise growth in green financing was higher for state-owned commercial banks (SOCBs), specialized banks (SDBs), and private commercial banks (PCBs) but lower for foreign-owned commercial banks (FCBs) during the COVID-19 epidemic. This suggests that the pandemic does not affect the expansion of green finance by SOCBs, SDBs and PCBs but significantly impacted the growth of green financing by FCBs. Furthermore, the research findings showed that the total outstanding and classified loans within the green finance investment decrease for both banks and NBFIs during the COVID-19 pandemic. The results indicated that the Bangladeshi banks’ level of automation towards green banking were satisfactory during the pandemic. Therefore, major policy implications for the green economic recovery by the government, BB, and managers of the banks and financial institutions in emerging economies like Bangladesh were discussed.

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in Defi in terms of impact, reliability, and security and conduct exhaustive analysis. We further conclude from our extensive literature review that we can identify possible new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.

This study examined the tail dependency structure of sovereign credit risk and three global risk factors in BRICS countries using copulas approach, which is known for its ability to provide the “true” tail correlation based on the correct marginal distribution. The empirical results show that global market risk sentiment comoves with sovereign CDS spreads across BRICS countries under extreme market events, with Brazil having the highest co-dependency followed by China, Russia, and South Africa. Furthermore, oil price volatility is the second biggest risk factor correlated with sovereign CDS spreads for Brazil and South Africa while exchange rate risk exhibits very small co-dependence with sovereign CDS spreads under extreme market conditions dominated by tail events. On the contrary, exchange rate risk is the second largest risk factor co-moving with China and Russia’s sovereign CDS spreads while oil price volatility exhibits the lowest co-dependence to CDS in these countries. Between oil price and currency risk, evidence of single risk factor dominance is found for Russia where exchange rate risk is largely dominant. These results suggest that BRICS policymakers might consider financial sector regulations that mitigate risks spill-over such as targeted capital controls when markets are distressed.

Small and medium-sized enterprises (SMEs) help and support the country in financial and business aspects and employs in the country, overcomes unemployment, and creates job opportunities in different sectors of the country. The study aims to know the entrepreneurial-specific factors like age, gender, education, experience, and skills used as predictors for access to financing of small firms in Khyber Pakhtunkhwa. The questionnaire is used for data collection, and about 204 questionnaires were distributed, of which 192 returned and analyzed through binary logistic regression. The study results reveal that age and level of education have been found significant while gender, experience and skills have insignificant with the access to finance in Khyber Pakhtunkhwa, Pakistan.

Understanding and evaluating urban investment environment is essential for effectively improving the efficiency of resource allocation between cities and promoting overall development of the regional economy. This paper takes 15 node cities on maritime Silk Road covered by the “Belt and Road” as the research object, establishes a dynamic evaluation index system for investment environment, and uses projection pursuit cluster to analyze and evaluate the investment environment of the cities. It is found that the investment environment potential of a city is directly related to the level of social development, economic development, and the degree of opening to the outside world. It is recommended that node cities should seize the important opportunity of the construction of the Maritime Silk Road, introduce world-wide human, financial and material resources to promote regional resources allocation and flow, and continuously improve and upgrade the investment environment quality.

We investigate the effect of geopolitical risk on the returns of the Information Technology, Communication Services, and Consumer Staples sector firms within the S&P 500 index. We use the event study methodology and perform more than 17,000 regressions to provide empirical evidence at the sector level that geopolitical risk leads to different responses across these 3 sectors. The response of the Information Technology sector is negative for all event windows under study, except the one spanning 10 days prior to the geopolitical event and 10 days after. The Communication Services sector has positive returns as a result of geopolitical events for all event windows, except the one from the geopolitical event date and 5 days after. The Consumer Staples sector shows a negative impact on geopolitical risk for all event windows except the one from the geopolitical event date and 5 days after, demonstrating a negative correlation to the Communication Services sector.

The stress testing methodology should be implemented and applied to the entity's overall financial system at least annually, and if the organization operates in a volatile economy, it should be performed at least twice a year. Finally, managers should include regular training and development sessions for relevant employees of their organization to be fully informed and more informed and informed, considering the evolving science, theory and practicality of a discrete range of stress testing mechanisms that can be appropriately applied to overall financial framework and system of multiple financial institutions and banks. In addition, stress testing is essentially a methodology that collects and analyzes certain future macro-prudential and micro-prudential economic drivers and indicators, the primary purpose of which is to assess the future financial and economic well-being, level of growth and status quo of a financial institution, bank, organization, credit institution or economy or the nation as a whole. In addition, several of these reviews were specifically focused and incorporated into the paper, which substantially and broadly discussed and summarized the importance, feasibility and implementation and conclusions of different stress testing approaches for financial institutions and banks, especially in European and Chinese countries. region. with the primary intention of assessing the future financial and economic well-being, level of growth and status quo of a group of financial institutions, banks, organizations, credit institutions or the economy or the nation as a whole. In addition, several of these reviews were specifically targeted and incorporated into a paper that substantially and broadly discussed and summarized the importance of the feasibility and implementation and conclusions of different stress testing approaches for financial institutions and banks, especially in European and Chinese countries. region. with the primary intention of assessing the future financial and economic well-being, level of growth and status quo of a group of financial institutions, banks, organizations, credit institutions or the economy or the nation as a whole. In addition, several of these reviews were specifically focused and incorporated into the paper, which substantially and broadly discussed and summarized the importance, feasibility and implementation and conclusions of different stress testing approaches for financial institutions and banks, especially in European and Chinese countries. region. the level of growth and status quo of the financial institutions, banks, organizations, credit institutions or the economy or the nation as a whole. In addition, several of these reviews were specifically focused and incorporated into the paper, which substantially and broadly discussed and summarized the importance, feasibility and implementation and conclusions of different stress testing approaches for financial institutions and banks, especially in European and Chinese countries. region. the level of growth and status quo of the financial institutions, banks, organizations, credit institutions or the economy or the nation as a whole. In addition, several of these reviews were specifically focused and incorporated into the paper, which substantially and broadly discussed and summarized the importance, feasibility and implementation and conclusions of different stress testing approaches for financial institutions and banks, especially in European and Chinese countries.

Financial Payment has evolved into mobile payment for easy access and convenience. Despite the benefits of mobile payment, the adoption rate is unsatisfactory worldwide. Therefore, understanding the customer's adoption intention factors is essential for both researchers and practitioners. This study examines mobile payment intention and actual use by adapting the integrated framework, which combines the Mobile Technology Acceptance Model and the Theory of Planned Behaviour with additional new constructs found to be related. The new model consists of 12 variables moderated by the individual's experience and is examined via a structured PLS equation mixed-mode methodology. Knowing mobile payment influence will shape the industry strategic decision and socially contribute to transforming society into a cashless society. This paper provides an opportunity to prove the relationship between technology variables and human behaviour concerning mobile payment adoption. In contrast, this study novelty adopted an integrated model that combines MTAM with the TPB.

Blockchain is a path-breaking paradigm, and cryptocurrencies are one of the main application areas of Blockchain technology. Bitcoin leads the cryptocurrency markets, both in terms of market capitalization and in scientific interest. In this paper, we performed a comprehensive bibliometric study of the Bitcoin-related literature. Using the Scopus database, we created a sample that comprises 4495 documents written in the 2011–2020 period. Furthermore, we provided insights about dimensions such as the change in the number of publications over the course of years, the main research areas, types of published documents, most important platforms and sources of Bitcoin publications, highly cited studies, productive authors, author’s countries, and finally main funders of Bitcoin-related research. Lastly, our bibliometric study manifests the current state and future path of Bitcoin literature from distinct perspectives.

This study examined the relationship between financial sector liberalization and agricultural sector output in Nigeria using annual data spanning the period 1986-2020. Specifically, the objectives of the study are to examine the relationship between lending rate, exchange rate, commercial bank credit to agriculture, inflation rate and agricultural sector output in Nigeria. Ex-post facto research design was employed and the annual time series data were collated from Central Bank of Nigeria (CBN) Statistical Bulletin. The econometrics methods of unit root, co-integration and error correction mechanism were used for the analyses. The outcome of the ADF unit root test show that the variables were stationary. Also the co-integration result showed that there exist co-integration amongst the variables in the model. The results from Error Correction Model indicates that lending rate and inflation rate have a negative relationship on agricultural sector output while exchange rate and commercial bank credit to agriculture have positive relationship on agricultural sector output. Based on these results, this study recommends that government and policy makers in Nigerian should initial policies that will boost investments in the agricultural sector through direct provision of credits to agriculturist and banks should also lend at a very low and subsidized interest rate to enable farmers’ access agricultural loans that will boost agricultural productivity in the economy.

FinTech has been in the focus of the discussion for quite some time. However, the market share of FinTech companies is still relatively small in compared to more traditional financial services. The purpose of this paper is to analyse the status quo, current developments and challenges ahead for the Latvian FinTech sector. We combine three analyses: the political and legal, the economic, the social and the technological environment (PEST) analysis, an analysis of the size and performance of FinTech companies during the last 10 years, and a survey among FinTech companies. We find that the current status of regulation is one of the main obstacles to FinTech development, because it does not sufficiently consider FinTech-specific aspects. Problems in attracting skilled workforce, and an environment that is not very supportive of new developments in finance are further challenges and might explain at least part of the growth and performance difficulties. A revision, modernization and harmonization of regulation is essential to create a level playing field for all market participants: FinTech companies, traditional financial service providers and those originally traditional players that are integrating FinTech solutions in their business model. Further efforts are also required to foster the Latvia’s attractiveness for skilled workforce. We hope that this study helps increase the visibility of Latvian FinTech and contributes to the development of the new Latvian FinTech strategy.

Green credit policy as an important tool to guide China's sustainable economic development, how to effectively play the function of capital deployment and improve the efficiency of industrial green innovation is an important issue facing the construction of ecological civilization. This paper uses China's Green Credit Guideline introduced in 2012 as a quasi-natural experiment , based on relevant panel data of industries from 2007 to 2018, uses the Super-SBM model including non-expected output to measure the green innovation efficiency of 35 industries in China, and constructs the PSM-DID model to explore how green credit policy impact on the green innovation efficiency of heavily polluted industries, the results show that : green credit policy significantly contributes to green innovation efficiency of heavily polluted industries with a lag. Further study finds that green credit policy pushes heavily polluted industries to improve green innovation efficiency by increasing financing cost and R&D investment; meanwhile, the heterogeneity test shows that the higher the state-owned share of industry, the greater the promoted effect of green credit policy on green innovation efficiency of heavily polluted industries. Finally, in order to accelerate the implementation of green credit policy and promote the green innovation efficiency of heavily polluted industries, relevant countermeasures are proposed from three aspects: banks, enterprises and government.

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

One of the most significant developments in the sports world over the last two decades has been the use of mathematical methods in conjunction with the massive amounts of data now available to analyze performances, identify trends and patterns, and forecast results. Football analytics has advanced significantly in recent years and continues to evolve as it becomes a more recognized and integral part of the game. Football analytics is also used to forecast game outcomes, allowing bettors to make educated guesses. This article describes mathematical concepts related to football analytics that enable a better betting strategies. We explain how the pitch is partitioned into different zones and we define possession sequences. Furthermore, we explain what an expected goals model is and which expected goals model we use in this research. Furthermore, we define two general characteristics of a player evaluation method, each corresponding to one of the equations of the Dawson model. Based on these characteristics, we describe the developments of several general approaches for evaluating players in the context of the Dawson model.

The authors propose in this study to evaluate financial performance applications for stock prices of the Indonesian Stock Exchange in manufacturing companies. The method of research used here is a quantitative descriptive method. In that statement, Indonesian Stock Exchange manufacturing companies between 2016 and 2018 are the population use in this research, using the sampling technique Purpose Sampling. This research uses secondary data from 2016 to 2018, the financial reports of Indonesian Stock Exchange manufacturing companies. The Indonesian stock exchange web site has been provided with all data sources at https://www.idx.co.id, and searching for www.google.id. Our analysis shows that book value prices and the net profit margin affect stock prices. The value of financial performance at the Indonesian Stock Bourses manufacturing companies' cost amounts to 64.5 percent, while other factors not listed in this study account for 35.4 percent.

Hybrid Classical-Quantum computing has already arrived at several commercial quantum computers, offered to researchers and businesses. Here, applications are made to a model of financial options, Statistical Mechanics of Financial Markets (SMFM). These applications were published in many papers since the 1980's. This project only uses Classical (super-)computers to include quantum features of these models. Since 1989, an optimization code, Adaptive Simulated Annealing (ASA), has been to fit parameters in such models. Since 2015, a path-integral algorithm, PATHINT, used previously to accurately describe several systems in several disciplines, has been generalized from 1 dimension to N dimensions, and from classical to quantum systems, qPATHINT. Published papers have described the use of qPATHINT to neocortical interactions and financial options. The classical space by SMFM applies nonlinear nonequilibrium multivariate statistical mechanics to fit parameters in conditional short-time probability distributions, while the quantum space described by qPATHINT deals specifically with quantum systems, e.g., quantum money. This project thereby demonstrates how some hybrid classical-quantum systems may be calculated quite well using only classical (super-)computers.

This paper shows the influence of CCP’s collateralization on the pricing of the Credit Default Swaps (CDS). A narrowly variant in the way the CDS seller decides over the resources in the settlement comes with a substantial change on the elements that determine the price of the CDS.

This paper investigates empirically the effect of export diversification (i.e., both export product diversification and services export diversification) on financial openness, using a sample of 119 countries (including both developed and developing countries) over the period 1985-2014. Based on the Blundell and Bond's two-step system Generalized Methods of Moments, the analysis has revealed that both export product diversification and services export diversification influence positively financial openness. However, this outcome hides differentiated effects across countries in the full sample. Specially, countries with a very high real per capita income experience a positive effect of export concentration on financial openness, while for countries with a relatively lower per capita income, it is rather export diversification that drives positively financial openness. Interestingly, the effect of export diversification on financial openness depends on the size of external shocks that affect domestic economies, as well as countries' economic growth performance. Overall, these findings add to the empirical literature on the effect of international trade on financial openness by showing that both export product diversification and services export diversification matter for financial openness.

This study was conducted to propose the Green Economic Development Plan for Manufacturing S.M.E.s based on financial performance and operations thru Green Initiatives. The descriptive survey method used to gather data to determine the green initiative's implementation of small and medium (S.M.E.s) manufacturing enterprises. Green initiatives use as a basis for crafting a green economic development plan. Document analysis was also employed to obtain data from any available printed materials and records provided by the respondents. Such methods of gathering information used to validate data gathered from local and foreign-related literature. The observation also employed to survey the assets owned and validate any green initiatives practiced, including their implementation. The study's findings show that among the green initiatives implemented by SMMEs, which resulted in the reduction of total costs and expenses, were the proper disposal and segregation of waste materials, water management by recycling wastewater and using water-efficient equipment, natural resources, and raw materials management. SMMEs should encourage active participation and support of suppliers and customers in achieving G.E.D.'s objectives by developing incentive schemes.Furthermore, SMMEs should continue to benchmark with G.E.D. Practitioners are operating locally and abroad to adopt best greening strategies and regularly network with concerned government agencies for continuous updating on G.E.D. Initiatives that may benefit the firm. Further research may be conducted on green initiatives implemented by small and medium enterprises in other industry sectors.