There is an increasing concern on the quality of jobs and productivity witnessed in the flexible employment arrangements. The aim of this study is to examine the relationship between various employment arrangements and the workplace performance. Home-based working-teleworking, flexible timing and compressed hours are the main employment types examined using the Workplace Employee Relations Survey (WERS) in years 2004 and 2011. The workplace performance is measured by two outcomes- the financial performance and labour productivity. First, the determinants of those flexible employment types are explored. Second, the ordinary least squares (OLS) method is followed. Third, an instrumental variable (IV) approach is applied to account for plausible endogeneity and to estimate the causal effects. The findings reveal a significant and positive relationship between these types of flexible employment arrangements and the workplace performance. Education, age, wage, quality of relations between managers-employees, years of experience, the area of the market the workplace is operated and the competition are significant factors and are positively associated with the propensity of the flexible employment arrangements implementation. This can have various profound policy implications for employees, employers and the society overall, including family-work balance, coping with family demands, improving the firm performance, reducing traffic congestion and stress among others. It is the first study that explores the relationship between flexible employment types and workplace performance using an IV approach. This allows us to estimate the causal effects of flexible employment types and the possible associated social implications.

The COVID-19 pandemic and the Movement Control Order (MCO) have had a negative impact on the financial well-being of low-income families in urban areas. The study involved respondents living in the Public Housing Project (PPR) residential areas in Kuala Lumpur – the capital of Malaysia. The key finding is the financial well-being of low-income urban families was negatively impacted due to the COVID-19 pandemic and the MCO implementation. Furthermore, the impact on the financial well-being of low-income urban families is significantly difference in terms of types of family, type & sectoral employment, type of home ownership, household monthly income and education level. Reforms to the financial assistance system and community empowerment of low-income urban families are necessary to increase the community's preparedness and resilience in the face of new shocks in the future.

This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. Traditional financial risk assessment methods are often unable to effectively cope with the dynamic changes and complexities of the financial market, but RL technology provides innovative solutions for modern financial risk management by means of real-time adjustment and adaptation mechanism. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.

Background: Parkinson’s Disease (PD) is a long-term condition, which is more common among people aged 65 years or more. People with Parkinson (PwP) tend to lose motor functions and find themselves in more fatigue and pain, sometimes leading to anxiety, distress and in extreme cases, even depression. So far, there is no cure for PD and they need support from family members, healthcare professionals and carers, as the disease progresses. A large proportion of carers for PwP are their spouses. Progression of PD can result in loss of employment, reduced work hours or premature retirement, for both PwP and their carers. This poses a huge financial burden as income reduces on one hand, and expenses increase on the other. There is a lack of evidence on the societal costs of Parkinson's disease, and this study tries to fill that gap. Objectives: To examine the impact of financial distress on quality of life (QoL) and wellbeing of PwP and their carers in the UK. Methods: A mix of online and postal, cross-sectional survey was administered, which received responses from 853 respondents, out of which 776 were PwP and 546 were carers. The survey sought data on management of the socioeconomic, health and living conditions of PwP and their carers. Net effect of financial distress (cost of healthcare use, non-health expenditure, societal costs and other direct / indirect cost components) on QoL (and VAS scores) as well as wellbeing scores (life satisfaction and happiness scores) of PwP and their primary carers are estimated using simple linear regression models. Results: Financial burden of treatment increase whereas quality of life and wellbeing of PwP decrease with progression of PD. Similar association but of a milder magnitude is observed on quality of life of their primary carers. A strong negative association is found between quality of life of PwP, with financial burden, while holding for demographic and socioeconomic factors, and for disease progression and treatment. The QoL of carers increase up to a financial burden threshold, before it starts declining. This could be because of the increased expenditure in initial phases is associated with improved activities of daily living and lesser reliance on carers. Financial distress is more pronounced among PwP having annual household income below £20,000. Despite a small representation of persons from ethnic minority (5 per cent representation), it is observed that the impact of financial distress could be more pronounced on PwPs from other ethnicities. Also, it would be more on women than on men, irrespective of ethnicity. Conclusion and Policy Implications: Cost of care cuts both ways, reduced or lost employment as well as increased expenses on healthcare. This results into a considerable financial distress and deterioration in quality of life and wellbeing of both PwP and carers. Most carers are spouses of PwPs, thus their physical and psychological health and wellbeing is found to deteriorate with the progression of PD due to increased burden of care. Therefore, informal / family carers should get support in the form of respite care. This can help revitalise their energy and psychological wellbeing. Financial distress indicators need to be explored further.

For several years, commercial banks in Poland have been reporting activities related to the impact on the environment, society, and corporate governance (ESG). However, only new guidelines, mandatory for many entities, including banks, will allow for comparing these reports, which will be of great importance mainly for investors. The forms of these reports were and still are different, difficult to compare in individual years, and difficult to compare between banks. The article aims to present the banks' preparation for the new reporting rules based on the latest ESG reports. The research was conducted in 4 groups of commercial banks operating in Poland, which are the largest companies listed in the WIG Banks sub-index of the Warsaw Stock Exchange. Gaps in the preparation of these banks for non-financial reporting were identified. The non-financial reports of the banks studied have significant information potential that can be used by various stakeholder groups, including investors, customers, employees, regulators, and local communities. However, the comparability of ESG reports is one of the key challenges faced by both reporting banks and users of these reports. The research results can be used both in scientific works and by bank representatives to improve non-financial reports.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

This study introduces a novel method that combines Generative Adversarial Networks (GANs) with conformal prediction to revolutionize the domain of stock ticker forecasting. Our approach enhances prediction accuracy and offers dependable prediction intervals to gauge uncertainty, surpassing current GAN-based techniques in unpredictable market scenarios. We made a validation using AAPL stock data, showing the ability of our model to generate accurate forecasts and reliable assessments of prediction uncertainty. The prediction intervals generated provide investors and risk managers with a powerful tool to make informed decisions. Our approach represents a notable advancement in financial prediction as it improves the clarity and reliability of forecasts. By incorporating conformal prediction into the GAN framework, the reliability of forecasts is improved, providing a thorough evaluation of uncertainty that is crucial for successful risk management. This framework has the potential to transform the field of predictive analytics in finance. It shows a solution that balances accuracy and uncertainty quantification. The results clearly show the effectiveness of this method, paving the way for its potential application in a wide range of financial prediction scenarios.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

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.

Corporate Sustainability has garnered increasing attention within the business community, as corporates communicate to influence its stakeholders for building sustainable relationships. There has been a surge in research exploring its connection to firm performance but, existing studies lack a cohesive and concentrated approach. This study employs scientometric and bibliometric methods to analyse scientific production which signals a scarcity of research from developing countries, indicating a need for greater investigation. Focussing on specific subfields of Corporate Sustainability and Firm Performance, we analyze and narrow down the scope of our study to a focussed sample. Employing use of structured methods and PRISMA Protocol, this scientifically rigorous study, points towards identification of research themes linking sustainability practices to firm performance for achieving business excellence. It goes beyond mapping of literature, thereby establishing linkages to Sustainable Development Goals (SDGs). Our analysis reveals clusters centered on Environmental Performance, Firm Performance, Corporate Sustainability Reporting, Corporate Social Performance and Green Supply Chain Management with significant citations related to various SDGs. We make use of time series analysis to determine the future trends of research in the field. This scientometric and bibliometric analysis study contributes to a more focused understanding of the linkages between corporate sustainability and firm performance, emphasizing the need for cohesive research approaches to address the complexities of sustainable business practices and their implications for financial performance and the economy.

The use of e-scooters and e-bicycles has been on the rise with an increased medical and financial burden. Our purpose was to identify risk factors for increased medical charges and recommend appropriate steps. We studied a retrospective cohort presenting to a level 1 urban trauma center from January 2018 through July 2019 due to micro-mobility platforms. The primary outcome for comparison was total billing cost and a regression analysis was performed to identify variables associated with higher cost. A total of 4073 patients were identified. The majority of which were males (67%), aged 20-30 years (41%). Helmet use reported in only 39.8%. Radiologic studies were performed more often in the e-bicycle group. Total billing charges amounted to 28.3 million New Israeli Shekels ($7.9 million). Average total cost was higher for e-bicycle injuries and in non-helmet users. Other factors associated with higher cost were age, and time of day 12:00-18:00. We recommend that urban cities invest in infrastructure in advance to micro-mobility introduction, regulate the use of e-platforms, designate resources for education on personal protective equipment and prepare medical centers in advance.

Financial fraud poses a severe threat to the integrity of corporate financial statements and market efficiency. While auditors play a crucial role in detecting and preventing such fraud, the unique characteristics of this domain, including class imbalance, high misclassification costs, and am-biguous attributes, pose significant challenges. Traditional classification algorithms may not be optimally suited for this task. This paper proposes a novel stacking ensemble learning approach to enhanced detecting of Financial Statement Scam. By combining the strengths of multiple base learners, including Random Forest, XGBoost, and Gradient Boosting Decision Trees, our model leverages their diverse predictions to improve overall performance. A Logistic Regression me-ta-learner is employed to integrate the outputs of the base learners, capturing their collective wisdom while mitigating individual weaknesses. Extensive experimentation using a real-world dataset shown the superiority of our approach, achieving remarkable recall, precision, accuracy, and area under the curve metrics. The proposed model outperforms individual base learners, under-scoring the efficacy of ensemble learning in tackling the intricate problem of financial fraud detection. This research contributes to the creation of substantial and robust fraud detection systems, fostering trust and transparency in financial markets.

External audit reliance issues on the internal audit function (IAF) are increasingly important in financial auditing, yet its application in audit engagements lags behind other applications. Despite the belief that audit reliance will shape auditing in the future, limited and intervalley publications explore this transformative potential. This paper investigates the increasing importance of external audit reliance issues in financial auditing. It investigates the use of the IAF in external audit reliance decisions and its potential to enhance audit quality, process transparency, and stakeholders' credibility. Through a systematic literature review of 68 scientific articles from 1984 to 2022, factors influencing external auditors' dependence on the IAF are analyzed, including IAF work quality, assistance type, organizational factors, customer pressure, and business risk. The review highlights the IAF's crucial role in improving financial reporting integrity and external audit effectiveness. However, the challenges that allowed external auditors to decide whether to rely on the works of IAF like limited disclosures and accessible data hinder assessing IAF reliance effects, leading to information asymmetry that may impact stakeholder confidence. The paper discusses implications, limitations, and future research directions to explore technology adoption and optimize audit practices, promoting transparency in the audit process.

Lump sum contracts are specific contracts used in the construction industry to give owners financial certainty and decrease risk while incentivizing contractors to finish projects on schedule and within budget. This article discusses the usage of lump sum contracts in construction projects, covering the types of projects appropriate for lump sum contracts, critical considerations for entering a lump sum contract, and the tender and preliminary expenses involved. The article also covers lump sum contracts' benefits and drawbacks and how owners and contractors allocate risk. Finally, the difficulties of implementing lump sum contracts, such as potential disagreements over scope changes and quality issues, are investigated. In general, lump sum contracts can be a valuable tool for managing construction projects, but their execution necessitates careful planning and collaboration to ensure successful completion.

The purpose of the article is to measure, using the VAIC and MVAIC models, the impact of intellectual capital and its elements on the financial performance of Omani industrial companies listed on the Muscat Stock Exchange (MSE) from 2017 to 2021. Regression analysis using the Gretl software was applied to the study of panel data on the activities of 34 industrial companies in Oman, obtained from the official MSE website and from published financial statements. The analysis of literature sources was carried out using bibliometric analysis, the results of which were visualized using the VOSviewer software. To analyze the relationships between financial performance and its determinants (intellectual capital variables and control variables), four types of PERF models were developed, based on which sixteen models were directly examined. Four dependent variables (Earnings before interest, Taxes, Depreciation and Amortization, Return on Assets, Return on Equity, Assets Turnover Ratio) and eight independent variables (Value Added Intellectual Coefficient, Modified Value Added Intellectual Coefficient, Capital Employed Efficiency, Human Capital Efficiency, Structural Capital Efficiency, Relational Capital Efficiency, Size, and Leverage) were used to form these models. The Empirical Results section has discussed descriptive statistics and correlations, selection of estimate panel data parameter based on the F-statistics, Breusch-Pagan test, and Hausman test, assumption test results (Normality, Autocorrelation, Heteroscedasticity), and panel regression models. Analysis of the impact of VAIC, MVAIC and its elements on the financial performance of Omani industrial companies has yielded mixed results. VAIC and MVAIC in general have a significant positive effect only on ATO. For individual elements of VAIC and MVAIC, the following most significant associations with financial performance measures were found: CEE negatively affects ROE; SCE has a positive effect on l_EBITDA and ATO; RCE negatively affects ATO; l_SIZE has a positive effect on l_EBITDA and ROA; and LEV has a positive effect on ROA.

Earnings management and financial distress are two critical facets of financial management in corporations that have lately been the focus of thorough research from both scholars and practitioners. The purpose of the research is to evaluate the influence of financial distress on the earnings management of companies listed at the NSE during the pre- and post-COVID eras. Multiple linear regression was applied in this study to examine the causal association between earnings management and financial distress. To assess the effect of financial distress on earnings management, the study analyzed data from 37 listed companies during the pre-COVID and post-COVID periods. The methodology also takes into account control factors like firm size, GDP, and inflation. The pre-COVID research indicates a significant positive correlation between financial strain and earnings management. In contrast, the correlation between financial distress and earnings management is not statistically significant in the post-COVID analysis. However, research shows that firm size has a statistically significant negative influence on managing earnings. The results of this study show that smaller firms are more inclined to engage in earnings management during the post-COVID era than bigger ones.

Weighted Student Funding (WSF) systems have been implemented in various countries to give schools more autonomy over how to allocate their funding. School principals use funding to maintain school operations and foster innovation for achieving educational goals. However, despite the importance of this process, scholarly research has largely overlooked how principals make decisions about their financial resources. Accordingly, this study seeks to provide practical insights into the strategies used by one school by highlighting their staff’s perceptions about using their WSF to maintain school operations and spur innovation. Using a case study approach, we investigated a principal who effectively used a school’s WSF to transform a failing school into an innovative one. The findings revealed that the principal strategically implemented financial management mechanisms in a way that inspired teachers to deeply consider how a school's WSF can help achieve educational goals. The principal fostered consensus on the school's direction, encouraged innovation through hands-on experiential learning and strategic planning, and facilitated funding for innovative teachers by guiding proposal development. Insights into the shifting cultural and practical landscape of financial resource utilization within schools are discussed.

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

Advanced Hepatocellular carcinoma (HCC) is no longer a terminal illness. This change was mainly attributed to the development of new treatments including tyrosine-kinase inhibitors (TKIs), vascular endothelial growth factor (VEGF) inhibitors and immune checkpoint inhibitors (ICPIs) but the financial toxicity of treating advanced HCC is of a major concern specially in low middle-income countries (LMICs) where the patients are still battling for their most basic rights. Most of advanced HCC patients in LMICs have very limited accessibility to the new treatments including ICPIs. Searching for out of the box solutions to improve access to treatments -mainly ICPIs- is an utmost necessity for LMICs advanced HCC patients.

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

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

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

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

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

This study investigates the relationship between shipping connectivity, environmental quality, financial development, and economic growth among 14 countries in the Regional Comprehensive Economic Partnership (RCEP) from 2006 to 2019. Using panel-corrected standard error, Dynamic Seemingly Unrelated Regression, and Driscoll-Kraay estimation methods, the analysis reveals that shipping connectivity significantly contributes to financial development and economic growth, while also exerting a negative impact on environmental quality. These findings underscore the dual role of shipping connectivity in fostering economic prosperity and increasing environmental challenges, highlighting the trade-offs between growth and sustainability. The study also emphasizes that as trade between RCEP countries grows, driven in part by the removal of customs tariffs, shipping activities—and consequently environmental pollution—will likely increase. To achieve sustainable development in the maritime sector, policymakers must devise strategies that maximize economic benefits while mitigating environmental harm. These strategies include promoting environmentally friendly green ports, adopting low-carbon and advanced technologies, and strengthening policies to control emissions and marine pollution. Collaboration among shipping companies, local governments, and environmental organizations is also essential.

This study assessed the financial management practices and challenges of food cart businesses in Cabanatuan City, Nueva Ecija. The study used a descriptive research design and structured surveys and questionnaires to gather quantitative data from 20 food cart businesses. The data were analyzed using statistical tools such as weighted means, frequencies, and percentages to provide a comprehensive overview of these small enterprises' financial practices and challenges. Key findings highlight that while many businesses demonstrate basic financial literacy, significant gaps exist in financial record-keeping, budgeting, and revenue management. The study emphasizes the importance of improving these practices to ensure the sustainability and growth of food cart businesses. The research contributes to the broader discourse on small business management and economic development by identifying common challenges and offering targeted recommendations. The findings underscore the need for targeted interventions, such as financial training and policy support, to enhance the financial acumen of food cart owners. This study adds to the existing body of knowledge as it provides practical insights that can benefit business owners, policymakers, and community stakeholders in fostering a more resilient and economically vibrant local economy.

The main purpose of this paper is to review systematically both theoretical and empirical literature about the relationship between corporate governance variables and the performance of microfinance institutions. The theoretical reviews were: agency theory stakeholders’ theory, resource dependency theory, stewardship theory, and social contract theory which deal with corporate governance mechanism and their effect on the performance of the firm. Empirical studies carried out in different countries at different times together with different findings were reviewed to compare the effect of corporate governance mechanisms on the performance of microfinance institutions and to establish areas of gaps for further studies. Board characteristics which include size, education, meeting frequency, board composition, and board independence, and their effect on the financial performance of microfinance institutions were discussed. The findings of different scholars show a lack of consistency on the effect of corporate governance variables on the financial performance of Microfinance Institutions so further investigation is required from potential scholars. Finally, theoretical, measurement and methodological drawbacks of previous studies were identified, and significant recommendations were forwarded for future studies in this area.

The modern world is gradually moving towards a global civilisation that is automated and propelled by technology. Artificial intelligence (AI) is a highly advanced technology that has been acknowledged as a major innovation. The incorporation of this technology into the banking sector has enhanced operational procedures in relation to customer satisfaction, virtual assistance, and risk reduction. This machine is employed to streamline activities and improve how decisions are made in this sector. This study investigates the potential benefits and challenges of implementing artificial intelligence in the banking sector. The methodology ensures a comprehensive understanding of the implementation of artificial intelligence in banking by showcasing its effectiveness, precision, and efficiency, as well as the opportunities it presents. The study data were collected from secondary sources, such as recent publications that demonstrate applications of artificial intelligence from five different banks, as well as the examination of industry reports. The main findings of this study demonstrate the substantial improvement of administrative operations through AI-driven technology, which effectively automates activities that include inputting data and handling information. As a result, the frequency of mistakes caused by people has reduced, leading to a 30% decrease in operational expenditures for all evaluated institutions. However, the banking industry admits the challenges it encounters, such as issues with confidentiality and a scarcity of skilled employees. In the financial services industry, the integration of artificial intelligence into the banking sector is perceived as an advantageous chance instead of a harmful danger, with a greater potential for beneficial impact.

The study used the data from emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalized method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterization, and optimizing formativeness and heterogeneous individualspecific factors. The empirical analysis utilized various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution) promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 per-cent of the income distribution). The impact loan-to-value ratios were found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to ad-dress income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. Monetary Supply Stabilization Fund could also be established to maintain financial stability and prevent excessive inflation.

This study examines the relationship between Gross Domestic Product (GDP) and income inequality in Greece across three key periods: the pre-crisis period (2003-2008), the crisis and memoranda period (2009-2014), and the post-crisis period (2015-2020). Using data from the World Bank Open Data database, the study employs correlation analysis, and linear regression models to analyze how economic changes influence income distribution. The findings reveal that during the pre-crisis period, economic growth was moderately correlated with increasing inequality, although this relationship was not statistically significant, indicating uneven distribution of growth benefits. In the crisis period, a strong and statistically significant negative relationship between GDP and the Gini coefficient was identified, highlighting the sharp rise in inequality as the economy contracted due to austerity measures. In the post-crisis period, the relationship between GDP and inequality weakened, showing a statistically insignificant correlation, suggesting that economic recovery alone did not significantly reduce inequality. The study concludes that economic growth is insufficient to address income inequality without targeted policy interventions, particularly during periods of crisis. These findings underscore the importance of comprehensive social policies to mitigate inequality and suggest areas for future research on the impact of specific interventions on income distribution.

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.

This paper presents a comprehensive analysis of the financial factors and risk management strategies essential for optimizing disaster resilience within the Serbian defence industry's critical infrastructure. The significance of this sector is multi-faceted, impacting national security, economic stability, and technological advancement. Primarily, the Serbian defence industry ensures the preservation of vital defence interests, maintaining Serbia's independence from foreign sources for weapons and military equipment in both peacetime and wartime. Economically, it is a significant employer of the working-age population, directly affecting local employment rates, fostering economic development, and ensuring the sustainable growth of this crucial sector. This, in turn, stimulates broader economic activity and enhances social cohesion while strengthening the national balance of payments through increased export potential. From a technological perspective, the defence industry drives scientific, technological, and industrial development, reinforcing Serbia's global political and military standing within the Western Balkans and on the international stage. Consequently, the paper aims to examine the risk management and protection of the Serbian defence industry's critical infrastructure, offering concrete and actionable measures to improve and develop these systems with a particular emphasis on security. The research's utility and contribution lie in identifying similarities and differences in the operational performance of defence industry companies, a vital segment of the national economy. The presentation of these findings focuses on the protection of critical infrastructure. The results will form the basis for further investigation into the underlying causes of business performance and the effective management of critical infrastructure security.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, assure accuracy and reliability for auditing, and adhere to tax compliance. The design involves data selection from Web of Science Core Collection (WoS) and Scopus, published between 2007 - 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers' interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data was retrieved, 9-11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

This study investigates the complex role of financial risks in the context of public–private partnerships (PPPs) in India and their relationship to the amount and extent of investments made by private partners (s). We hypothesize that subsidies, bid criteria, and risk have a positive relationship with investments made by private partners. A project’s leverage moderates these relationships. Regression was used to analyze the hypotheses using SPSS 24 and PROCESS Macro. Data from the World Bank were collected from 2009 to 2019 for the study variables. Interestingly, our research finds support for these relationships that may seem contrary to some commonly-held notions of risk relationships, such as – the government using ‘viability gap’ funding to attract private investment and ‘leverage’ does not moderate the relationship between risk assumed and private investment. This study is among the first to recognize and elaborate on financial risk relationships, specifically in the context of Indian PPPs. These findings are significant for both private and public participants in terms of financial considerations in PPP projects, especially in emerging markets.

The oil and gas industry in Nigeria, a vital contributor to the nation's economy, has long been associated with significant environmental challenges. Pollution, habitat destruction, and greenhouse gas emissions have raised global concerns about sustainability and corporate responsibility. This empirical study explores the relationship between environmental accounting practices and the financial performance of listed oil and gas companies in Nigeria. We investigated eight oil and gas companies that are publicly traded on the Nigerian Stock Exchange Market (NGX) as of January 17, 2022, from 2011 to 2022. The study employs Driscoll-Kraay standard errors and reveals that environmental accounting had significant effects on returns on assets, earnings per share, and liquidity ratio. The study revealed that the implementation of environmental accounting practices had diverse effects on the performance of oil and gas companies in Nigeria. The findings encourage policymakers and stakeholders in the sector to utilise the insights and design more effective regulations and incentives that promote environmental corporate responsibility. Also, valuable insights into the potential benefits and challenges associated with adopting environmental accounting practices, and influencing the decision-making processes of corporate stakeholders were provided to ensure sustainability in terms of improved financial performance of the oil and gas sector in Nigeria.

This study analyzes the convergence between artificial intelligence (AI) and management accounting in hospital contexts. Management accounting plays a central role in the effectiveness of planning, control and decision-making in healthcare institutions such as hospitals. The application of AI in this scenario offers powerful tools to improve accounting processes, optimize operational efficiency and maximize the use of available resources, resulting in an improvement in the quality of care provided to patients. Through in-depth analysis, the article explores how AI is bringing about a revolution in hospital management accounting, presenting case studies illustrating its successful implementation, outlining the tangible benefits.

This study examines the effects of working capital management strategies on firm’s financial performance of Indian cement companies using 11-year (2010-2020) financial data of 31 companies listed in Bombay Stock Exchange. It is intended to find out whether the strategies made by finance managers with respect to the components of working capital such as average receivable period, inventory conversion period, average payment period and cash conversion cycle, etc. affects the firm’s performances individually and in total. The results of Pearson’s correlation coefficient and random effect regression model shows that there exists a negative relationship between financial performance measured in terms of return on assets (ROA) and inventory turnover period (ITP) as well as accounts payable period (APP), whereas firm’s performance is not significantly affected by accounts receivable period (ARP) and cash conversion cycle (CCC). Similarly, it has been observed that the liquidity measures such as current ratio (CR) and quick ratio (QR) have a significant positive association with ROA. Moreover, the size of the firms and leverage are inversely related to ROA but the age of the firms is not significantly affecting their financial performances. Hence, it can be concluded that how an organisation designs its working capital strategy do have an impact on its performances.

This study empirically analyzes how R&D expenditures affect firm value using a regression model for listed firms in Shandong Province, China. The dependent variable is measured by To-bin Q, which measures firm value, and the experimental variable among the independent varia-bles is R&D expenditure. In addition, financial leverage and firm size, which have been found to have a significant impact on firm value in previous studies, were selected as control variables. The study sample consisted of 554 data points analyzed over a nine-year period from 2010 to 2018. Without controlling for variables that affect firm value, the correlation analysis shows that R&D expenditures of listed firms in Shandong Province, China have a significant positive impact on firm value at the 1% level. For a more precise analysis, we ran a multivariate regression analysis controlling for other variables that affect firm value and found that R&D expenditure has a significant positive impact on firm value at the 1% level. These findings suggest that man-agers of listed companies in Shandong Province, China, need to actively engage in R&D activi-ties to increase firm value. Companies in Shandong Province, China, are currently experiencing very strong growth, and this study, which shows that R&D is the key to increasing the value of these companies, can help companies in Shandong formulate their business strategies.

This study analyzes the convergence between artificial intelligence (AI) and management accounting in hospital contexts. Management accounting plays a central role in the effectiveness of planning, control and decision-making in healthcare institutions such as hospitals. The application of AI in this scenario offers powerful tools to improve accounting processes, optimize operational efficiency and maximize the use of available resources, resulting in an improvement in the quality of care provided to patients. Through in-depth analysis, the article explores how AI is bringing about a revolution in hospital management accounting, presenting case studies illustrating its successful implementation, outlining the tangible benefits.

Achieving universal access to water and sanitation services in developing countries requires a coherent legal, political, and institutional environment, along with a clear financial strategy. Regionalizing utilities may be crucial for addressing disparities, economic inequalities, and governance challenges. Regionalization offers economies of scale, resource efficiency, knowledge sharing, and collaboration. The cornerstone will be ensuring the financial-economic viability of regional utilities, considering their social impacts in terms of affordability. The State of Santa Catarina case study is assessed. In the case of Santa Catarina, only four regional utilities currently generate sufficient revenue, without factoring in necessary investments, indicating the need to review design parameters to improve operational efficiency and increase revenues. Additional financial support may be required to ensure universal access. Tariff adjustments must strike a balance between cost-effectiveness and affordability for families.

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

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

This study aims to test empirical research on the effect of financial resource ability, research and development (R & D) on innovation orientation and competitive position. This study also examines the critical mediating role of innovation orientation and competitive position to achieving new products performance (NPP). This study used a quantitative research approach by comparing data from service industry and manufacture industry in Indonesia included in Indonesian-State-Ownership companies. The analysis unit in this study used middle managers and top managers who responsible for managing divisions within the Indonesian-State-Ownership companies. The number of respondents studied in this study was 287 sample. The purposive sampling technique was used in taking the research sample. This study indicated that financial resources abilities, research and development (R & D) abilities positive effect on innovation orientation and competitive position. This study also testing the importance role of innovation orientation and a competitive position to enhancing new products performance (NPP).

In the domain of financial trading, where accuracy and swift response to data discrep- ancies are paramount for maintaining clienttrust, our research paper delves into the development of a real-time alert system utilizing SpringBoot, a framework renowned for its robustness and adaptability in enterprise applications. We embark on a comprehensive journey, beginning with addressing thefoundational challenges inherent in monitoring trade inquiries - the necessity for real-time processing, unwavering data accuracy,and ensuring unwavering system reliability. The study then methodically transitions to the architectural design, where we explore howSpringBoot’s capabilities are optimally utilized to meet these challenges. Following this, we delve into the im- -plementationphase, detailing the strategic selection of compatible technologies and methods for effective data management and discrepancydetection. The paper culminates in a thorough evalu- ation of the system, where we assess performance across various operationalscenarios, scrutinize the accuracy of the discrepancy detection mechanisms, and integrate real-world feedback from users in thetrading sector. This holistic approach not only highlights the versatility and efficiency of SpringBoot in creating reliable alert systems in the high-stakes world of financial trading but also serves as a detailed guide for similar endeavors in this field

This research investigates the economic resilience of small and medium enterprises (SMEs) during financial crises, focusing on the factors that enable these businesses to survive and recover from economic downturns. Employing a qualitative methodology, the study examines various aspects of SME operations, including financial management, innovation, leadership, government support, geographical location, social capital, and contingency planning. The findings reveal that effective financial management—encompassing cash flow management, debt restructuring, and diversified funding sources—is crucial for maintaining stability during financial crises. Innovation and adaptability also play significant roles, with SMEs that embraced new technologies and market strategies demonstrating greater resilience. Leadership emerged as a critical factor, with leaders who communicated effectively and managed stress contributing to their organization's ability to navigate crises. Government support provided essential relief, although its impact varied depending on accessibility and timeliness. Geographical location influenced resilience, with urban SMEs having better access to resources, while rural SMEs benefited from strong community networks. Social capital, including relationships with suppliers and customers, was vital for providing stability and support. Contingency planning and risk management were identified as essential for preparing for and responding to disruptions. The research highlights that resilience is a multifaceted attribute resulting from the interplay of financial prudence, adaptability, effective leadership, supportive policies, geographic factors, and strong social networks. These insights offer valuable lessons for SME owners, policymakers, and support organizations, emphasizing the need for a comprehensive approach to building and sustaining resilience in the face of economic challenges.

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.

In recent years, ESG (environmental, social, governance) has emerged as a critical investment concept. Its goal is to create value for both shareholders and society, encouraging companies to optimize social value. However, the exploration and research into "the proportion of firms ex-porting and the pathways through which the environmental, social, and governance activities of carbon-intensive firms influence firms' financial performance" remains largely unexplored. This study establishes a research framework within this context, utilizing listed Chinese manufactur-ing companies as the research subjects. Employing a fixed-time, fixed-industry, two-way fixed-effects model methodology, it delves into the relationship between ESG ratings, the pro-portion of firms' exports, and firms' financial performance through panel regression modeling. This study emphasizes the moderating role of carbon-intensive firms in the relationship. Find-ings indicate that both enterprise export proportions and carbon-intensive firms significantly affect the correlation between corporate ESG activities and financial performance. This has im-plications for investors, company management, policymakers, and regulators. Based on these findings, we propose policy recommendations at corporate and governmental levels to enhance ESG significance, strengthen corporate governance, and promote continuous ESG advancement. The study provides micro-level evidence of the interaction among ESG ratings, export ratios, carbon-intensive enterprises, and corporate performance, empowering investors to make in-formed decisions.

Eco-agricultural technology is crucial to alleviating agricultural resource scarcity and environ-mental pressures. However, financial constraint affects its successful promotion. Digital finance has a significant impact on farmers, but existing research lacks an exploration of the impact of digital finance on farmers' adoption of eco-agricultural technology. This study focuses on Rice–Crayfish Co-cultivation Technology as an example. It utilizes survey data from 1,063 households in China. An Endogenous Switching Probit model is employed to solve self-selection bias. The results reveal several key findings. First, the average treatment effect on the treated is 51.5%. This indicates that if farmers who use digital finance were to stop using it, the probability of adopting rice–crayfish co-cultivation technology would decrease by 51.5%. Therefore, digital finance is beneficial for farmers in adopting this technology. Second, heterogeneity analysis reveals that digital finance has a greater promoting effect on older farmers, those with lower education levels, and higher proportions of agricultural income. This suggests a greater reliance on digital financial services among vulnerable groups. Third, digital finance promotes farmers' adoption of rice–crayfish co-cultivation technology by alleviating financial constraints, expanding information channels, and increasing social capital accumulation. Overall, the findings offer valuable insights for for-mulating supportive policies.

The paper discusses the experience of implementing an efficient economic management system at the University of Vigo in Spain. This system, which focuses on managing secondments, aims to minimize bureaucracy and standardize economic procedures for various staff groups. By adopting a qualitative methodology and utilizing content analysis, the study compares the University of Vigo's fully digital administrative procedure with other universities in the CRUE network. The results show significant time and cost savings due to this implementation. The improved traceability and information for users support the administrative change, and the positive environmental impacts highlight the commitment to a more sustainable approach. The paper's originality lies in demonstrating how this approach enhances efficiency, reduces costs, and improves sustainability within a Spanish public administration setting.

The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test

The objective of this study is to determine the impact of COVID-19 on the performance of Pakistani Stock Market. This study uses the data of COVID-19 related positive cases, fatalities, recovers and the closing prices of PSX 100 index of the first half of 2020. The findings of the study suggest that only COVID-19 recoveries are influencing the performance of the index and the daily positive cases and fatalities are insignificantly related to the performance. Further studies can be performed by incorporating other variables such as economic growth, interest rate and inflation rate along with the COVID-19 related variables at a cross-country level.

The World economy after global crisis of 2008–2009 entered a restructuring era. It defines relevance of researches of its directions and patterns. The principles of determinism and systemic analysis are methodological basis of article. In research were used the methods of processing Big Date, as of continuous changes and the symmetric analysis of macroeconomic indicators according to databases of the IMF, WB, BIS, the Central banks and treasuries. As a result of the research it is proved that depth and globalism of recessionary processes are caused by a combination of crisis of world financial system and civilization problems. Intrinsic signs of risks in the modern economy caused by the duality of the nature of global crises have been identified. There are analyzes: the deficit of resources of the international financial institutions, a negative role of a fixed rate of Yuan, a big share of derivatives and off-balance obligations of banks, use of SPV in structures of deals, growth of debt obligations, trade wars, slowdown in the growth of the Chinese economy, aggravation of contradictions between global and national finances. The thesis is reasonable that deglobalization and dedollarization deepened this conflict, started the rollback mechanism from achievements of globalization, led to tariff wars. By means of a systematization it is proved the key directions of the restructuring of global economy: legal basis, leadership and reserve currency. On the basis of SWOT-analysis of the USA and China it was concluded that the question of leadership of one country should be excluded, but slow transition to use SDR (with a basket from 15–20 currencies) as a reserve.

Disaster Risk Reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development and Earth Observation (EO) can provide useful, timely and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: ‘Earth Observation for Sustainable Development (EO4SD)’. One activity area under EO4SD is devoted to Disaster Risk Reduction: EO4SD DRR. Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision making. To understand and fully account for the essential user requirements (IFI & Client States), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

In response to the growing environmental concerns and sustainability imperatives in the (GCC) and the world in general, companies are increasingly being urged to integrate environmental accountability into their core strategies and operations by accounting for environmental performance in their financial statements and the impact on supply chains and sustainable development. The problem of the study is to identify the impact of environmental accounting disclosure challenges on supply chains and sustainable development in companies. By taking the experiments of 20 companies and representatives of the study community interested in the topic, and using the heuristic research approach through the questionnaire tool and a set of key variables, including the adoption of disclosure on environmental accounting performance (DEAP), supply chain sustainability performance(SP), Sustainable Development Index(SD), and using the (PLS) program, in the statistical analysis, the study reached the most important results: The study revealed a positive relationship between the disclosure of environmental accounting performance, improved supply chains and sustainable development in the companies represented by the study sample in the (GCC), where both financial performance and environmental performance contribute to achieving the Sustainable Development Goals and supply chains. Accordingly, the study recommends many recommendations, the most important of which are: the need for companies to be encouraged to disclose the performance of environmental accounting as a strategic necessity, promote more sustainable supply chain management, improve financial performance, and effectively contribute to achieving the Sustainable Development Goals, thereby enhancing sustainability within companies and across supply chains, the study also recommends the need to continue studies in this aspect, especially in the (GCC) and East Asian countries, to urge companies to disclose the performance of environmental accounting in their financial statements.

Background: Pet ownership is a common wellbeing enhancing practice globally, offering com-panionship and emotional support. However, it also entails financial responsibilities that can pose challenges and have negative wellbeing impacts for both human and animal. Aims/Purpose: This study aimed to explore pet owners' the financial aspects of pet ownership in New Zealand, including the importance of pet insurance, the impact of veterinary costs, and the ethical considerations of owning a pet. Understanding these aspects of pet ownership is crucial for promoting responsible pet ownership and ensuring animal welfare. Study Designs: Data was collected through a nationwide online survey conducted in 2019 as part of the Furry Whānau Wellbeing research project. The survey included questions about the financial aspects of pet ownership, and respondents were asked to indicate their level of agreement with various statements. Findings: The study found that the cost of veterinary care is a significant concern for many pet owners, with 83.9% of respondents agreeing that expense is a factor in the level of healthcare their pets receive. While pet insurance can mitigate financial burdens, only a third of respondents considered it essential due to high costs and exclusions. The study also revealed disparities for rural and low-income households. Conclusions: The financial aspects of pet ownership present a complex challenge, impacting both pet and owner wellbeing. This research highlights the need for comprehensive strategies to promote sustainable and equitable pet ownership, including improving access to affordable vet-erinary care, enhancing financial literacy among pet owners, and providing support systems for those facing economic hardship.

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.

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.

The review aims to examine how well numerical indicators of business bankruptcy can predict outcomes. The paper examines ten critical studies that concentrate on statistical models for bankruptcy predictions utilizing PRISMA criteria. The findings highlight the usefulness of numerical indicators in indicating financial hardship, particularly cash flow ratios. In order to connect theoretical knowledge with real-world corporate strategy applications, the study ends by reaffirming the importance of these indicators in strategic decision-making.

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

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

The Directive 2014/95, in force in 2017, is the first European step that requires mandatory non-financial information to undertakings (all “public interest entities” with more than 500 employees). The regulation is concerning sustainability information as environmental, social and employee, human rights and anti-corruption and bribery matters and disclosure of diversity policy for board members. The study, in the strand of the regulation of accounting, part of the broader field of research into accounting regulation, contributes to the debate on the quality of regulation, in this specific case referred to sustainability disclosure. The regulation of sustainability matters is studied in literature broadly in a post-implementation phase and at national level. This research, instead, aims to analyse, through the causal chain of regulatory policy, in the ex-ante stage, the quality of the regulation and, at least, the usefulness of the normative pressure. The Oil & Gas sector is chosen as sample of the study, because it is one of the most advanced sectors in sustainability disclosure. The examination of the law, in terms of content requirements (what) and location of information (where), is the basis to apply the disclosure-scoring system, a partial form of content analysis, to the reports of the sample. The findings reveal a good level of completeness of non-financial information, however, there are some areas that have to be improved to reach the requests of the Directive. Results show also the presence of overlap between financial reports and sustainability ones. In conclusion, the regulation is useful to prompt undertakings to reflect on their reporting and so doing improve their sustainability approach.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

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 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.

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.

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.

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.

This study examines the causal relationships between interest rates in the Korean financial market using a vector error correction model (VECM) and a Bayesian network. The Bayesian network, a novel approach in this context, identifies two distinct transmission channels: one led by call rates (reflecting monetary policy) and another by 10-year Treasury bond yields (reflecting fiscal policy). While the call rate channel aligns with traditional views, affecting bank lending rates, corporate bond spreads, and commercial paper rates, the 10-year Treasury yield channel highlights a separate fiscal policy transmission mechanism, influencing commercial paper rates indirectly through 30-year Treasury yields and directly impacting merchant bank lending rates. This finding suggests that fiscal intervention could potentially interfere with monetary policy, emphasizing the need for coordinated macroeconomic policy measures. The study also emphasizes the role of 10-year Treasury bonds as a benchmark in the Korean bond market, reflecting medium- to long-term economic outlooks and serving as the underlying asset for various derivatives.

We introduce a novel stochastically correlated two-factor (i.e., bivariate) diffusion process under the square-root format, for which we obtained, analytically, the corresponding solutions for the conditional moment generating functions and conditional characteristic functions. Such solutions recover verbatim those of the uncorrelated case. We believe that the The aforementioned model encompasses a range of processes similar to that produced by a bivariate square-root process in which entries are correlated in the standard way, i.e., via a constant correlation coefficient. Note that, for the latter, closed-form solutions for the conditional characteristic functions are not available. We focus on the financial scenario of obtaining closed-form expressions for the exact price of a zero-coupon bond and Asian option prices via a Fourier cosine series method.

Energy transition is one of the greatest challenges facing humanity today. All micro, small, medium and large enterprises in operation will have to face it, and some can al-ready boast the first successes in this regard. This paper focuses on the involvement of these smallest players in energy transition. Micro and small entities are the largest part of the economy, having a great influence on its development. Looking at the wide scale of their activities, these entities are distinguished by a specific owner-manager relationship. This means that the final decision-making is influenced by business goals, but also by the manager's approach, their awareness of changes, and personal and other factors. Based on a structured and standardized survey questionnaire, the research was carried out using the Computer-Assisted Telephone Interviewing technique between April and May 2022 among owners of micro and small enterprises operating in Poland. The survey showed that active engagement of micro and small enterprises in energy efficiency initiatives is related to their market success and financial health.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

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

City marathons have evolved and grown exponentially in type and popularity, managerial complexity, for their financial impact on their host cities and for the attraction of corporate sponsors. Much research has focused on evaluating the broad economic, urban, tourist, social, sporting, and symbolic effects of city marathons on host cities. Research have not examined the importance of the contribution of sponsors to the financial stability and its implications to the overall management and further success of marathons. This article focuses on the cases of the Bank of America Chicago Marathon and the Marathon Valencia Trinidad Alfonso and examines how effective has been the contribution of their sponsors to the financial stability and its implications for the management and success of both races over time. Results show that the international success of both events –in terms of sporting participation, performance and economic impact– is closely related to the design and management of the event; the synergies between the political, business and sporting spheres that the organizational leadership of the event has made it possible to implement and, as a consequence, the support received from sponsors, which has not only provided both races with financial stability, but also has contributed to improve the management of the race.

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.

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 study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

This study investigates the moderating effect of corporate governance on the associations of internal audit and quality of internal audit with quality of financial reporting among commercial banks in the Republic of Yemen. The final sample includes 210 internal auditors, heads of internal auditors, chairpersons, and members of audit committees. Using a survey-based methodology, the results of the Smart-PL4 analysis showed a positive association between internal audit and quality of internal audit and quality of financial reporting. Interestingly, the results showed an insignificant association between internal audit, quality of internal audit, and quality of financial reporting when considering the moderating effect of corporate governance. The findings of this study contributes valuable insights to the existing literature on the influence of internal audit, quality of internal audit, and corporate governance on quality of financial reporting in developing countries, specifically in Yemen.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

This paper reports a new global Mittag-Leffler synchronization criterion with regard to fractional-order hyper-chaotic financial systems by designing the suitable impulsive control and the state feedback controller. The significance of this impulsive synchronization lies in the fact that the backward economic system can synchronize asymptotically with the advanced economic system under the effective impulse macroeconomic management means. Matlab LMI-toolbox is utilized to deduce the feasible solution in numerical example, which shows the effectiveness of the proposed methods. It is worth mentioning that the LMI-based criterion usually requires the activation function of the system to be Lipschitz, but the activation function in this paper is fixed and truly nonlinear, which cannot be assumed to be Lipschitz continuous. This is another mathematical difficulty overcome in this paper.

The purpose of this project is to explore the potential of Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), as a tool for making directional predictions for assets. The focus will be on using LSTM for classifying up/down moves in stock indices, stocks, and FX rates (EUR/USD). Various financial ratios, advanced technical indicators, and volatility estimators will be used as features to enhance the predictive power of the model. Incorporating credit spreads and news indicators can also improve the model. The advantage of LSTM is its ability to handle sequences of arbitrary length, which means that longer time frames such as 5-day, 10-day, or 1-week returns can be used for prediction. Before using RNNs, exploratory data analysis (EDA) will be performed to understand the underlying patterns in the data. This may include dimensionality reduction techniques such as autoencoding, self-organizing maps, or decision tree regression. The results of the EDA will be used to guide the selection of features for the LSTM model. It is important to note that long memory and stationarity are common characteristics of time series data, such as interest rates or economic indicators. Power-law autocorrection, Hurst exponent fractional, or Markov processes may be used to model these data. Predicting 5-day, 10-day returns for equities or 1-week, 1-month returns for financial factors presents two challenges. First, a large amount of data is required, typically 7–10 years of data. Second, positive autocorrelation in the 5-day or 10-day returns in equity time series must be isolated.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

The rapid growth of the elderly population is a major global demographic and social issue. Unfortunately, there is a shortage of pension plans and social se-curity programmes for this population in developing countries which has severe consequences for their quality of life and planetary health (PH) wellbeing. In this article, we aimed to better understand the pension systems in Ghana, Pakistan, and Myanmar, by reviewing the official government materials (such as pension reports) and published literature. We observed several policy implementation gaps and inequity in pension schemes for older people, specifically for informal and private sector workers. Considering the size of formal versus informal economies and the level of development index of each country, we suggest a wide variety of options in pension policies, financing, designing cash benefits and pension payments to cover all older citizens. This article addresses an unmet need of the elderly people, their wider environmental and economic sustainability to ensure social justice and resource utilization. The governments of developing countries should embrace and establish unique, inclusive, and friendly policies encompassing the informal sector to warrant older adults’ functional and planetary wellbeing with dignity and honor.

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.

We have estimated the level of Risk Weighted Assets among 30 countries in Europe, in 30 trimesters, using data of the European Banking Authority-EBA of 139 variables. We perform an econometric model using Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares. We found that Risk Weighted Assets is negatively associated, among others, to the level of NFC loans in mining and quarrying, in public administration and defence, and in financial and insurance activities and positively associated, among others to distribution of NFC loans in human health services and social work activities, in education and the level of net fee and commission income. Furthermore, we apply a cluster analysis with the k-Means algorithm, and we find the presence of two clusters. A comparison was then made between eight different machine learning algorithms for predicting the value of the RWAs and we found that the best predictor is the linear regression. The RWA value is predicted to increase by 1.5%.

In light of the worldwide spreading requirements related to the disclosure of non-financial information, which are aligning to the Sustainable Development Goals (SDGs) developed by the United Nations (UN) in 2015, the study aims to analyse the influence of sustainability and other non-financial reporting on companies’ engagement in earnings management practices, through a pre-post adoption of European Directive 2014/95/EU comparative analysis for firms listed on the Bucharest Stock Exchange (BSE) in the period 2015-2019. To conduct the investigation, the research involves the assessment and analysis of three earnings management metrics resulted by running multiple linear regression models on a sample of 31 companies listed on BSE. Research findings emphasise a decrease in the use of income smoothing practices by sampled companies in the post-adoption period 2017-2019, compared to the period preceding the implementation of the EU directive related to mandatory disclosure of non-financial information, 2015-2016. Thus, firms characterised by a higher transparency in terms of sustainability reporting are less inclined to engage in earnings management practices. This research complements the literature in the field of sustainability reporting and earnings management, providing empirical evidence on the significance and impact of publishing non-financial information.

The aim of this cross-country research is to examine how the profit and loss sharing mudarabah investment deposits are classified and disclosed in the financial statement of Islamic banks. The cross-country study examined the financial statements of fifty-one fully-fledged Islamic banks. The results of the data analysis show that Islamic banks disclose the mudarabah investment account in different ways. The absence of standardized disclosure for mudarabah investment deposit confuses the stakeholders. This research suggests to the regulators to fully or partially adopt the AAOIFI standards specifically for Islamic financial institutions.

It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and are also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.

In the construction business, which is known for its intricate and strategic nature, it is crucial to have efficient risk management in place. This is because various internal and external elements can cause disruptions to projects. Although the industry has had a historically negative reputation for risk analysis, the methodical process of identifying, analyzing, and managing risks has been more widely recognized and respected. This paper examines the historical progression of construction risk management, highlighting the need to identify and classify risks promptly. The study examines various risk factors related to finance, law, environment, and logistics. The study explores various risk management strategies, such as contract management, risk distribution, evaluation, mitigation, and sharing. The article discusses the significant impact of risk management software, such as Procore, PlanGrid, e-Builder, Autodesk BIM 360, and Oracle Primavera, on improving project results. The study finishes by highlighting the importance of proactive risk management in protecting against financial losses and maintaining a reputation in the construction business.

The present paper investigates the effect of poverty on foreign direct investment (FDI) inflows in developing countries. It complements the important extant literature on the effect of FDI inflows on poverty by examining the issue the other way around. The analysis is conducted using a sample of 117 countries over the period 1980-2017, and the two-step system Generalized Methods of Moments (GMM) technique. It has relied on two indicators of poverty, namely poverty headcount ratio and poverty gap. Findings indicate that over the full sample, poverty influences negatively FDI inflows, including through its adverse effect on human capital (that is, both education and health). Unsurprisingly, low-income countries (considered as poorest countries in the full sample) experience a higher negative effect of poverty on FDI inflows than other countries. On another note, participation in international trade matters for the effect of poverty on FDI inflows. In fact, an increase in poverty levels results in lower FDI inflows in countries that experience low workers' productivity, a less developed financial sector, and a low level of infrastructure development. Furthermore, the effect of poverty on FDI inflows does not depend on the prevailing economic growth rate. Finally, the analysis has revealed the existence of a non-linear effect of poverty on FDI inflows for the poverty headcount indicator, but not for the poverty gap indicator. The non-linear effect of poverty headcount on FDI inflows is such that a rise in poverty headcount ratio results in lower FDI inflows, but an additional increase in poverty more than further discourages FDI inflows. The conclusion discusses the implications of these findings.

This study established a relationship between computerized accounting information systems (CAISs) and performance of SMS business enterprises in Burao, Somaliland. The main three objectives of this study were; (1) to assess the impact of computerized accounting information System on the performance of SMS business enterprises in Burao. (2) To measure the role of CAIS in the control system and management performance of SMS business enterprises in Burao. (3) To measure the impact of CAIS on the financial and operational performances in SMS business enterprises in Burao. The research approach of the study was quantitative research approach. The data used in this study was primary data obtained from the results of questionnaire survey on respondents. It was also utilized secondary data from the national documents, reports, different books, published materials and websites on the topic under the study. Using Slovene's formula, a sample size of 150 was used to collect data from respondents at SMS business enterprises in Burao through purposive sampling techniques. The data was analyzed through use of descriptive and inferential statistics. Reliability test and Validity test were also employed to test consistency of the measure and validity of data collected. The research findings indicated that there is a positive relationship between CAIS and the Performance of SMS business enterprises in Burao. This study recommended that SMS business should take advantage of the excellent benefits derivable from the adaption of well-designed CAIS because this will help to achieve a high and acceptable standard of quality in the performance of their business operations. The Management of SMS business enterprises (using CAIS) will benefit from this study understanding the impact of CAIS on their business practices. The academic generation and researchers will also benefit from the study in terms of literature review in matters of computerized accounting systems. In other terms, this will act as a point of reference for other future researchers.

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

1) Background: Genomic medicine harbors the real potential to improve the health and healthcare jour-ney of patients, care provider experiences, and improve health system efficiency – even reducing health care costs. There is expected to be an exponential growth in medically necessary new genome- based tests and test approaches in coming years. Testing can also create scientific research and commercial opportu-nities beyond healthcare decision-making. The purpose of this research is to generate a better under-standing of Canada’s state of readiness for genomic medicine, and to provide some insights for other healthcare systems; (2) Methods: a mixed-methodsapproach of literature review and key informant in-terviews with a purposive sample of experts was used. Health system readiness was assessed using a pre-viously published set of conditions. (3) Results: Canada has created some of the established conditions but more needs to be done to improve the state of readiness for genome-based medicine. Important gaps are the need for linked information systems and data integration; evaluative processes that are timely, and transparent; navigational tools for care providers; dedicated funding to facilitate rapid onboarding and supports test development and proficiency testing; and broader engagement with a broader set of inno-vation stakeholders. These findings highlight the known role of organizational context, social influence, and other factors that are known to affect the diffusion of innovation within health systems

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

The famous Capital Asset Pricing Model (CAPM) takes into account only business risk. In practice, companies use debt financing and operate at non–zero levels of leverage. This means that it is necessary to take into account the financial risk associated with the use of debt financing along with business one. The purpose of this paper is to simultaneously take into account the business and financial risk. A new approach to CAPM has been developed that takes into account both business and financial risk. We combine the theory of CAPM and the Modigliani–Miller (MM) theory. The first is based on portfolio analysis and accounting for business risks in relation to the market (or industry). The second one (the Modigliani–Miller (MM) theory) describes a specific company and takes into account the financial risks associated with the use of debt financing. The combination of these two different approaches makes it possible to take into account both types of risks: business and financial ones. We combine these two approaches analytically, while Hamada did it phenomenologically. Using the Modigliani–Miller (MM) theory, it is shown that the Hamada’s model, first model, used for this purpose half a century ago, is incorrect. In addition to the renormalization of the beta–coefficient, obtained in the Hamada model, two additional terms are found: the renormalized risk–free return and the term dependent on the cost of debt kd. A critical analysis of the Hamada model was carried out. The vast majority of listing companies use debt financing and are levered, and the Hamada model is not applicable to them in contrast to a new approach applicable to leveraged companies. Implemented a new approach to specific companies. A comparison of the results of the new approach with the results of the conventional CAPM is shown. Two versions of CAPM (market or industry) are considered.

We investigate how formal institutional distance (FID) moderates the relationship between cul-tural distance (CD) and the financial performance of foreign subsidiaries firms. Following recent research, we estimate the asymmetric effects of CD by considering its size and direction towards host countries on the opposite poles of each cultural dimension` scale. We propose that a limited understanding of the formal institutions in the host country, as measured by the magnitude and direction of the FID, can have a positive moderating effect, increasing the impact of CD on finan-cial performance. This is mainly because foreign subsidiary firms may be more reliant on their ca-pacity to navigate the less formal (and more implicit) aspects of the host country's institutional environment, such as their ability to cope with the CD. We use foreign subsidiary data from the Orbis database including 22 developed and 22 developing home countries and over 1400 foreign subsidiaries during a period of 3 consecutive years operating in 10 of the largest economies (host countries) in Latin America including: Argentina, Brazil, Colombia, Chile, Ecuador, Mexico, Panama, Peru, Uruguay, and Venezuela. Findings confirm the asymmetric effects of CD, howev-er, by considering the direction of FID, our findings reveal that the higher the FID towards less developed host countries, the more significant the effects of CD on the financial performance. These findings contribute to the knowledge of how formal and informal institutional distances in-teract by showing that the greater the FID towards less developed host countries, to higher the impact of CD on the financial performance of foreign subsidiary firms.

This study examines whether information about the winners of the Sustainability Reporting Award (SRA) contributes to the usefulness of the information in the financial statements. This study used a sample consisting of 110 winners of SRA (SRA firms) and 110 companies that did not receive SRA (non-SRA firms) from 2008 to 2016. The study found that earnings per share (EPS), book value per share (BVPS), and earnings per share change (EPSC) are value relevant information. Results of comparison between SRA firms and non-SRA fimrs, this study found that EPS positive association with stock price and with returns for SRA firms is higher than that for non-SRA firms. Findings of this study also show that, value relevance of BVPS for non-SRA firms is higher than that for SRA firms. When mesures of Price and BVPS are transformed into natural logarithm, the value relevance of BVPS for SRA firms is higher than that for non-SRA firms. Thus, the results are sensitive to measures of the variables. The findings of this study indicate that information about the winners of SRA contributes to the usefulness of financial statements.

. In this article, a search for a calculation method and an analysis of performance indicators of mutual investment funds is carried out. Many factors can influence the return on investments in portfolio investments, which makes the choice of the fund incredibly difficult. However, in addition to the fact that it is difficult to determine which indicators should be given more attention and which should be omitted, it is not so easy to get these data. Some of them are publicly available on the Internet, while others can only be found in trading systems that are not accessible to people outside of this area. The article proves that a well-trained neural network can easily find existing patterns between risk and expected return on investment. It is a well-trained neural network that provides the ability to use the "what-if" function to justify your choice on real factors, as well as the ability to download available data and calculate the estimated income and its changes. This makes it much easier to choose a Fund, especially for inexperienced investors. The article also presents the results of a study of the dependence of estimated income on correlation, standard deviation, and volatility using a trained neural network. According to the theory, higher values of these three factors correspond to a higher amount of income. The obtained graphs of the calculated income dependence on correlation, standard deviation, and volatility confirmed the correctness of the neural network training and compliance with the relations described in the theory. The paper presents graphs of the dependence of the estimated income on the beta and alpha coefficients. The higher the beta and alpha indicators, the higher the expected return on investment. This corresponds to the dependency accepted in the model. When the values of the beta and alpha coefficients increase, the income also increases, which is completely consistent with the theory.

In this article, a search for a calculation method and an analysis of performance indicators of mutual investment funds is carried out. Many factors can influence the return on investments in portfolio investments, which makes the choice of the fund incredibly difficult. However, in addition to the fact that it is difficult to determine which indicators should be given more attention and which should be omitted, it is not so easy to get these data. Some of them are publicly available on the Internet, while others can only be found in trading systems that are not accessible to people outside of this area. The article proves that a well-trained neural network can easily find existing patterns between risk and expected return on investment. It is a well-trained neural network that provides the ability to use the "what-if" function to justify your choice on real factors, as well as the ability to download available data and calculate the estimated income and its changes. This makes it much easier to choose a Fund, especially for inexperienced investors. The article also presents the results of a study of the dependence of estimated income on correlation, standard deviation, and volatility using a trained neural network. According to the theory, higher values of these three factors correspond to a higher amount of income. The obtained graphs of the calculated income dependence on correlation, standard deviation, and volatility confirmed the correctness of the neural network training and compliance with the relations described in the theory. The paper presents graphs of the dependence of the estimated income on the beta and alpha coefficients. The higher the beta and alpha indicators, the higher the expected return on investment. This corresponds to the dependency accepted in the model. When the values of the beta and alpha coefficients increase, the income also increases, which is completely consistent with the theory.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

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.