The aim of this study is to examine the short-run and the long-run equilibrium relationship between banking sector’s efficiency and stability and its endogenous and exogenous determinants such as inflation and international oil prices shocks in Saudi Arabia over the period 2004–2022. This study differentiates between the direct and indirect effects of international oil price changes on bank efficiency and stability to investigate the extent to which these changes can affect the banking sector through inflation. The first stage consists of using a panel Autoregressive Distributive Lag (ARDL). The empirical result confirms the existence of a long/short run relationship between oil price shocks and stability and efficiency of banks. In the long run the relationship is statistically significant and positive and is negative in the short run. On the other hand, this study finds that oil price shocks directly affect the stability and efficiency of banks. In the second stage, this study uses a nonlinear ARD (NARD) to examine the short- and long-run asymmetric impacts of oil price shocks on the stability and efficiency of banks by decomposing the oil price index into positive and negative changes. The finding confirms an asymmetric relationship between oil prices and the stability and efficiency of banks in Saudi Arabia. In addition, a positive change in oil price can affect the stability and efficiency of banks more than a negative one. Overall, the finding highlights the need for policymakers in Saudi Arabia to be vigilant in addressing the potential risks arising from oil price fluctuations and to adopt appropriate policy measures to maintain stability and efficiency in the banking sector.

The increasing prevalence and heightened severity of extreme weather events, which are consequences of climate change, pose significant challenges to the global property insurance sector. This study examines the concurrent crises faced by insurance companies in maintaining profitability and by property owners in affording coverage, thereby necessitating a reevaluation of current insurance paradigms. We present an innovative approach that utilizes a back-propagation (BP) neural network to forecast insurance losses and guide strategic underwriting decisions in regions prone to perils. Our advanced model combines crucial factors such as the likelihood of extreme meteorological phenomena, insurance risk premiums, and capital adequacy ratios to predict impending losses and inform insurance policy formulation. Through an extensive analysis of nearly two decades of catastrophe data from the United States and New Zealand, we determine the model's effectiveness in anticipating insurance-related losses and providing strategic guidance to insurance entities. The study concludes with recommendations for insurers to enhance risk assessment, innovate products, collaborate with governments, invest in green projects, and establish dedicated climate risk management teams. The study contributes to the discourse on climate change adaptation in the insurance industry and offers practical solutions for navigating the complexities of extreme weather risk.

Infrastructure financing in least-developed countries (LDCs) faces various obstacles, including fragile economic conditions, inadequate regulatory frameworks, and concerns over transparency and investor security. This deters private sector involvement, impeding vital infrastructure development and long-term economic progress. Blockchain technology, on the other hand, has demonstrated its ability to enhance transparency and improve operational efficiency. To bridge this gap, this study presents a novel blockchain-based framework for private financing governance in such projects. The proposed framework was built on three components: a foundational blockchain layer for secure transactions, a DAO layer for governance, and a node layer for user interaction. Moreover, a decentralized decision-making structure was outlined, which allocates voting power according to roles and reputations. The framework also introduces well-defined entry and exit criteria to ensure commitment and transparency. Further, it incorporates a member-driven proposal process and a reputation-based compensation system to encourage active involvement. Finally, a real-world case study was used to assess the framework's practicality and efficiency. According to the findings, the proposed framework has the potential to address and improve the identified gaps. The results of this study provide conceptual insights for LDC infrastructure project stakeholders to enhance the efficiency of infrastructure development in these nations.

Financial Market Infrastructures (FMIs) are crucial for the efficient operation of financial markets. However, studies focusing on Chinese FMIs are limited. Following the release of a draft regulation on Chinese FMIs in December 2022, this study, building on Zhang and Yin (2023), provides an updated analysis. It introduces non-central bank affiliated FMIs with the latest data, explores the evolution of regulatory reforms, and examines the newly-released regulations. In particular, it offers a critical review of specific regulations and their implications for China's recent policy shift towards prioritizing security and establishing financial dominance. Finally, the study outlines a research agenda for future investigations.

This research paper investigated the hedging effectiveness, dynamic dependence, safe haven ability, and portfolio diversification benefits of alternative assets (precious metals) with conventional and Islamic stock indices. The data covered the period from March 2011 to March 2021, and a copula model with switching dependence was utilized. The estimated transition probabilities demonstrated a high persistence of the same dependence regime in all estimated pairs. The research found that the use of precious metals with stocks for diversification is a favorable choice, proving them to be the best suitable solution to avoid risks. They also provide a save haven effect along with diversification benefits and reveal that risk reduction benefits vary with respect to portfolio types. The findings of the research indicate that negative shocks prompt investors to seek refuge in safe haven assets to safeguard their investments from extreme negative shocks. For most of the situations studied, precious metals provide the best and most effective hedge to stock returns. Overall, the addition of alternative assets to the stock portfolio reduces risks and provides better diversification benefits. Results vary depending on the circumstances of the country. These findings have practical implications for policymakers, fund managers, investors, and risk management practitioners, guiding them in their investment and risk management decisions.

There are different audit methods for analysing operational facts and identifying deficiencies in business process management. However, financial data and transactions indirectly relate to physical objects and their attributes, resulting in distinct characteristics for their analysis and auditing methods. It is important to find new methods for finding a process extraction solution based on the financial audit data space to increase the efficiency of the auditors' work by reducing their time costs and increasing the possibility of detecting financial anomalies in large amounts of financial data. The research presents discussed different financial data space (FDS) methods, and process mining (PM) that include related classifications of financial data and external and internal factors that can influence data behavior (value changes) and the peculiarity of FDS is that it includes not only the characteristics of financial data but also other operational characteristics (events, environmental and internal changes, business location) that may be related to the change in financial objects (FO) values. The authors of the paper discuss the prototypes of specific elements in financial data analysis that were developed using the Process Mining software tool Disco and present the conclusions of the effectiveness of the financial processes cube defined to increase the analytical possibilities of the auditor's work with large amounts of data, introducing more flexible functionality for the analysis of financial activities.

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

This study empirically investigates the relationship between realized higher-order moments and the Fear and Greed Index as a measure of sentiments. We estimate daily realized moments for five different sentiment levels using 5-minute return data of the S&P 500 index from January 3, 2011, to September 18, 2020. We found that the Fear and Greed Index significantly impacts realized volatility during periods of extreme fear. Additionally, various sentiment indicators influence realized skewness and realized kurtosis. The VIX index significantly reduces realized skewness across all sentiment levels. Bearish and bullish sentiments have a significant negative relationship with negative realized skewness during periods of extreme fear and extreme greed. Conversely, the Fear and Greed index, bearish and bullish sentiments have a significant positive relationship with positive realized skewness. During extreme fear, the Fear and Greed index, bearish, and bullish sentiments have a significant negative relationship with realized kurtosis. These results remain consistent when considering the non-linear characteristics of the Fear and Greed Index during periods of extreme fear and extreme greed. These findings highlight the relevance of understanding sentiment in financial risk management and its significant relationship with asymmetric and extremity characteristics of asset returns.

This study examines the relationship between financial investment, shareholder preference, corporate social responsibility (CSR) performance and life satisfaction for BP and Shell within the UK, together with their alignment with specific Sustainable Development Goals (SDGs). about, using regression analysis, in both projects Hypotheses were tested to assess the significance of these predictors in the performance of CSR developed. The results showed that financial investment, shareholder ownership, and social satisfaction did not significantly predict CSR performance for BP and Shell. Furthermore, there were no significant differences in the impact of these predictors between the two companies. Further research revealed Shell’s strong commitment to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), with both initiatives drawing on Shell’s significant investments in renewable energy and climate recognition in the management system established him as a leader in sustainable development efforts. BP’s goal of achieving net zero emissions by 2050, especially focused on SDG 7 and SDG 12 (Responsible Consumption and Production), this proposal was adopted. Comparing the two sectors, Shell showed significant advantages, which can be attributed to its diversified capabilities and its strong alignment with the sustainability objectives of BP, while growing largely, it faces significant financial challenges in its transition to clean energy. The study concludes that although both companies are committed to sustainability, Shell now leads in terms of effective CSR and financial performance

India’s increasing reliance on equated monthly installments (EMIs) has reshaped household financial behavior in ways that pose significant risks to long-term economic stability. This paper examines the growing trend of EMI-driven debt accumulation, highlighting its detrimental effect on household savings and the rising financial vulnerability across demographic groups. Leveraging data from the Reserve Bank of India (RBI), CRISIL, and the National Statistical Office (NSO), the study delves into how debt dynamics are shifting within Indian households, particularly in the context of broader economic trends. Retail credit has expanded rapidly, growing from 12.1% of GDP in 2017 to 19.4% by 2023, with housing loans now making up nearly half of all retail loans. Concurrently, the growth of unsecured loans, such as credit card debt, has surged at an annual rate of 21.3% between 2021 and 2024, reflecting the increasing ease of borrowing for consumers. However, this boom in credit has coincided with a marked drop in household financial savings, which fell to a 47-year low of 5.3% in FY22, underscoring a troubling shift from saving to borrowing. The younger population, particularly those under 35, have been the most active in taking on debt, with loans to this group nearly doubling between 2015 and 2021 due to the rise of fintech platforms and non-banking financial companies. This shift has contributed to a steep rise in debt-service-to-income ratios, amplifying the financial risks for these households in the face of potential economic shocks. By connecting these micro-level household debt trends to broader macroeconomic risks, this paper reveals how India’s EMI culture could deepen the impact of future financial crises, leading to widespread defaults and destabilizing the economy. The study calls for proactive regulatory measures and enhanced financial literacy programs to curb the growing reliance on consumer credit and mitigate the risks of a broader financial collapse.

Recently, the introduction of Environmental, Social, and Governance (ESG) scores has become crucial for investment decisions and in minimizing portfolio risk. This study aims to understand the relationship between ESG scores and Value-at-Risk (VaR), computed by using a Vine copula-GARCH based approach, chosen for its reliability in detecting interdependencies among multiple stocks. In fact, one of the main challenges in estimating VaR for a stock portfolio is capturing the dependence structure among a large number of assets. The dataset consists of 16 companies listed on the FTSE100 index. The corresponding ESG scores were collected over a comprehensive period of five years, from 2018 to 2022, covering both normal and stressed market conditions. Additionally, a focused analysis was conducted for the period from 2020 to 2022 to isolate the specific effects of the COVID crisis. The results indicate that an increase in assets with the highest ESG scores reduces potential losses in the portfolio. This finding underscores the importance of integrating high-level ESG scores into portfolios to mitigate market risk. Additionally, during periods characterized by stressed market conditions, the impact of ESG scores on VaR is even more pronounced, demonstrating that sustainable assets are more resilient in times of crisis.

A longstanding debate is raised about how far does the credit sector promote local investment and whether banking sector is efficacious in the meaning it helps respond to investors financing requirements and whether it serves the conduct of monetary policy in its conventional formulation. There are much concerns also about the alternatives of monetary policy conduct when the interest rate channel transmission mechanism does no longer work in terms of effectiveness of monetary policy and how to step aside credit supply insufficiencies and disruptions in order to respond to excess credit demand. This article provides an assessment of the relevance of unconventional monetary policy to deal with the issue of the vicious role played by the credit sector in its failure to fulfill banking sector efficacy due to an excessive search for yield motivation and extreme awareness from systemic risk. We run an ARDL and Bound test to the data collected on the Tunisian banking sector and show that Banking sector efficacy is affected in the short run by Fisherian sensitivity another expression of the impact of inflation on real activity and banking sector specificities and in the long run it is affected by the impact of inflation on real activity and business cycle effects expressed in terms of sensitivities of Credit supply and demand to output gap. Banking specificities do no longer matter in the long run and banking sector efficacy is not at all affected by Monetary policy in the short run because of nominal rigidities and in the long run because of monopolistic competition and backward oriented expectations that diverts the pass through effect of monetary policy as the pricing of loans is exclusively tributary on the search for yield motivation and the agency costs and result in a non competitive price of risk premiums that do not translate the transmission of monetary policy. We conclude that the behavior of the banking sector is vicious because it conveys importance exclusively to the search for yield motivation and profitability of the banking sector and to the mitigation of systemic risk and does not play any role in the promotion of local investment which is a pillar of economic performance and growth. We propose the Credit easing as an unconventional monetary policy that can step aside the hindrance of an inelastic credit supply through modifying asset prices and thereby improving attractiveness of assets to the banking sector and forcing financing through credit allocation but warn from the difficulties that might result from unconventional monetary policy in terms of inability to withdraw from the perturbation and gauge discretionary policy accurately.

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for, when extreme catastrophic events occur, resulting in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance in the level of risk. Our starting point is to use a Hierarchical Risk Aggregation method, which was initially based on 2-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over or underestimating of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines.

The Association of Mutual Funds of India (AMFI) under the direction of the Securities and Exchange Board of India (SEBI) provided open access to various risk parameters with respect to MidCap and SmallCap funds on its website in February, 2024. This disclosure of month wise cross-sectional data depicting stress-test and liquidity analysis was hailed as a milestone as it provided a glimpse into inherent risk of mutual funds. The AMFI dataset consisted of 14 variables including stress test results, ownership concentration on both liability & asset sides, standard deviation on portfolio’s returns, portfolio beta, trailing 12-month PE ratio, portfolio turnover etc. Among these, the primary variable identified in grading mutual fund is the stress test parameter, expressed as number of days required to liquidate 50% and 25% of the portfolio respectively on a pro-rata basis under stress condition. A higher number of days to liquidate the portfolio under stress conditions represented higher risk for the investor. The objective of our paper is to test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors.Our study utilizes AMFI datasets available for MidCap and SmallCap mutual funds from February 2024 (inception period) to May 2024. To operationalize the analysis, the derived response variable was binned to represent high and low risk based on the time required for pro-rata liquidation of the portfolio. The study employed feed-forward neural networks, with the logistic regression as the step function, to model the relationships. Results suggest that the simpler neural network models with one hidden layer and two to three nodes show higher accuracy and specificity. Analysis also shows that more complex architectures do not always guarantee better performance as these models might suffer from higher bias. By leveraging advanced computational techniques, this study contributes to the ongoing discourse surrounding risk management and decision-making in the realm of mutual fund investments.

The aim of this study is to explore the influence of Environmental, Social, and Governance (ESG) factor on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted Principal Component Analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of 7 variables that predict business failure. The results indicate that ESG factor significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

This study, which investigates the impact of foreign bank entry on the efficiency and sustainability of domestic banks in developing countries using a Meta-Frontier analysis to estimate efficiency scores, presents findings of significant importance to banking and finance. By incorporating financial, social, and environmental sustainability proxies—such as efficiency, loan portfolio composition, and macroeconomic conditions—this study assesses whether foreign competition enhances or undermines the long-term stability of domestic banking sectors. The results show that while foreign banks can improve financial efficiency, they may destabilize domestic banks, notably smaller or less capitalized institutions. Additionally, the findings suggest that banks with higher investments in SME lending and green projects demonstrate better social and environmental sustainability. Policymakers and financial institutions must consider these dual effects when promoting foreign bank entry.

The consistent recognition of both explicit short term and implicit long term elements of post-retirement contracts between employees and the firm can have a significant implication for estimating a firms long term sustainable cost of capital. In this paper, we show that the significant levels of investment in company own stock by pension and post-retirement plans can effectively dilute the interest of pre-existing shareholders in the enterprise value of corporations in ways which are not revealed by a classic entity theory perspective on equity. We test the empirical implications of adopting a pre-existing shareholder model of the reporting entity to re-examine the relationship between a firm’s sustainable cost of capital and various sources of idiosyncratic risk. We find that the estimated cost of capital is sensitive to: (a) alternative explicit versus implicit definitions of pension liability; (b) the nature and scope of long-term deferred compensation arrangements; and (c) the scope and nature of investment-related risks through investment in sponsoring company stock that are associated with these pension arrangements.

The dynamics of society is enabled, in large part, through an efficient matching between people who have money to lend and people who have a need to borrow. Fair loans serve both the lender and the borrower. This task is handled today mostly via traditional banks. They offer various plans to depositors from whence they serve borrowers. Proposing to replace such rigidity with cyberspace-rooted dynamic ad-hoc global matching of lenders and borrowers. Uniquely here we build a chain of lenders to support a single borrower. Lenders will post a proposition to lend a given amount of money for a stated duration for a set interest rate. Borrowers will post a request to borrow a desired amount of money, for a specified duration, and for which they are ready to pay a stated interest. A virtual digital bank (VDB) will match all the posts, negotiate terms as necessary, and move the money -- everyone benefits. Distributed Digital Lending, DDL, applies to macro, long terms loans, as well as to micro. very short terms loans. DDL efficiency will ensure that all money at rest will be money in growth, and the full global lending capacity will be put to good service through the exact loans borrowers want. DDL is hinged in digital transactions which are immediate, irrevocable, and private, per regulatory allowance. The matching is optimized with AI tools. BitMint*LeVeL is the design standard coin for DDL, but other digital money options will do too.

The prediction of stock prices is a challenging task, particularly for retail investors who may lack the resources and expertise to perform sophisticated quantitative trading. This study focuses on enhancing stock price prediction for retail investors by employing advanced machine learning techniques on data from the stock exchange market. We utilize a comprehensive methodology that includes data preprocessing to handle missing values and outliers, feature engineering, cross-validation, and parameter tuning. The techniques applied include Keras Deep Neural Networks (DNN), LightGBM, LSTM, GRU, and linear regression (LR). Our proposed ensemble model, which combines time series and deep learning models, demonstrates superior performance compared to individual models. This integration of methods leads to significant improvements in prediction accuracy, providing a robust solution for retail investors.

Accurate prediction of investment returns is crucial for financial decision-making. Traditional models often struggle with the complexity and variability inherent in financial data. This article presents an advanced predictive model integrating three deep neural networks, pretraining, and ensemble learning to enhance prediction accuracy. By leveraging a combination of DNNs for handling different complexities of data, and a CNN for efficient feature extraction, our approach significantly outperforms traditional methods. Our model incorporates sophisticated data handling techniques, including extensive preprocessing and data augmentation, and benefits from pretraining and ensemble methods to improve robustness and performance. This comprehensive framework offers a robust solution for investment return prediction, addressing the shortcomings of existing models and providing enhanced accuracy and reliability.

In this study, we provided an empirical testability of the FF3FM against the co-moment-augmented FF3FM using the Newey–West GMM methods to account for potential endogeneity. The results showed that value risk is not significantly priced, and systematic skewness and systematic kurtosis play more important roles in explaining asset return than market and size factors. We discovered that there is much to gain in moving from the incumbent FF3FM to the co-moment-augmented FF3FM. By extending this study to Nigeria and using out-of-sample data that span across the recent COVID-19 period, the study has contributed to existing knowledge significantly. The study concludes that the co-moment-augmented FF3FM is superior to the incumbent FF3FM in pricing assets, and the anomalies associated with the return-size risk factor relationship are reduced by accounting for systematic skewness and systematic kurtosis in the FF3FM. Therefore, we recommend that investors should take a long position in size-hedged portfolios and hold systematic skewness and kurtosis as constant, and vice visa.

While the financial technology (FinTech) sector has revolutionized conventional banking services with technology-driven solutions, it also faces considerable cybersecurity hurdles to guard against data protection and sustain the overall financial performance. The main purpose of this paper is to identify the key cybersecurity challenges encountered by FinTech companies, such as data confidentiality concerns, complex cyber-attacks, difficulties with regulatory compliance and third-party risks or technological innovations. This also assesses the regulatory responses to those challenges, such as data privacy regulations, macroprudential regimes, industry-specific guidance and international regulatory cooperation. Through this analysis, the paper attempts to synthesize current state of cybersecurity domain within FinTech and provides suggestions for strengthening data shelter and financial stability by implementing best practices with regulatory strategies.

The study aims to explain the relationship of financial literacy with spending habits and behavior. Financial behavior is a mediator of Generation Z. Financial literacy is a very important dimension in making financial decisions, and this research highlighted the direct and indirect impacts that financial literacy exerts on spending behavior. Data is collected from 317 respondents using a structured questionnaire measuring financial literacy, financial behavior, and spending behavior. Correlation, regression, mediation analysis, and Structural Equation Modeling have been used in evaluating these relationships. Financial literacy directly reflects a strong relationship with spending behavior, and its coefficient is 1.167. Financial behavior, therefore, exercises a strong mediation effect on this relationship with a mediation coefficient of 0.797, thus indicating that financial behavior is an essential tool in successfully translating financial knowledge into responsible spending. From these findings, greater financial literacy and responsible financial behavior may increase the aggregate financial efficacy of Generation Z. Although the current study heavily focuses on the fact that financial literacy and behavior are highly associated with financial well-being, it shows that targeted financial literacy education programs on either behavior or financial literacy enhance healthier spending behavior. The scope of future studies involves considering the role of income and personality characteristics as well as an elongation of the time horizon to uncover the impact of financial literacy on shifts in the individual's financial well-being.

In this work, we explore Conjecture 1 proposed in the newly released pioneering paper “Volatility transformers: an optimal transport-inspired approach to arbitrage-free shaping of implied volatility surfaces” by Zetocha Valer, which posits that the “One-X” property is both a necessary and sufficient condition for convex order between non-negative continuous strictly increasing distributions with the same mean. We provide a counterexample demonstrating that the conjecture, as originally stated, does not hold. By examining stochastic orders, particularly the increasing convex order, and the “One-X” property, we propose an enhanced version of the conjecture which is shown to be valid. Furthermore, we discuss the implications of these findings in the context of equity implied volatility fitting and the construction of implied volatility surfaces, highlighting the challenges posed by real-market conditions. In the end, we propose several applications in implied volatility surface construction or simulation.

This paper presents a novel approach to financial network analysis by leveraging PolyModel theory. Traditional financial networks often rely on correlation matrices to represent relationships between assets, but these fail to capture the complex, non-linear interactions prevalent in financial markets. In response, we propose a method that quantifies the relationship between financial time series by comparing their reactions to a broad set of environmental risk factors. This method constructs a network based on the inherent similarities in how assets respond to external risks, offering a more robust representation of financial markets. We introduce several network topological properties, such as eigenvalues, degree, and clustering coefficients, to measure market stability and detect financial instabilities. These metrics are applied to a real-world dataset, including the DOW 30, to predict market drawdowns. Our results indicate that this PolyModel-based network framework is effective in capturing downside risks and can predict significant market drawdowns with high accuracy. Furthermore, we enhance sensitivity to smaller market changes by introducing new time-series indicators, which further improve the predictive power of the model.

This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.

This study investigates the impact of Artificial Intelligence (AI) on traditional firms, specifically examining how scalable network properties and the integration of Environmental, Social, and Governance (ESG) considerations affect financial margins, sustainability, and bankability. The research addresses the core question of how AI investments can drive economic value by enhancing EBITDA and financial stability while aligning firms with sustainability goals. Using advanced network theory and multilayer models, the study reveals that AI-driven scalability significantly improves a firm's financial performance, making it more attractive to investors and financial institutions. Additionally, the integration of ESG factors further enhances operational efficiency, market valuation, and long-term financial stability. The study introduces an innovative "with-or-without" analysis to quantify the comparative benefits of AI and ESG integration, underscoring the role of these technologies in driving sustainable growth and addressing bankability concerns in traditional firms. This comprehensive approach offers new insights into the evolving dynamics of AI, ESG, and corporate finance.

Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.

This study investigates the strategic unity of fintech enterprises, assessing whether their characteristics bear greater resemblance to conventional banking institutions or technological corporations. Through the evaluation of operational functions, target demographics, business frameworks, stakeholder engagements, equity market dynamics, and empirical data, the analysis indicates that fintechs exhibit a closer alignment with technology companies. The inquiry uncovers significant lacunae in the prevailing scholarly discourse, particularly regarding the examination of fintechs’ fundamental attributes and their ramifications for strategic positioning. The results not only imply that fintechs are more akin to technology firms but also underscore the increasing convergence between fintechs and tech enterprises, propelled by the incursion of Big Tech and a heightened focus on sustainability. This implies an evolving financial landscape where traditional categorizations are progressively obscured. Our research is pivotal for comprehending and navigating this emergent terrain, which holds paramount importance for professionals operating within the financial and technological domains.

The rapid emergence of Decentralized Finance (DeFi), driven by blockchain technology and smart contracts, is transforming the landscape of the financial industry by enabling peer-to-peer financial services without traditional intermediaries. This paper examines the impact of DeFi on traditional banking systems, focusing on the opportunities, challenges, and future directions of this revolutionary financial model. We explore how DeFi disrupts traditional financial intermediation by offering faster, cheaper, and more transparent financial services, potentially reducing the dominance of conventional banks. The study addresses critical issues such as regulatory challenges, including compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements, and the importance of consumer protection in a decentralized environment. We also investigate collaboration opportunities between traditional banks and DeFi platforms, which could lead to innovative financial products and services. To provide a comprehensive analysis, the paper employs a mixed-method approach, combining a review of recent literature, empirical data, case studies, and expert interviews. The findings highlight significant gaps in current regulatory frameworks and emphasize the need for a balanced approach that fosters innovation while ensuring financial stability and consumer safety. This study concludes by offering recommendations for policymakers, financial institutions, and DeFi developers to harness the potential of DeFi while addressing its inherent risks, paving the way for the sustainable growth of decentralized finance in the broader financial ecosystem.

This report provides a comprehensive analysis of portfolio risk management using Value at Risk (VaR) for a banking sector investment portfolio. We evaluate a portfolio composed of SBI, HDFC, Axis Bank, and ICICI Bank, using historical data from July 1, 2023, to June 30, 2024. The report covers the calculation of VaR, portfolio variance, and standard deviation, highlighting the impact of different weightage adjustments on the portfolio’s risk profile. Two scenarios—one with increased weightage in SBI and another with increased weightage in HDFC—demonstrate how changes in asset allocation affect potential losses. The findings emphasize the importance of strategic asset allocation for both speculators and hedgers. Speculators are advised to focus on high volatility and market trends, while hedgers should prioritize stability and risk management. This analysis aids investors in making informed decisions to balance risk and return in their banking sector investments.

This study investigated the relationship between environmental, social, and governance (ESG) disclosure and the performance of Saudi Arabian companies. We analysed panel data from the 100 non-financial companies listed on the Saudi stock exchange (Tadawul) from 2017 to 2022. Using fixed effects, random effects, and generalised method of moments (GMM) models to account for endogeneity concerns, we examined the impact of ESG disclosure on the return on assets (ROA), return on equity (ROE), and Tobin’s Q. An ESG index was constructed through principal component analysis of individual environmental, social, and governance scores. Our results indicate a significant positive relationship between ESG disclosure and companies’ key performance variables across all models. These findings are consistent with stakeholder theory and signalling theory, suggesting that comprehensive ESG practices can lead to better financial performance and serve as a positive signal to stakeholders. The study also reveals sector-specific differences, with non-manufacturing firms showing stronger positive relationships between ESG disclosure and performance measures compared to manufacturing firms. Additionally, we find that firm size, age, and liquidity are important factors influencing the ESG–performance relationship. This research contributes to the growing literature on ESG and corporate performance in emerging markets, offering valuable insights for policymakers, investors, and corporate practitioners in Saudi Arabia’s evolving sustainable business landscape. Our findings underscore the importance of ESG disclosure in driving sustainable and responsible business practices in the region.

The integration of Artificial Intelligence (AI) in the FinTech industry has substantially reshaped operational workflows, product innovation, and risk management, all of which influence company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation. Utilizing a novel scalable multilayer network model with AI-driven Copula Nodes, the research reveals that operational efficiency is a significant driver of market value. However, the findings emphasize the necessity of a balanced strategy that integrates AI across all operational layers, particularly in risk management and product innovation, to maximize valuation. This study offers a comprehensive framework that enhances understanding of AI’s complex role in FinTech valuation, providing critical insights for industry stakeholders.

This study investigates the integration of natural intelligence (NI) and artificial intelligence (AI) within traditional firms, proposing a multilayer network framework to enhance economic value. The research explores the central question: How can the integration of NI and AI, facilitated by copula nodes, drive economic value creation in digitized firms? Through empirical testing across multiple industries—namely manufacturing, retail, and finance—the study evaluates the cost-benefit improvements associated with AI adoption. The findings reveal substantial efficiency gains, improved decision-making, and notable cost reductions, validating the proposed model's predictions. The study emphasizes the critical role of copula nodes in optimizing interactions between NI and AI, ensuring that their integration yields maximum economic benefits. By providing a novel framework for quantifying AI's economic impact within digitized firms, this research fills a significant gap in the literature on multilayer networks and intelligent systems. The practical implications are profound, offering a strategic pathway for firms to enhance profitability and competitiveness in the digital age.

With growing global emphasis on sustainable development and social responsibility, Environmental, Social, and Governance (ESG) ratings have emerged as critical metrics to evaluate commercial banks’ operating performance. The paper utilizes panel data spanning 2009 to 2022 from large state-owned and joint-stock commercial banks in China. It employs a two-way fixed effect model and robustness tests, alongside tests for heterogeneity analysis and moderating effect to conduct empirical analysis. The findings of the fixed effect model indicate that improvements in ESG performance have advantageous impacts on commercial banks’ operating performance, particularly with higher growth rate for operating income Robustness tests including mixed regressions, Tobit tests, variable substitutions, and tests of endogenous bias have validated that initial regression findings still hold. Tests of heterogeneity analysis reveal that among large state-owned commercial banks, banks with higher leverage, larger major shareholder ownership, and larger banks scales, ESG performance can significantly affect the banks’ operating performance, and the relationship exhibits positive. Furthermore, the paper finds that GDP growth rate, CPI and analysts’ coverage can strengthen the relationship between ESG performance and commercial banks’ operating performance through moderating effects. The research provides policy proposals and strategic recommendations tailored for policymakers, banks, and investors, building on these insights.

A significant part of the global economy is focused on financial technologies, such as digital wallets, a rapidly growing sector. In 2020, 2.4 billion people used digital banking services globally, and this figure is projected to rise to 3.6 billion by 2026. This study aims to evaluate the current state and future trends in research on digital wallets using scientific databases. The methodology employed was a scoping review that analyzed 778 documents after the eligibility process. The terms used were “digital wallet,” “mobile wallet,” and “e-wallet.” Results indicate that India, the United States, and China lead in digital wallet research. Factors such as data security, ease of use, and integration with traditional payment systems significantly influence the adoption of digital wallets. Despite numerous studies, this article provides the first in-depth bibliometric analysis using Scopus and Web of Science. There has been an exponential research increase over the last decade, with a shift towards technological adoption and innovation. Less developed but key topics such as “digital wallets,” “blockchain,” and “block-chain” are fundamental yet have potential for further development. Driving themes like “electronic money,” “e-wallet,” and “electronic commerce” are central and highly developed, indicating their relevance and high research activity.

We examine worldwide Property and Casualty and Life and Health insurance companies from 2004 until 2023, implementing panel regression models and mediation analysis to show that insurers raise their reserves when they face increasing costs for their potential environmental damages, ultimately reducing their profitability and underwriting capacity. Our findings extend to the insurance sector the previous evidence on banks, demonstrating that environmental damages could affect profits and solvency of financial intermediaries. These insights are important especially for insurance managers and regulators.

We use Fourier analysis to access risk in financial products. With it we analyze price changes of e.g. stocks. Via Fourier analysis we scrutinize quantitatively whether the frequency of change is higher than a change in (conserved) company value would allow. If it is the case, it would be a clear indicator of speculation and with it risk. The entire methods or better its application is fairly new. However, there were severe flaws in previous attempts; making the results (not the method) doubtful. We corrected all these mistakes by e.g. using Fourier transformation instead of discrete Fourier analysis. Our analysis is reliable in the entire frequency band, even for frequency of 1/1d or higher if the prices are noted accordingly. For the stocks scrutinized we found that the price of stocks changes disproportionally within one week which clearly indicates speculation. It would be an interesting extension to apply the method to crypto currencies as these currencies have no conserved value which makes normal considerations of volatility difficult.

Financial institutions are currently undergoing a significant shift from traditional robo-advisors to more advanced generative artificial intelligence (GenAI) technologies. This transformation has motivated us to investigate the factors influencing consumer responses to GenAI-driven financial advice. Despite extensive research on the adoption of robo-advisors, there is a gap in understanding the specific contributors and differences in consumer attitudes and reactions to GenAI-based financial guidance, particularly in the context of GenAI. This study aims to address this gap by analyzing the impact of personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI-provided financial advice on consumers' perceived authenticity, their utilitarian attitude towards the use of GenAI for financial advice, and their reactions to GenAI-generated financial suggestions. A comprehensive research model was developed based on Service-Dominant Logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA). The model was subsequently employed in a structural equation modeling (SEM) analysis of survey data from 822 mobile banking users. The findings of this study indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI's recommendations have a positive influence on consumers' perception of its authenticity. Moreover, we discovered a positive correlation between utilitarian attitudes and perceived authenticity, which ultimately influences consumers' responses to GenAI's financial advisory solutions. This is manifested as either a willingness to engage or resistance to communication. This study contributes to the research on GenAI-powered financial services and underscores the significance of integrating GenAI financial guidance into the routine operations of financial institutions. Our work builds upon previous research on robo-advisors, offering practical insights for financial institutions seeking to leverage GenAI-driven technologies to enhance their services and customer experiences.

The increasing rearrangement is a rewarding instrument in financial risk management. In practice, risk must be managed in different perspectives. A common example is portfolio risk which often can be seen from at least two perspectives: market value and book value. Different perspectives that is different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce the increasing rearrangement for application in financial risk management and apply the increasing rearrangement to the class of distortion risk measures. We derive formulas to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely Value at Risk, Expected Shortfall, Range Value at Risk, Conditional Value at Risk, Wang's risk measure. Finally, we present the connection of increasing rearrangement to inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as illustrative example for the method.

Financial technologies (Fintech) have become an indispensable part of most of the business infrastructure around the world. The Fintech ecosystem provides this solution by providing a suitable environment for all financial techno services to synergize. The objective of this paper is two folds; first, it aims to investigate & summarize Fintech programs products, and services in Saudi Arabia. Second, it maps the sustainable development goals (SDG) and indicators of the Fintech programs in Saudi Arabia. Secondary data was collected from published reports, institutional & governmental websites, official portals, and research papers focusing on the companies operating in Saudi Arabia. In the second step, the content analysis method was employed to investigate the features and characteristics of each program to map it to the sustainable development goals (SDGs). It was found that Fintech in Saudi Arabia is addressing SDGs goals 1, 2, 5 8 & 17. The paper also attempted to map further the SDG indicators with the products and services which Fintech companies are offering. This study will be beneficial for the institutions and policymakers of the Fintech industry to fill the gaps in addressing SDG goals which is a global agenda for eradicating poverty and offering sustainable development opportunities.

Purpose: This study's primary objective was to demonstrate a link between managerial traits and their significant impact on the financial performance of NIFTY 50-listed firms. These traits were age, gender, educational background, total time spent working. These criteria were seen as independent of the investigation. Design/ Method/ Approach: The data were collected from the official company websites, where the financial reports of the company have been published for the common people's access, and the traits of the management in terms of age, gender, and educational qualification have been covered from the Nifty 50 company’s websites.Findings: Regression analysis in terms of the multiple linear regression method has been applied to highlight the different managerial traits, i.e., gender as a trait of management personnel, that have a significant impact on the financial performance of the Nifty 50 companies, i.e., ROE and ROA.Originality/ Value: This study provided meaningful insights in terms of the different human traits that are prevailing in the management officials, which have a significant impact on the management decisions on the various parameters and have a significant impact on the financial performance of the companies in the Nifty 50.

The relationship between the returns and volatility of stocks, gold, bonds, and Bitcoin (BTC) to changes in inflation, interest rates (SBI), and exchange rates is a compelling topic, encompassing factors such as inter-asset interactions, and the roles of assets as hedge or safe-haven. We aims the inter-assetscorrelation using Granger Causality Test and the role of hedge or safe-haven assets against uncertainty using GARCH for normal condition and quantile regression in crisis. Last, we built an optimal portfolio using the Arbitrage Pricing Model (APT) consists of 28% stocks, 16% gold, 27% bonds, and 29% BTC, with returns 1.7796% over the Rf. Monthly trading records from 2018 until 2023 are used. The results demonstrate a correlation between the returns of gold and bonds with BTC, while stock volatility is found to correlate with BTC volatility. Furthermore we found a negative correlation between gold returns and exchange rates, though gold shows no correlation with other variables, indicating its effectiveness as a hedge in normal conditions. The bond serve primarily as a diversifier against exchange rate and inflation in normal, bearish, and bullish market conditions. These findings suggest that gold and bonds are more stable instruments and serve as risk mitigation strategy for BTC.

The following article analyses the relationship between Non-Performing Loans and innovation systems at a global level. The analysed data were obtained from the World Bank and the Global Innovation Index for the period 2013-2022 for 149 countries. The analysis was conducted using clustering with the k-Means algorithm, optimized with the Silhouette coefficient and the Elbow method. Subsequently, econometric analysis was carried out using panel data with Random Ef-fects and Fixed Effects. The clustering results show that the optimal number of clusters is three. The econometric analysis highlights the positive relationship between Non-Performing Loans, the exports of cultural and creative services, and the Innovation Input Sub-Index. The results also show the negative relationship between Non-Performing Loans, the H-Index, and the exports and imports of ICT services.

This study examines the integration of Environmental, Social, and Governance (ESG) factors into global banking practices and their impact on financial stability, measured by the Bank to Capital Asset Ratio. Through a comprehensive literature review and data analysis, the paper highlights the dual role of banks as catalysts for ESG investment and risk managers. It discusses the challenges and opportunities in transitioning to ESG-aligned business models, emphasizing the importance of regulatory frameworks and risk management approaches. The findings suggest that ESG integration enhances bank stability and competitiveness, contributing to sustainable economic development. The paper concludes with policy implications and recommendations for further integration of ESG factors into banking governance.

This paper explores the critical role of the banking sector in facilitating the ecological transition through the integration of ESG (Environmental, Social, and Governance) factors. By analyzing the intersection of ESG considerations and access to credit globally, the study highlights how banks can catalyze sustainable investments while balancing financial risks. Using a systematic literature review and k-Means clustering analysis, we assess the global landscape of credit access, emphasizing the implications of ESG adoption on financial stability and economic growth. The findings suggest that while ESG integration presents challenges, it offers significant opportunities for banks to enhance their competitiveness and foster resilient financial systems. The paper concludes by proposing policy recommendations aimed at improving the incorporation of ESG factors within credit risk management and promoting sustainable finance.

This study examines how profitability moderates capital structure and dividend policy in selected Sub-Saharan African manufacturing enterprises. The study analyses 2012–2022 data from 131 listed firms in six countries using a quantitative and ex post facto research design. Debt-to-equity, asset structure, debt-to-asset, and return on assets are variable of interest. The debt-to-equity ratio negatively affects dividend policy, but asset structure positively affects it, according to pooled OLS and moderated regression models. Profitability considerably moderates the association between debt-to-equity and dividend policy, while debt-to-asset ratio has a positive but negligible influence. The findings emphasize the role of profitability in financial decision-making and suggest that firms should balance their capital structure and profitability to optimize dividend policies. The study fills gaps in emerging economy literature and offers managers and policymakers practical advice to improve financial performance and shareholder value in Sub-Saharan African-listed manufacturing enterprises.

Fintech's emergence is one of the most significant developments in finance, and its application has been of much interest and popularity in recent decades. This study aims to explore the adoption of fintech in emerging economies by employing a structural equation model. The study's sample population consists of bank clients of Ethiopian commercial banks who have used financial technology platforms for at least one year. The total number of sample sizes is 440. The data collection instrument used for this study is a questionnaire (seven-point likert scales ranging from strongly disagree to strongly agree). Structural equation model (SEM) analysis was implemented using Amos software to examine the relationship between variables. The findings of the study support the hypotheses that various factors, including performance expectancy, effort expectancy, social influence, facilitating condition, traliaility, and user innovativeness, significantly influence the adoption and usage of fintech services. The study recommends that FinTech companies implement pilot programs to allow potential users to trial FinTech services on a limited basis before full commitment. Fintech industries should engage with local communities through events, seminars, and workshops to introduce fintech services. Having community leaders and respected individuals advocate for the technology can enhance acceptance.

Property Assessed Clean Energy (PACE) programs finance energy efficiency and renewable energy improvements in residential and commercial properties with a special tax assessment added to property taxes. The paper surveys the literature and documents the quantitative estimates of environmental and economic benefits. The paper extends to discuss the securitization of PACE loans. The issuance of PACE green bonds frees up capacity for more PACE improvements. In addition, we summarize the concerns raised after the programs have been implemented. Those concerns include consumer protection, audit after program implementation, and lien-related risks for lenders. We highlight the challenges and discuss measures to continue to grow PACE financing. The success of PACE programs will contribute to mitigating climate change and to achieving the United Nations Sustainable Development Goals (SDGs).

This paper investigates the long-run relationship between the U.S. dollar and Canadian dollar by defragmenting the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. The study also decomposes the real shocks into demand and supply factors from both Canada and U.S. and compare their impact on the nominal and real exchange rate. Results are compared to shocks driven by country specific nominal factors. The study is conducted using quarterly based data over December 1972 – December 2023. Findings suggest that the real shocks have a permanent impact on both the nominal and real exchange, compared with nominal shocks which have a temporary impact. Country-specific real supply-side factors have a more discerning impact than country-specific real demand-side factors. Country-specific nominal barely impacted the nominal and real exchange between U.S. and Canada.

This paper assesses the association of ESG scores with stock returns and highlights the moderating role of the COVID-19 pandemic and the country's governance. The study uses panel data regressions to analyze how ESG performance influences plain, absolute, and risk-adjusted returns in a sample of European and selected non-European countries. These suggest that higher overall ESG scores are related positively to financial performance, and this relation is enhanced during the COVID-19 pandemic. Specifically, the two dimensions of ESG that matter most are environmental and governance. Country-level governance is important because firms in well-governed countries amplify the benefits of high ESG scores. The opposite is true for the higher Controversies scores, whose bad financial outcome is magnified during the pandemic. These results present an argument for the resilience of firm financial performance, dependent on strong ESG practices and governance frameworks. This holds great interest for investors and policymakers in associating good ESG considerations with the effective management of financial risks, leading to sustainable returns during periods of widespread economic uncertainty.

The financial crisis augmented the need to predict real estate prices. The current study examines the application of Artificial Neural Network (ANN) model and Vector Autoregressive (VAR) model in forecasting real estate prices. The study uses daily observations, which accounted for a total of 2955 observations collected. We found that ANN outperformed significantly the VAR model providing evidence that these models can be used for valuation purposes in the area of real estate and financial markets. These findings are novel and important as they provide evidence of the usefulness of more sophisticated methodologies for market participants and regulators in real estate and financial markets.

The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the creativity of the project, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data was collected from multiple crowdfunding platforms for projects in Africa, that is, cross-country data from 54 African countries, to overcome data limitations from a single country. Econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges in the success of online crowdfunding. The signalling and agency theories support the finding. The findings were limited to crowdfunding projects on the African continent and are not necessarily applicable to some developed countries.

Ensuring banking stability is essential for economic resilience and the prevention of financial crises. Financial Stability Reports (FSRs) provide critical data on the financial system. This study advocates for the use of text mining tools to enhance the analysis of FSRs. Text mining offers a systematic method for extracting insights from unstructured data, allowing analysts to uncover detailed information on capital adequacy, risk management, and regulatory compliance. Additionally, this approach identifies emerging trends and vulnerabilities that may not be apparent through traditional analysis methods. The paper examines the application of text mining techniques to FSR analysis within the Republic of Armenia.

Digitalization has fundamentally changed the global economy and will continue to do so. This study investigates how the US Federal Reserve and US Treasury may work together to use decentralized finance (DeFi) systems to promote economic growth in local communities. The study looks into the potential for transformation and the difficulties in incorporating DeFi into conventional financial institutions. Research shows that the use of cash as a means of payment is widely expected to decline in the future. As a result, the public’s ability to make transactions using central bank money may decline rapidly. This study's goals are to solve economic issues, promote innovation, and increase financial efficiency. The use of in-depth interviews, theme analysis, case studies, stakeholder perspectives, comparative analysis, and document analysis is suggested as part of a qualitative research methodology. The goal of these approaches is to offer a sophisticated comprehension of the dynamics of collaboration and the consequences of DeFi integration in the context of the Federal Reserve-Treasury relationship. Important data sources that are necessary to answer the research question are indicated, such as US Treasury statistics, Federal Reserve publications, DeFi platforms, and International Monetary Fund economic indicators. Informed decision-making and policy formation may pave the way for a more inclusive and efficient financial ecosystem, which will eventually drive regional economic growth in the United States and promote monetary sovereignty for the aforementioned monetary entities. This can be achieved by investigating creative collaboration tactics between the Federal Reserve and the US Treasury.

This study focuses on examining the determinants of capital structure and the influence of taxes within Iran Khodro Company in the automobile industry from 2016 to 2023. The research analyzes the relationship between debt, taxes, and various company-specific factors. Findings indicate a direct relationship between taxes and debt, with financial leverage being the most significant element related to taxes. The study aims to provide valuable insights for managers and decision-makers in enhancing financial decision-making and shareholder wealth. Various studies have also been conducted on the impact of taxes and different factors on the capital structure of companies listed on the Tehran Stock Exchange, highlighting the importance of tax implications in financial decisions.

This study investigates the dynamic relationship between leverage and dividend policy in Nigerian manufacturing enterprises. Utilizing panel data from 26 firms between 2016 and 2020, our findings reveal that while dividend payments boost firm value, leverage typically diminishes it. Intriguingly, when leverage interacts with dividend policy, the combined effect on firm value becomes significantly positive. This observation, despite Nigeria's institutional differences from advanced economies, supports modern capital structure theories such as the pecking order and static trade-off theories. Our results suggest that for mature Nigerian companies with limited growth opportunities, high leverage and dividends effectively mitigate the "free cash flow" problem, curbing managerial tendencies to overinvest or underinvest. Thus, leverage and dividends serve as complementary mechanisms for monitoring and controlling managerial discretion. Consequently, firms with high leverage ratios can leverage dividend policies as disciplinary tools, and vice versa. This underscores the necessity for Nigerian manufacturing firms to carefully balance debt and equity to sustain market confidence and investment potential.

This study explores the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, focusing on ethical dimensions and perceived algorithmic fairness. From the perspectives of organizational justice theory and the heuristic-systematic model, we examine how constructs of algorithm transparency, algorithm accountability, and algorithm legitimacy influence users' perceptions of fairness, and subsequently, their satisfaction with and recommendation of AI-driven financial inclusion. Through a survey-based quantitative analysis, our results indicate that perceived algorithmic fairness acts as a mediating factor between the ethical attributes of AI systems and user satisfaction as well as their recommendation. Findings reveal that higher levels of transparency, accountability, and legitimacy enhance customers' perceptions of fairness, which in turn significantly increases both their satisfaction with financial inclusion services facilitated by AI and their likelihood to recommend them. This research not only contributes to the literature on AI ethics by highlighting the critical role of transparent, accountable, and legitimate AI practices in fostering satisfaction and recommendation among users, but also fills a significant gap in understanding the ethical implications of AI in financial inclusion contexts.

The current economic and political crisis has brought about a change in the environment in which Non-Governmental Development Organizations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds they receive and an increase in the population they must serve. The need then arises to have mechanisms that allow an analysis of the good work performed by the NGDOs. Knowing the efficiency of the NGDOs in the management of their previous projects can contribute towards improving their future achievements. The aim of this research is to establish some objective indicators that allow an evaluation of the efficiency of these organizations. Firstly, a detailed analysis of the regulation of the three Agencies is conducted (Spanish-AECID, European- EuropeAid and American -USAID). This allows us to synthesize the indicators of good performance of the NGDO based on the study of the eligibility criteria of public donors. The research concludes with the study of the efficiency following the Promethee Approach. Our results reveals that 44.6% of the NGDOs (33 out of the 74 studied) operate inefficiently, compared to 29.7% which are efficient.

The rapid evolution of financial technology (fintech) has significantly transformed the financial services landscape, creating opportunities for innovation and introducing new risks. Traditional financial institutions and fintech companies operate under different paradigms, resulting in disparate risk management practices. This paper proposes a comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML), the framework aims to ensure consistent and effective risk assessment across the financial sector. The financial services industry is characterized by rapid innovation, primarily driven by fintech companies offering various services that enhance efficiency, accessibility, and customer satisfaction. However, the growth of fintech brings substantial risks, including cyber threats, data privacy concerns, regulatory compliance challenges, and operational vulnerabilities. Traditional financial institutions prioritize stability, security, and compliance within established risk management frameworks. The divergence in operational models and risk management approaches creates a fragmented risk landscape, posing significant challenges to the financial system's stability and security. This paper identifies the critical need for a unified framework integrating the risk management practices of traditional financial institutions and fintech companies. The proposed framework leverages AI and ML to enhance the accuracy and comprehensiveness of risk assessments, utilizing a centralized data repository for real-time risk assessment. Unified risk management policies covering cybersecurity, operational risk, regulatory compliance, financial crime, and real-time monitoring and reporting tools ensure robust risk management protocols and prompt response to potential risks. Aligning with regulatory requirements and incorporating best practices from both sectors, the integrated risk management approach enhances the financial ecosystem's stability, security, and public confidence.

Finance is increasingly dominating the entire economy and determining social life more and more. This credit economy is unstable. To ensure stability, central banks intervene massively in the financial market. As a result, credit is growing, but economic growth is lagging behind. This has serious consequences. The evolutionary balance between "credit" and "growth", on which society's confidence is based, is in danger. This Imbalance leads to a serious loss of trust in the foundation of the market economy. In order to secure trust and master the two upcoming transformations - the transition to green production and the splitting up of global economy to competitive areas -, the state, or more precisely the central bank, must also transform itself. The central bank can add a second orientation to its hitherto one-sided focus on the financial system by supporting the development of civil society with direct financial transactions.

The aim of this study is to investigate the impact of delegated monitoring by the group leader and peer monitoring by group members on loan defaults in a group-based lending program in Vietnam. Data used in the study were collected from a questionnaire survey of 675 participants involved in group-based lending program in the Mekong River Delta, Vietnam. The group-based lending program employs a unique monitoring system that involves hiring the group leader to supervise the group and encouraging group members to monitor each other. The empirical findings derived from the Probit model indicated that delegated monitoring significantly reduces loan defaults, but there was no evidence supporting the effectiveness of peer monitoring within the group. Additionally, under the delegated monitoring scheme, group size plays an important role in decreasing loan defaults.

The purpose of this study is to explore the effect of Environment, Social, and Governance (ESG) scores on Return on Assets (ROA), Risk-Adjusted Return on Capital (RAROC), and Tobin's Q on firm performance. This study used 37 non-financial companies listed on the IDX between 2018 and 2022 in Indonesia companies. Linear and nonlinear panel data regression methods were used on the ESG score and its pillars, resulting in eight different models. The study found a U-shaped impact of ESG scores and social and environmental pillars on ROA. There is a positive linear relationship between ESG scores and the environmental and governance pillars with RAROC, while the social pillar shows an inverted U-shaped effect. However, ESG and its environmental and social pillars have a negative linear relationship with Tobin's Q. The findings suggest the need to manage ESG initiatives strategically, aligning them with financial objectives and awareness of how ESG factors relate to profitability.

In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking, and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. Utilising the Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, the study constructs a regression model to examine the relationship between the Return on Equity (ROE) and the components of the VAIC™. The findings confirm the study’s hypothesis, demonstrating that the Structural Capital Efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in ROE for the analysed institutions. The results suggest that the Intellectual Capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period.

This paper examines the impact of random sample sizes on the extreme value theory of competing risks, a significant area in finance and environmental science. We capture limit distributions of two extreme types under random sampling sizes, known as accelerated mixed l-max and p-max stable type distributions. The study presents results for both maxima and minima in competing risks scenarios, addressing cases of independent and non-independent random sample sizes. Numerical examples validate our theoretical findings, demonstrating the applicability of our approach to various random sample size distributions, including time-shifted Poisson or binomial, geometric, and negative binomial distributions.

Environmental, social, and governance (ESG) investment has become a common trend in capital markets, and many institutional investors have publicly declared their long-term ESG investment target (ESGIT). This study focuses on investors who conduct strategic asset allocation (SAA) using composite indices but establish their ESGIT outside the SAA process. A mismatch could arise between investors’ planned and actual portfolios because they must pursue their ESGIT by asset selection, which inevitably makes the constituents of their portfolios different from those of composite indices. To scrutinize the potential existence of a misalignment between their SAA and ESGIT, this study analyzes actual stock and bond indices over the past decade using traditional mean-variance optimization. The results reveal significant differences between the expected and realized performances, and the misalignment is more significant when the analysis period is divided into two sub-periods: stable and volatile. Specifically, the results indicate that the efficient frontier constructed using ESG indices significantly differs from that constructed using composite indices, and that the difference between these two efficient frontiers becomes more significant during volatile periods. Furthermore, the results show that investors cannot meet their targets if their ESGIT is established outside the SAA process.

The West Pomeranian Anti-Crisis Package was an effective tool of the Voivodeship Executive Board of West Pomeranian self-government, which was used to combat and counteract and counteract the social and economic effects of the COVID-19 pandemic. The Anti-Crisis Package was prepared with a view to undertaking systemic, but also point-based measures in order to save and protect regional health care, entrepreneurs, the labour market, but also to use the existing social capital of non-governmental organizations involved in measures reducing the effects of the COVID-19 pandemic. The West Pomeranian Anti-Crisis Package, as evidenced not only by the financial outlays incurred, but also by the outcome of implementing measures achieved in 2020 and their continuation in 2021.

The present study discusses how adopting cryptos affects financial inclusion in developing economies. Primary constructs like Financial Inclusion (FI), Perceived Economic Empowerment (PEE), Trust in Financial Institutions (TFI), User Satisfaction (US), and Cryptocurrency Adoption (CA) were tested through Structural Equation Modeling (SEM). The results indicated that CA significantly and positively influenced FI, US, TFI, and PEE. These relationships extend to the interaction effects: US, TFI, and PEE all positively related to FI. It is a reflection of cryptocurrencies as an opportunity to redress most of the afflictions characteristic of traditional finance systems and to promote financial inclusion and economic empowerment in developing countries. Future research should also investigate whether digital literacy and regulatory environments support cryptocurrency access.

In response to the escalating climate-related catastrophes of recent decades and their significant impacts on global transportation systems, this study explores the concept of resilience bonds as a novel financial tool for enhancing supply chain sustainability. The research applies a case study from China to demonstrate the application of resilience bonds in funding infrastructure projects that aim to enhance the sustainability and resilience of supply chain networks. The study examines the market mechanisms and valuation models of resilience bonds, assessing their potential to mitigate risks associated with transportation failures and to reduce economic losses from unexpected disruptions. By applying empirical data, the research quantitatively evaluates the impact of resilience bonds on the sustainability and reliability of transportation systems. The findings show that resilience bonds can significantly contribute as a sustainable solution to enhance supply chain resilience and sustainability, offering insightful alternatives for financing resilient infrastructure projects. This study not only supports policymakers, investors, and business leaders in understanding innovative funding mechanisms but also makes a significant contribution to resilience financing for sustainable transportation within supply chains, especially in the context of global disruptions.

Consumer protection in the financial market has several dimensions. From a formal point of view, consumer rights are guaranteed by law. Market asymmetries are eliminated through the obligation to provide consumers with complete and objective information about financial products and services, honest marketing communication and professional sales practices. Programs are implemented in schools and the media to promote knowledge and responsible use of financial products and services. Despite the efforts made, the number of incorrect and suboptimal financial decisions is so high that the risk of households falling into excessive debt remains significant. The limited effectiveness of the law led to the claim that only effective education can reduce the risk of suboptimal financial decisions. Unfortunately, the efforts made in this area are not fully satisfactory. The aim of the article is to review scientific research on the financial knowledge of consumers in Central Europe and compare it to the results of a study conducted in Poland in January 2024. The results of the analysis could indicate the direction of further research on the possibilities of eliminating suboptimal financial decisions of consumers without limiting - for their better protection - access to financial products.

We evolve our past models PYR, Loukeris, et al. (2016) to provide a competitive system whose infiltration of categorical information and fundamentals into advanced higher moments, supports a more objective Portfolio Selection, aided by Intelligent Computing. The system of Portfolia Yield Reactives (PYR) searches for hidden prototypes into big data of accounting and financial statements, restricting malicious patterns such as hoax, noise, manipulation, incorporated to a novel optimal portfolio selection method.

In this paper, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets.

The corporate strategic planning of businesses in sub-Saharan Africa (SSA) largely focuses on immediate financial performances with minimal credence to social sustainability. Thus, studies on the linkage between corporate governance and sustainability reporting have focused on developed economies. This study therefore investigated the role of institutional ownership in the impact of board committee characteristics on social sustainability reporting. The study involved strongly balanced panel data with 1,969 observations of 275 publicly listed non-financial firms in SSA within the timeframe of 2012 to 2021. Data was analyzed using STATA 14.1. The hypotheses were tested using the two-step system of the generalized method of moment (GMM) using the Arellano-Bond dynamic panel-data estimation method. The rate of social sustainability reporting was 39.4%. Relatively, Mauritius and South African firms had the most effective board committee characteristics and higher levels of social sustainability reporting. Institutional ownership had no significant effect on social sustainability reporting. Institutional ownership moderated the effect of sustainability committee independence and sustainability committee gender diversity on social sustainability reporting negatively. The paper presents a new perspective on corporate governance and social sustainability literature by examining the effect of institutional ownership on board committee characteristics and social sustainability reporting in SSA.

Working capital management (WCM) is critical because it affects a company's profitability, liquidity, and investment decisions, all of which have an impact on financial performance. As a result, effective and efficient working capital management is an essential component for commercial and service businesses. Given the importance of the commercial and services industries to the Kenyan economy, the goal of this research was to look into the impact of working capital management on the financial performance of these firms, particularly those listed on the Nairobi Securities Exchange (NSE), from 2003 to 2022. Working capital management was measured using the average age of inventory, average collection period, average payment period, and cash conversion cycle, whereas financial performance was measured using return on asset, return on equity, and net operating profit margin. Using panel regression analysis, the results showed that the average inventory age, average collection period, average payment period, and cash conversion cycle were all negatively related to financial performance for NSE-listed commercial and services firms. Based on the findings, it is recommended that Kenyan commercial and services firms adopt prudent optimal working capital management practices in order to improve firm financial performance and maximize shareholder wealth.

Emission trading schemes (ETSs) have become vital tools in striving toward global emission reduction objectives and promoting a sustainable economy. This paper empirically investigates the impact of market conditions on interconnectedness within precious metals, clean energy, green bonds, and carbon markets, focusing on China and the EU ETSs as the study subjects. A TVP-VAR-based asymmetric connectedness approach is adopted to capture the evolving relationships between the variables of interest in the time–frequency domain. The empirical findings reveal that: First, time-varying asymmetric connectedness and spillover effects among clean energy, precious metals, green bonds, and carbon markets are identified in both time and frequency domains under diverse market conditions, with long-term connectedness asymmetry being particularly sensitive, although spillovers exhibit variations. Second, the role and intensity of net transmitters or receivers vary across market conditions and frequency domains, indicating the complex and nuanced nature of market interactions. The EU ETS demonstrates a higher level of connectivity with global markets for clean energy, precious metals, and green bonds when compared to the Chinese ETS. However, it also shows greater susceptibility to fluctuations in market conditions. These results have significant implications for both investors striving to build sustainable diversified portfolios and regulators endeavoring to craft effective policies for risk regulation and sustainable development.

We examine the relation between environmental, social, and governance commitment levels (ESG) and firm dividend payer status. Given that larger and more profitable firms are positively associated with both payer status and ESG, it could be that ESG and dividends are complements. However, given that both dividends and ESG relate to firm spending decisions, it may be that the choice is “either/or” and that ESG and dividends are substitutes. We document a positive relation between ESG and dividend payer status in U.S. firms over the period 1991-2016. In particular we find that the proportion of dividend payers is roughly 13% higher for firms with positive ESG compared to those with negative ESG. Including ESG in the models used to predict payer status provides, on average, a nearly 26% improvement in relative forecast accuracy. Our results are robust to estimation techniques and the inclusion of variables known to be determinants of payer status.

Green finance in the context of sustainable development sits within the broader discourse of environmental economics and sustainable finance. Their integration has become imperative in addressing global challenges, aiming to understand how financial mechanisms can be aligned with sustainability goals, investigating the role of green finance in promoting environmentally friendly investments and fostering sustainable development. This bibliometric analysis explores the evolution, trends, and challenges in green finance research. It examines 436 articles published between 2016 and 2024, revealing insights into influential publications, authors, journals, institutions, and countries engaged in green finance for sustainability. The study identifies China, the UK, and Pakistan as leaders in research output and citation impact. Furthermore, it highlights the interdisciplinary nature of green finance, reflected in diverse publication outlets spanning environmental, social, and economic domains. The analysis underscores the increasing global interest in green finance, as evidenced by the growing citation rates over time. Key findings include the pivotal role of green finance in energy efficiency, renewable energy development, and the promotion of sustainable economic growth. Overall, this research provides valuable insights for policymakers, researchers, and practitioners, emphasizing the importance of interdisciplinary collaboration and continued research efforts in advancing sustainable finance agendas.

Swimming with Sharks: Can Small Investors Compete with Buffett? Pankaj Topiwala. FastVDO LLC, 3097 Cortona Dr., Melbourne, FL 32940 Abstract: Millions of small investors fare poorly in the financial markets. For them, the markets are like turbulent seas. Braving them in a mere skiff, a major storm can spell financial doom at any time. But the reality is that most investment funds also underperform the market. Against this backdrop, there are some savvy investors who have consistently beat the market, for decades! Names such Benjamin Graham, John Templeton, Peter Lynch, or Jim Simons come to mind. But no investor has beat the market as decisively or for as long as Warren Buffett, getting an annual return of nearly 20% since 1965! Is there a way that the small investor can swim with these sharks? These legendary investors developed a refined sense for valuing companies through fundamental analysis (hard work), investing in solid companies that are deep in value. The untrained small investor cannot hope to replicate their method. To enter these waters, she needs a new boat: algorithmic trading. Not the kind that trades in microseconds (high frequency trading), which is again out of her reach, but the type that trades only a few times a year (very low frequency). Using an approach developed and tested in previous papers, we go head-to-head with Buffett's famous BRK-A fund and compare performance over the 44-year period 1980-2024. Buffett's BRK-A earned 19% annual return over the 44 years. We also test a 50-year period, the historical limit for Nasdaq. By using simple algorithmic trading methods, we show that the small investor can indeed keep pace with Buffett, without the hard work. Our simple methods allow us to obtain both strong gains, and reduced drawdowns, helping us to survive the major market downturns known as Black Swan events. This win-win is good news for small investors worldwide.

Establishing systemic communications between the project plan, stakeholders, and the organization through integrated management meets stakeholders' demands and managerial expectations in project-oriented organizations. Performance assessment can be defined as a control system in any company that reviews its daily operations and evaluates how well it has achieved its goals. This study aims to investigate the impact of an integrated project management system on the financial performance of the Organization for Renovation, Development, and Equipment of Schools. This applied research is descriptive-analytical. The statistical population comprises 2,700 employees of the organization, with a sample size of 340 individuals examined. The reliability of the designed questionnaire was confirmed by experts in the organization using Cronbach's alpha (α=0.91). Data were analyzed using SPSS version 20 and AMOS 18 software through descriptive statistics and confirmatory factor analysis. The results indicate that the seven dimensions of the integrated project management system (cost management, quality management and continuous control, change management, systems and information management, human resources management, interaction and communication management, procurement management) have a positive and significant impact on the financial performance of the Organization for Renovation, Development, and Equipment of Schools.

We use the Maximum Overlap Discrete Wavelet Transform (MODWT) to analyse the interconnectedness between the stock markets of continental Europe and the BRICS countries. More specifically, we examine the presence of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the EuroZone Sovereign Debt Crisis (EZDC). We find evidence of co-movement and volatility spillover at short time scales between the Eurozone equity market and the equity markets in South Africa and Russia, with the Eurozone stock market leading the BRICS markets. The presence of co-movement in short time scales is indicative of the presence of financial contagion. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European sovereign debt crisis. The absence of financial contagion observed in these three BRICS countries during the European sovereign debt crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels.

Using a sample of 103 surviving and non-surviving global bond funds over a long sample period of 1998 to 2022, we perform a comprehensive analysis of the funds under our modified Sharpe’s (1992) style analysis model. We find that dynamic style analysis is useful in explaining time-series and cross-sectional and in predicting future returns of global bond funds. The dynamic style analysis also allows us to examine asset allocation over time. The results show that, during 2001 to 2014, funds shift their allocations from intermediate-term bonds to world corporate bonds and from world corporate bonds to emerging market bonds in search of higher returns at the price of credit quality.

Sustainability experienced significant global momentum in recent decades. The emergence of the sustainability movement in the Financial Sector was prompted by the increasing global climate change resulting from environmental deterioration. Primarily associated with the Financial Sector, it has increasingly been recognised that Financial Institutions are significant contributors to global climate change, both via direct and indirect means. This has resulted in a heightened emphasis and demand from stakeholders for the implementation of Sustainability Principles on a worldwide scale. This study presents a methodology that utilises the various dimensions of Sustainable Finance through the concept of Triple Bottom Line to determine the factors influencing the Sustainability Performance of Financial Institutions. The suggested approach is grounded and supported by the current body of literature. The study presents a hypothesis to investigate the correlation between factors of Green Finance Practices and Sustainability Performance of Banking Sector. The suggested framework can be utilised by regulatory bodies and individual financial institution of a country to identify the elements that influence the Sustainability Performance of Banking Sector.

This study investigates the impact of fintech, particularly blockchain technology and non-fungible tokens (NFTs), on the art market. As fintech has been widely accepted, the art market has undergone transformations, including increased transaction transparency, enhanced liquidity, improved risk management capabilities, reduced entry barriers to the market, and more developed copyright protection. Using a Vector Autoregression (VAR) model, this paper clarifies the significant effects of fintech, especially the NFTs, on the size and profitability of the art market. The findings not only emphasize the importance of NFTs in developing the art market and increasing investment returns but also provide insights and constructive suggestions for the future growth and sustainable development of the art market. By fostering transparency, efficiency, and accessibility, fintech contributes to a more resilient and sustainable art market, ensuring its long-term prosperity and cultural enrichment.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, we found that in the short-term, oil price volatility has negative asymmetric effects on the market returns. Specifically, in the short-term, a one percent increase in oil price volatility immediately leads to a 2,6868 percent decrease in the market returns while a similar magnitude decrease in oil price volatility is associated with 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the long-term, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that 98.21 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

This paper explores the emergence of Sustainability-Linked Bonds (SLBs) as an innovative instrument to finance sustainability objectives. SLBs have emerged as a transformative financial instrument, establishing an explicit link between cost of funding and sustainability objectives. SLBs were first launched in 2019 and represent 7% of labelled bonds, still dominated by the more established green bonds. Issuance of SLBs now exceeds $250 billion. Following initial enthusiasm, they are now facing criticism. In the context of the growth of sustainable finance and concerns of greenwashing, this paper investigates ways to enhance the design of SLBs. Drawing on literature and expert interviews, the findings highlight the strong potential of SLBs to contribute to sustainability financing, especially in hard-to-abate sectors. The paper identifies the key positive attributes of SLBs and their limitations in five areas: key performance indicators’ (KPI) selection, sustainability performance targets’ (SPT) calibration, bond characteristics, reporting practices, and resource requirements. Recommendations to financial intermediaries, issuers and policy makers include defining standardized KPIs based on a materiality assessment, requesting SPTs to be supported by science and in line with an issuer’s core strategy, tailored step-up mechanisms and avoidance of call loopholes, and stricter reporting. SLBs represent an opportunity to add rigor to sustainable finance and better price externalities, where material topics have an explicit impact on cost of funding and credit ratings.

In the present study we examine the relationship among sovereign yields, temperature and precipitation using a large monthly panel data set which consists of 20 eurozone members, over the period 1980M1 – 2023M4. To account for possible asymmetries along the distribution of the climate variables we assume a quadratic modelling specification and apply various mean panel estimation techniques of heterogeneous coefficients. At a next step, to consider possible nonlinearities in the distribution of the dependent variable (sovereign yields), we apply the quantile via moments methodology of Machado and Santos Silva (2019), which accounts for possible cross-sectional dependence and slope heterogeneity. We contribute to the existing literature in two main ways. First, by applying a quantile methodology that provides a more in-depth analysis of the climate effects along the distribution of the sovereign yields, especially in the presence of non-normally distributed data. Second, we find that climate change, as proxied by higher temperatures or lower precipitation (drought), will increase the sovereign risk of all countries, but the magnitude of the impact will be higher for countries that are already characterized by higher sovereign risk levels and/or face extreme weather conditions (hotter countries and/or countries with low levels of precipitation).

As per the portfolio theory proposed by Markowitz (1952), diversification has the potential to mitigate portfolio risk. Further benefits can come from diversifying internationally since poor correlations among international markets can cause risks that are systematic in one market to become unsystematic in terms of the global economy. SEBI granted approval for Indian Mutual Funds to invest in ADR/GDR released by Indian Companies in 1999. Subsequently, SEBI has eased the rules for investment and several times it has now allowed Indian mutual funds to invest in foreign equity subject to certain limits. While SEBI had approved Indian mutual funds to invest in ADSs of Indian companies way back in 1999, the growth in investment in foreign securities started with the outbreak of COVID-19 in 2020. The exponential growth in the value of stocks in the US, driven largely by growth in technology stocks, propelled investment by Indian Mutual Funds that increased investment by ten-fold to over INR 30,000 Crores. While several focused funds with a mandate to invest in US stocks formed the bulk of the investment, other flexi cap and blue-chip funds, which had erstwhile fully invested in Indian stocks, also joined the bandwagon. While research on other developed economies has revealed the positive impact of international diversification, the Authors current research examined the advantages of international diversification on the Indian mutual funds’ performance. Authors used methods like t-test, F-test, Anova, Levene test for homogeneity and regression to check the validity of our results. Authors found that the international diversification by Indian equity mutual funds that invest in both US and Indian equities outperform benchmarks one third of the time. More importantly, by diversifying into international equities, these mutual funds are able to lower their volatility and therefore provide better risk reward returns. This is the first major study of this kind about Indian mutual funds investment in foreign equities and would be valuable investors, mutual fund managers and the regulator. As investments by Indians and Indian equity mutual funds are expected to increase significantly in the coming years, this study is timely and relevant at the current context of Indian mutual fund industry.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

This paper investigates calendar anomalies, volatility patterns, and the best forecasting model for predicting volatility in the cryptocurrency market, focusing on ten prominent cryptocurrencies: Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Spanning from January 2016 to December 2023, the study utilizes sophisticated statistical models such as GARCH (p,q), EGARCH (p,q), and GJR-GARCH (p,q) to analyze precise changes in market dynamics and the impact of day-of-week fluctuations on cryptocurrency returns. Empirical evidence reveals significant findings regarding the persistence of volatility, positive and negative news effects on volatility, and day-of-week effects on cryptocurrency returns. Post-COVID-19, Sunday emerges as the least volatile day for cryptocurrencies, while Thursdays and Tuesdays exhibit greater volatility. Binance, Ethereum, Dogecoin, and Tether show anomalies where returns on Tuesday and Thursday significantly differed from those on other days of the week. Many other currencies, like the USD coin, Cardano, and Ripple, show anomalies only in the pre-COVID-19 period. The findings highlight the best forecast model for volatility for each top cryptocurrency, offering practical implications for investors, traders, regulators, and policymakers. These insights emphasize the importance of understanding and addressing calendar anomalies in the cryptocurrency market for informed decision-making, trading strategies, regulatory frameworks, and market stability.

This paper utilizes the 2020 financial data of A-share listed companies, both ST and non-ST, as the data sample. The model is constructed employing the Factor-Logistic fusion algorithm. Eight key indicator factors were selected from the frameworks of profitability, solvency, operating capacity, development potential, shareholder retained earnings, cash flow and asset growth. An independent indicator system was subsequently established to address the issue of collinearity among risk indicators. The results of model are presented in the form of probabilities, enhancing the interpretability of model. The weights of the features indicate the influence of different features on the final outcome. The model achieves a prediction accuracy of over 89%, with an AUC score exceeding 95%. Finally, by applying the principles of interval estimation theory in statistical hypothesis testing, the risk levels are categorized as A-level representing significant risk, B-level representing moderate risk, C-level representing minor risk and D-level representing no risk. This paper aims to provide a comprehensive definition of a universal financial risk management warning model for enterprises, applicable to all enterprises in China.

The integration of commodities into stock exchanges marked a pivotal moment in the analysis of price dynamics. Commodities, essential for both daily sustenance and industrial processes, encompass hard commodities like metals and energy, alongside soft commodities such as agricultural produce. This paper investigates the implications of commodity price volatility on commercial and financial landscapes, recognizing its profound impact on global economies. This study employs a meticulous methodology, delineated into four stages: identification, control, and final selection of academic articles, culminating in their tabulation. Methodologically, a literature review serves as the primary tool for data acquisition, with a focus on existing scientific knowledge to bridge gaps and propose avenues for further inquiry. Drawing from reliable sources, including Google Scholar and Science Direct, the research navigates through a wealth of scholarly discourse on commodity market dynamics. By analyzing trends in academic publications, particularly focusing on the interplay between volatility spillover and gold, this paper provides insights into the evolution of research paradigms over time. Ultimately, this paper not only synthesizes existing knowledge but also identifies lacunae for future investigation, thereby contributing to a deeper understanding of the complexities surrounding commodity markets and their ramifications on global economies.

This study investigates how customers’ technological readiness affects service quality and the adoption of self-service banking by developing an empirical model. The model was proposed to explore the relationships between technology readiness, self-service quality, and customer satisfaction. The hypotheses and stability of the model were tested using structural equation modeling. The findings revealed that technology readiness, including the optimism, innovation, and insecurity dimensions, had a significant positive impact on self-service quality. In addition, service quality fully mediated the relationship between technological readiness and customer satisfaction. These results suggest that when customers are ready to embrace and use new technology, their evaluation of service quality is high. Thus, bank managers should drive customer perceptions of the quality of self-service banking by improving their technological readiness through various measures. Furthermore, based on the mediating effect of self-service quality, banks must be aware that improving service quality can enhance customer satisfaction and maintain a competitive advantage.

Emergence of COVID-19 had profound impact on global stock markets, including Indian mar-ket. Key indices like NIFTY50 and Sensex dropped by approximately 40% within two months in 2020, which also affected major mutual funds, leading to significant depreciation of their Net Asset Value. Securities and Exchange Board of India (SEBI) allows mutual funds to use deriva-tives for hedging purposes, capped at 100% of the scheme's net assets covering debt, equity, and derivatives. However, circular amendment by SEBI in January-2019 permitted covered call strat-egies on constituent stocks of Sensex and NIFTY50 for mutual fund schemes, excluding ETFs and Index Funds. This study investigated whether mutual funds increased their derivative us-age as risk mitigation strategy during COVID-19 pandemic, analyzing derivative holdings from 2019-2023. Surprisingly, findings indicated a decline in derivative holdings in 2020 compared to pre-pandemic levels. Statistical tests and performance evaluation metrics such as T-test, F-test, Treynor Ratio, Sharpe Ratio, and Jensen's calculations were employed to assess mutual fund performance. Although derivative-holding mutual funds didn't outperform benchmarks in aver-age returns, it showcased significantly reduced volatility, offering investors favorable risk-return trade-offs. research sheds light on derivative utilization in Mutual Fund industry, potentially in-forming regulators, fund managers, and investors in their decision-making

In this research paper, we address the critical role of data security in the rapidly evolving FinTech sector. We explore the challenges and strategies for establishing robust data security systems, emphasizing the consequences of data breaches and common vulnerabilities. The paper highlights the efficacy of technologies like encryption, blockchain, and cloud security, supported by real-world case studies from leading FinTech companies. Additionally, we examine regulatory frameworks and standards essential for maintaining data security. Concluding with the proposal of an integrated security system model, the paper underscores the synergy between technological innovation, regulatory compliance, and proactive risk management in the FinTech industry

In the rapidly evolving FinTech sector, risk management is paramount for dealers and brokers. This paper underscores the significance and progression of risk management within this realm. Initially, it reviews the traditional risk management technologies and their inherent limitations. The discourse then shifts to contemporary solutions, specifically emphasizing the role of artificial intelligence, machine learning algorithms, and big data analytics in forecasting and mitigating potential risks. The paper also presents case studies of companies effectively utilizing these advanced techniques, highlighting their benefits in real-world scenarios. Additionally, it addresses forthcoming challenges, including regulatory compliance, data privacy issues, and the imperative for strengthened cybersecurity measures. The conclusion acknowledges that the adoption of these innovative technologies not only boosts operational efficiency but also markedly diminishes financial risks, thereby contributing to a more stable and reliable financial market

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. Firstly, we consider the conventional GARCH involved volatility models. We next extend the corresponding GARCH based forecasting and calculate a specific probability associated with the predicted volatility levels. Since the probability evaluation is based on a stochastic model, we next develop an additional data-driven estimation of this probability. The proposed advanced statistical estimation uses real (historical) market data. The obtained theoretic results for the statistical probability of levels are also discussed in the framework of the integrated volatility concept. A possible application of the established probability estimation to the volatility clustering problem is also mentioned. Our paper includes a concrete practical implementation of the resulting (combined) volatility prediction tool and considers a novel module for trading and volatility estimation in crypto markets recently developed by 1ex Trading Board group in collaboration with the GoldenGate Venture. Moreover, we discuss shortly a possible using of the proposed model based and data-driven volatility prediction methodology in the financial risk management.

The rise of fintech in the financial sector presents a transformative shift towards digitalization and sustainability, leveraging technologies like AI to minimize environmental footprint. Neobanks not only challenge traditional banking models but also offer innovative solutions that align with sustainable objectives. The purpose of this paper is to analyze the impact of neobanks on global sustainability, from economic, environmental, and social point of view. A comprehensive literature review of existing literature and current sustainable practices of neobanks was conducted. We have found that neobanks significantly positively contribute towards environmental sustainability with reduced paper use and logistics requirements of banking services. By offering more accessible and affordable banking services they importantly contribute towards higher financial inclusion, and with innovative products towards more competitive and innovative financial markets. AI-based tools they employ are increasing financial literacy and social inclusion. We also highlight concerns regarding electronic waste management, potential high energy consumption, required digital literacy and cybersecurity risks. We conclude that despite the mentioned risks, neobanks importantly contribute to global sustainability in many ways, and will even more in the future. Our findings can help neobanks shape sustainable practices and guide policymaking, as well as spread awareness of sustainable impact of banking services.