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.

Financial statement analysis is an analysis that uses analytical procedures to evaluate a company's financial health, risks, performance, and future growth potential. This article aims to find out the application of analysis of financial statements of PT. Astra Internasional, Tbk towards the decision-making of novice investors in the Indonesian capital market. This article is qualitative descriptive with literature studies. The results of this article can be concluded that the analysis of the financial statements of PT. Astra Internasional, Tbk can help novice investors in the Indonesian capital market in making investment decisions.

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.

An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

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.

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.

This comprehensive review delves into gold market business management, covering historical perspectives, market dynamics, mining models, investment strategies, technology, regulations, global trends, and sustainability. Starting with a historical overview, it traces the gold market's evolution and analyzes supply-demand dynamics, price fluctuations, and geopolitical influences. The article scrutinizes gold mining models, evaluating environmental and social responsibilities, and explores financial management strategies, emphasizing risk and gold's role in portfolios. Examining technology's role, it highlights innovations in exploration, mining, and processing for improved efficiency and sustainability. The regulatory landscape analysis encompasses compliance challenges, environmental regulations, and ethical considerations, emphasizing global impacts. Global market trends are explored, identifying potential opportunities and challenges, offering strategic insights. The review concludes by evaluating sustainability practices, emphasizing environmental conservation, community engagement, and ethical sourcing.

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 research article focused on discovering appropriate liquidity risk concerns in the financial Sector. Primarily, it deals with the appropriate liquidity factors that affect financial performance services pertained to several liquidity crises and the respective liquidity measures. Liquidity is considered the primary constraint. The Liquidity Ratios are the defective condition of calculating the financial performance services and their respective status. For fulfilling this performance service status prediction, this research use calculation for consistency index procedure. Liquidity index-based predictions have collaborated concerning the price movement in the stock market. The Liquidity will affect the trading and market transparency whenever the price moment varies. To control this trading and not market transparency of liquidity index be employed the greedy approach for estimating the most factor affected by the liquid index. Hence, this research elaborates on the greedy approach today with 25 essential financial factors. Each Panther will have combined with the tractor-confirmed liquidity crisis whenever the factors of liquidity crisis are combined with the actual financial factor, the minimum and maximum Degree of matching are calculated. This calculation determines the factors that affected the Liquidity concerning their exact, Plugin, subsume, and fail condition. Eventually, the search concludes the determination of factors affecting Liquidity concerning the actual finance factor and liquidity crisis. Finally, the result was concluded with the Degree of matching and their efficiency of finding liquidity factors with a successful reasonable result. Based on the Degree of matching result, this research found the actual reason for Liquidity with the help of various financial factors

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.

Finding the key factor and possible "Newton's laws" in financial markets has remained the central issue in this area. However, with the development of information and communication technologies, financial models are becoming more and more realistic but complex which is contradictory to the objective law “Greatest truths are the simplest”. Therefore, this paper attempts to discover the most critical parameter and establishes an evolutionary model which is independent of micro features. In the model, information is the only key factor and stock price is the emergence of the collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provide a new idea for the study of key factors and core structures in the financial market.

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).

The aim of this study was to determine the impact of information technology in management of risks in the capital market-listed company is in Tehran Stock Exchange. The purpose of the present study is an applied descriptive approach. The target population for the survey, companies that from 2009 to the first half of 2015 have been a member of the Tehran Stock Exchange, through Cochranʼs sample size of 140 companies, respectively. We used cluster sampling method. In order to collect data from two questionnaires: risk management questionnaire Foakeh (2013) has 38 items and a standard questionnaire Chanvyas (2006) has 40 items, the whole five-item Likert scale questionnaire is above has been used. Data gathered through the questionnaire, sign the application was 21spss. For inferential analysis of the variables and to analyze the data from different statistical tests and regression was used Kolmogorov-Smirnov test. The results showed that information technology on risk management and its dimensions (primary market risk, market risk and the risk of secondary non-financial) impact.

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.

Global economy, financial markets and technology are on the verge of moving to a new stage of development – information society. Three major world countries the US, Russia and China will play a leading role in this process. However, the specific development path will depend on many factors, including the stability of legal systems, security of computer and financial technologies, the effectiveness of legislation. The focus of this article is to assess the current condition of the economy the major countries, to identify trends and methods of stabilization of legal systems on the basis of balance of rights and obligations of the business, suggest some methods of objective evaluation of the impact factors of the total taxes, fixed costs and the legal system dynamics on financial indicators.

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.

Four discrete models using the exact spectral derivative discretization finite difference (ESDDFD) method are proposed for a chaotic five-dimensional, conformable fractional derivative financial system incorporating ethics and market confidence. Since the system considered was recently studied using the conformable Euler finite difference (CEFD) method and found to be hyperchaotic, and the CEFD method was recently shown to be valid only at fractional index , the source of the hyperchaos is in question. Through numerical experiments, illustration is presented that the hyperchaos previously detected is in part an artifact of the CEFD method as it is absent from the ESDDFD models.

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.

The aim of this research work was to provide model for predicting stock volatility in Nigeria Stock market. To achieve this, monthly data for Nigerian stock exchange, Exchange rate, Share index and inflation rate was collected for a period of January 1990 to December 2016.The descriptive statistics revealed these variables to exhibit volatility as a characteristics of financial time –varying series. DCC Model was fitted, were the coefficients for all the parameters and that of the correlation-Targeting (rho_21) are both negative and positive and tend very close to 1 and -1, indicating that high persistence in the conditional variances. The Model DCC, satisfied the properties of a good model of conditional mean and variance of the confidential Interval (C.I) of 1 and -1, that is, the conditional variances are finites and their series are strictly stationary. This therefore implies that the Nigerian Stock Exchange, Exchange rate, share index and Inflation rate will experience a non-steady shock in the Stock market. However Each of these variables have different length of recovery (volatility half- life) ranging from 1.5month, 6.5months, 6months to 2,4months for stock exchange, exchange rate, share index and inflation rate respectively. By implication, the volatility of these variables had a long memory, persistence and mean-reverting.

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.

Taiwan's bed and breakfast (B&B) development has been more than 25 years ago, from the first generation of retirement to the B&B industry, serving tourists from all over the world accom-modation, providing breakfast and accommodation mode to the present, after the change of employees from the second generation, more and more attention is paid to the cultivation and cultivation of business ability. With the advent of the era of AI technology networking, the ca-pabilities required for B&B operation are more focused on all aspects of service, and the good marketing ability of B&B can promote a green chimney-free tourism economy, which is also a favorite industry of various governments and the public. This study explores the basic vocational competency indicators required for running a B&B, with (1)EQ emotion management skills, (2)Financial management ability, (3)(SK) technical skills, and (4)Marketing and promotion management capabilities. Through the study method, the K-S Z test was determined by the con-sensus conclusion of expert reliability and validity p value

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

This study investigates the latest advancements in utilizing machine learning techniques for predicting financial market movements. Financial markets are complex systems influenced by numerous factors, making accurate predictions challenging but highly valuable. The research explores various machine learning approaches, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, to enhance the accuracy and reliability of financial market forecasts. The study provides a comprehensive review of current methodologies, highlighting key innovations and their applications in financial market prediction. Techniques such as neural networks, support vector machines, random forests, and ensemble methods are examined in the context of their ability to analyze vast amounts of financial data, identify patterns, and generate actionable insights. Additionally, the research delves into the integration of alternative data sources, such as social media sentiment, news articles, and macroeconomic indicators, to improve prediction models. Through empirical analysis and case studies, the study demonstrates the effectiveness of advanced machine learning models in predicting stock prices, currency exchange rates, and commodity prices. It also discusses the challenges associated with these techniques, including overfitting, data quality issues, and the need for interpretability in model outputs. Findings suggest that machine learning techniques can significantly enhance the predictive power of financial models, offering more accurate and timely insights for investors and financial analysts. The study concludes with recommendations for future research directions, emphasizing the importance of model transparency, the incorporation of diverse data sets, and the continuous adaptation of machine learning algorithms to evolving market conditions.