This study examines the effect of market risk on the financial performance of 31 non-financial companies listed on the Casablanca Stock Exchange (CSE) over the period 2000-2016. We utilize three alternative variables to assess financial performance, namely return on assets, return on equity and profit margin. Next, we use the degree of financial leverage, the book-to-market ratio, and the gearing ratio as market risk variables. Besides, we employ the pooled OLS model, the fixed effects model, the random-effects model, the difference GMM and the system GMM models. The results show that market risk indicators have a negative and significant influence on the companies' financial performance. The elasticities are greater following the book-to-market ratio compared to the degree of financial leverage and the gearing ratio, respectively. In most cases, the firm size, the tangibility ratio, and the cash holdings ratio have a positive effect on financial performance, whereas the firms' age, the debt-to-income ratio, stock turnover, and leverage hurt the performance of these non-financial companies. Therefore, decision-makers and managers should mitigate market risk through appropriate strategies of risk management, such as derivatives and insurance techniques.

This study aims to explore methods for automating the replication of correlated portfolios across shares using various algorithms. The goal is to select one algorithm for implementation in a visualization tool that will facilitate portfolio building. The dynamics of the stock market provide a comprehensive view of the global economy, acting as a catalyst for stakeholders to participate and benefit various sectors financially, primarily through investments in companies that yield returns and contribute to the economy. The evaluation and comparison of these algorithms are crucial for making well-informed decisions regarding the core objectives of the study. Ultimately, a determination will be made as to whether the program can effectively analyse data from Yahoo finance to provide a quick overview of the financial market state of companies in the S&P 500 US stock prices.

According to its inner property, a crisis in the financial market can be considered as a collective behavior phenomenon. Through the prism of collective behavior, the crisis does not happen if the companies are independent of each other. In this work, cooperative movement processes in a stock market are investigated in a manner similar to that Vicsek first described collective behavior for self-propelled entities. To this end, a phase space is defined as the one in which the return of volume of transactions versus return of price is represented with each share in each day corresponding to a unique point in the space. The findings of the observation show that during times of crisis, the phase space is limited with the vector velocity of shares in the same direction. In contrast, on a regular day, the phase space is entirely accessible, with vector velocity aligned randomly. Moreover, in line with the Vicsek model, an order parameter is introduced, which evaluates the cooperative effects for the shares so that the higher the value of this parameter, the stronger the collective behavior of the shares.

In this paper, we focus on the impact of financial liberalization on stability of emerging stock market. We identify crises in a group of Latin American (Argentina, Brazil and Chile) and Asian countries (Philippines, Korea, Taiwan and Thailand) during 1975–2005. This paper aims to apply the methodology of CMAX method. Our results indicate that liberalization triggers more unstable stock market in the short run and generate several crises. Still, liberalization seems to generate more stable financial markets in the long run. Financial liberalization does not increase the frequency of crises in emerging countries and at long-term, crises are less several.

Financial market data analysts are increasingly turning towards machine learning techniques for data analysis and making decisions. This paper explores how knowledge graphs, essentially a network of real entities and their relationships, can be used to improve collaborative financial market data analysis and make it more efficient and user friendly. This paper reviews the current state of financial market data analysis, the challenges in deploying machine learning models in industrial environments, and the concepts such as ML-Ops and knowledge graphs. A software architecture comprising various roles, layers and services such as Data Engineers, Data Analysts, public users, APIs, workflows, analytics libraries, UX layer, Business Layer, etc. is introduced and described in detail. Then, it discusses how knowledge graphs can be used to enhance collaborative financial market data analysis. Finally, a case study is presented to demonstrate the usage of the whole system, within the context of financial market data analytics of equities.

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16th June 2016 to 9th July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Financial bubble prediction has been a significant area of interest in empirical finance, garnering substantial attention in the literature. This study aimed to detect and forecast financial bubbles in the Vietnamese stock market from 2001 to 2021. The PSY procedure, which involves a right-tailed unit root test to identify the existence of financial bubbles, was employed to achieve this goal. Machine learning algorithms were then utilized to predict real-time financial bubble events. The results revealed the presence of financial bubbles in the Vietnamese stock market during 2006-2007 and 2017-2018. Additionally, the empirical evidence supported the superior performance of the Random Forest and Artificial Neural Network algorithms over traditional statistical methods in predicting financial bubbles in the Vietnamese stock market.

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

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.

The primary objective of this study is to examine the extent of financial integration between Latin American and US financial markets, particularly in light of recent efforts to foster integration through trade agreements. Spanning from January 1, 1990, to December 31, 2019, the sample focuses on major market indices and key sectors. Financial integration is quantified using a DCC multivariate GARCH model, incorporating a smooth transition model, structural breaks, and regression-based approaches. Results indicate increased comovement with the US for main market indices in Argentina, Chile, Colombia, Mexico, and Peru, while Brazil shows a decrease. Similar trends are observed in sectoral analyses. The study also reveals heightened correlation post-trade agreements. Structural break analysis highlights significant shifts in dynamic correlations for countries with US free trade agreements. These findings support the argument of increased financial integration, bearing significance for portfolio diversification and international policy formulation.

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.

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.

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.

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.

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

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 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.

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.

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.

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

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.

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.

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 has been observed that most adult citizens, globally, are not enjoying financial services. They are financially excluded, especially rural dwellers. Monetary authorities initiated financial inclusion, with the aim of including all adult population who are excluded from financial services into the financial system at an affordable cost to meet their basic needs. Studies have shown that it encourages household savings and affects growth positively. Central Bank of Nigeria initiated financial inclusion in 2012 and commenced implementation in 2014. Given the short period of its implementation, this study ascertained the effect on economic growth in Nigeria. The study used time series data ranging from 2004 to 2021. The study covered the pre-inclusion period, through its implementation period. Ordinary Least Square technique was adopted for data estimation. Preliminary and post-estimation tests were also conducted. Despite the short period of the implementation of financial inclusion strategy, the OLS result affirmed that it positively affects economic growth. It was concluded that financial inclusion is a growth driver. It was recommended that Central Bank of Nigeria, in collaboration with commercial banks and microfinance banks, continue to enforce inclusion exercise in all rural communities, ensuring that women, youths, farmers, traders in the informal sector, are captured by the financial system. Also, Central Bank Nigeria should set up financial inclusion compliance committee at the Local Government levels, with a State Monitoring unit to ensure compliance.

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

This study explores the impact of financial inclusions on financial resilience. In doing so, we utilize the World Bank’s data on global financial inclusions. Our study confirms that the respondents with financial accounts are more resilient than those without accounts. The chances of being financially resilient are around 1.4 times higher for the account holders than their counterparts. We find a significant relationship between gender and financial resilience; males are 1.4 times more resilient than females when other covariates are considered in the regression model.

The field of financial literacy is intricate and developing. This systematic review of the literature provides a thorough overview of financial literacy. It examines the sample of 3182 papers of the period 1990 to 2023 from different databases such as Scopus, Web of Science, IEEE, Science Direct, and JSTOR and identifies important publications, knowledge gaps, and rising themes. The four major topics that emerged are the degree of financial literacy among various groups of people, gender disparity in financial literacy, the influence of financial literacy on financial behavior and planning, and the impact of financial education on day-to-day decisions. Groups with lower levels of financial literacy include adults, women, people in the lower-income band, rural people, etc. Financial planning and financial behavior are positively associated with financial literacy. Financial literacy could be enhanced with financial education; however, output depends upon program design and experimental group. The review also discusses financial literacy in the context of gender disparity, including its definition, difficulties encountered, and possible solutions. This systematic study will help researchers, practitioners, and policymakers by providing insightful information on financial literacy and its ramifications.

A study analyzed financial statements in the beer sectors of Italy and Spain during 2019-2021 using compositional data analysis (CoDa) to gauge pre- and post-pandemic financial health and aid sector-level decision-making. Geometric averages were employed to mitigate statistical issues, offering more accurate insights than arithmetic averages. The exploratory, quantitative methodology calculated key financial ratios and profitability metrics. Results indicated satisfactory short-term solvency. Returns and margins remained subpar despite pandemic policies. Geometric averages proved essential in portraying sector health accurately, underscoring the need for their use in classic brewing sector ratios. The study also showcased CoDa's efficacy in diagnosing sectoral financial health for informed decision-making. Furthermore, a web analysis revealed varying transparency levels in communicating non-financial indicators, with Spain excelling in waste, water, and energy disclosures, while Italy prioritized energy and waste with less transparency.

This study focuses on the creation and implementation of a platform to improve personal financial management for scholarship students of the National Program of Scholarships and Educational Credit (Pronabec) in Peru. Using a mixed methodology that combines qualitative and quantitative techniques, the effectiveness and user satisfaction with the platform were evaluated. The results indicated that 50\% of users found the tool useful, 55\% considered it easy to use, 70\% reported a positive impact on their personal budget planning, and 85\% expressed high overall satisfaction. In addition to providing tools for financial recording and analysis, the platform promotes financial education among its users, significantly contributing to improving their financial management skills.

This study focuses on a sample of Chinese listed companies from 2019 to 2020 to explore the relationships among corporate social responsibility, financial constraints, and financial performance. In addition, we discuss five factors affecting financial constraints. We also analyze the types of enterprises that can improve their financial performance by implementing corporate social responsibility keeping in mind the factors that lead to a high degree of financial constraint. The results indicate that: 1. The degree of financial constraints has a negative and significant impact on financial performance; 2. There is a reverse relationship between the degree of financial constraints and the effectiveness of corporate social responsibility measures; 3. Enterprises with high financial constraints (due to lower financial slack and revenue growth rates) can significantly improve their financial performance through the implementation of effective corporate social responsibility programs. 4. Enterprises with high financial constraints, caused by financial slack and revenue growth rate, can significantly improve their financial performance by implementing corporate social responsibility programs.

Reporting on CSR activities has become the essence of reporting for modern business entities. In this regard, particular attention is paid to public interest companies. Therefore, the following paper aims to answer the question of whether there are differences in the linguistic structure of the studied CSR reports in three selected industry indices on the Warsaw Stock Exchange (WSE) in Poland, i.e. WIG-energy index, WIG-fuel index, WIG-mining index and their relationship with the performance of selected companies. The study was conducted on a purposely selected sample of companies between 2013 and 2018. A total of 138 CSR reports and 138 annual separate financial statements prepared in accordance with international balance sheet law were collected. The study was carried out based on a panel regression model. It was found that CSR reports contained similar average percentages of parts of speech such as nouns and adjectives. When linking the economic performance of companies, expressed with selected indices, to the information on the implementation of CSR concepts, it was revealed that the results are more likely to describe business performance when it is satisfactory.

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

Financial literacy is a path to sustainability and has an important role in ensuring the financial sustainability of individuals, families, enterprises and national economies. The level of these economic indicators such as debt, payment discipline, savings and financial management all translate into prosperity or insolvency and bankruptcy and result partially from financial literacy. The higher the level of financial literacy, especially of young people, the more favorable the level of economic indicators, which translates into the economy and sustainable development. However, despite many years of research, there is still a lack of a uniform and coherent definition of financial literacy, as well as methods for measuring it. This study offers a small step forward, presenting the authors’ own view of the concept of financial literacy, the conceptualizations of financial literacy and methods used for investigating. Moreover, they present the results of a survey conducted on the financial behaviour, financial attitudes, and financial knowledge of the Polish youth and compare this to a PISA study on 15-year-old students. Results demonstrate a good, and partially very good, level of financial literacy among young Poles, showing that they are rational in their financial decision making.

While financial literacy is crucial in improving the population's financial well-being, its effectiveness can be enhanced by exposure to financial information. This paper investigates the nexus between financial literacy, financial information consumption, and financial well-being in rural Ghana. The study employed cross-sectional data from a survey of 663 rural households using simple random and cluster sampling with reflective-reflective constructs. The data was analyzed using Structural Equation Modelling-Partial Least Squares. The findings reveal that financial literacy and financial information consumption significantly enhance financial well-being among rural households in Ghana. Financial Literacy also promotes Financial Information Consumption. Notably, financial literacy's impact on financial well-being is stronger when mediated by the consumption of financial information. These findings underscore the importance of improving financial literacy and information access to uplift financial well-being in rural areas. Moreover, the study highlights that financial literacy education is crucial as it plays a mediating role; recipients of financial education experience a more substantial impact. Such findings emphasize the importance of acquiring financial knowledge and effectively processing financial information to achieve financial prosperity, particularly in rural areas. These findings should motivate individuals, especially those in rural areas, to process financial information successfully rather than merely acquiring financial knowledge to attain financial prosperity.

In a business environment that is constantly changing, intellectual capital stands out as the most valuable asset for companies. Those who invest in developing their employees, especially their Chief Financial Officers (CFOs), tend to outperform their competitors. This study explores the relationship between the intellectual Capital of CFOs and the financial performance of companies listed on B3. The 100 companies with the largest capitalization listed on B3 in 2022 were analyzed, with available data on the CFO. A statistical model was applied in a cross-section analysis, considering two of the three subdivisions of Intellectual Capital: Human Capital ( experience, education, and certifications) and Relational/Social Capital ( network and reputation of the CFO). The results indicated a statistically significant relationship between CFOs' Human Capital level and the performance of the companies they manage, measured by EPS, ROA, and ROE. However, no statistical significance was found for Relational/Social Capital. This study contributes to the literature on intellectual capital and corporate finance in the Brazilian context, providing new evidence on the importance of CFOs' intellectual capital for company performance. It was observed that there are few equivalent studies in Brazil on the topic, especially about the Intellectual Capital of CFOs of companies listed on the stock exchange (BOVESPA).

We seek to examine effective collaboration when engaging in value cocreation with the customer for mobile financial services in EMs. Our study is a motivation for multi-stakeholder engagement in value cocreation with financial inclusion of all consumers including marginalized populations. Furthermore, our work discovers that social sustainability is a product of social creativity and results in financial inclusion. We apply a qualitative descriptive methodology utilizing exemplars from extant research and phenomenology to analyze the data from the contexts of Ghana and Kenya. The results show that value is co-created for mobile financial services in networks in emerging markets when actors integrate resources for social and environmental sustainability as essential elements for mobile financial inclusion.

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

There has been rampant fold-ups, merger and acquisitions occurring in the Ghanaian banking industry. Then, the questions arise: Is the Ghanaian Financial System in Crisis? This study was conducted to find answers to these problems unsolved with prior literature. A sample of seventy customers of the Royal Bank, 8 employees of the Royal Bank and 2 managers of the Royal Bank were selected for a case-survey. The study also monitored the Trend of the Ghanaian Financial System through the reading and monitoring of daily news on the Financial System and reports of banks. The data from the field and the secondary data from news and reports were analysed symmetrically. The study drew on Minsky’s Financial Crisis Theory to explain the phenomenon in the Ghanaian economy and to draw predictions of what would happen in other developing economies. The study found out that: (1) The Ghanaian financial system is fragile and it holds true for most developing economies; (2) The financial system suffers greatly when the confidence level of customers falls significantly; (3) Management information systems raises the confidence level of customers (borrowers and lenders) such that there is a greater fall and impact in times of instability in the economy; (4) The higher the level of MIS adoption in an unstable economy, the more fragile the Financial System becomes and (5) A higher adoption of Management Information Systems in a Fragile Financial System indirectly contributes to Financial Crisis of the Financial System.

Aim: The aim of the paper is to assess the usefulness of sectoral means in financial analyses of enterprises. Research methods: The article uses an inductive-deductive approach to assess the suitability of sectoral means used in analyses of financial standings of enterprises. The method of analysis and logical construction was used for literature review. The methods of descriptive statistics such as, arithmetic means, standard deviation and coefficient of variation were used in order to analyze the data and draw conclusions based on the results. Contribution: The assessment of the usefulness of sectoral means in the context of their variability seems to be a crucial aspect for the analysis of a company’s financial situation. When assessing the financial standing of an enterprise, it is necessary to use a comparative analysis method, based on, inter alia, a comparison in space. This type of comparison requires the use of sectoral means, whose assessment is extremely important to drawing reliable and correct conclusions of the financial situation of a company.

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

The present study aims to find out the financial risk tolerance scores of the rural residents of Ludhiana district. Also, how drivers such as socio-demographic factors, namely, gender, age, education, annual income, existence of loan, occupation, employment status and psychological factors, namely, deliberative thinking, optimism and personality type A/ B impact financial risk tolerance of rural residents. The study is important for financial policy makers as they will become aware about scenario of rural residents and frame policies accordingly. Stepwise regression analysis was carried on. The result was found out that optimism positively and deliberative thinking negatively impacted financial risk tolerance, but personality type A/B did not have a significant impact on financial risk tolerance. Socio-demographic factors such as existence of loan, full-time salaried individuals, marital status significantly impacted financial risk tolerance. Other factors failed to have impact on financial risk tolerance of rural residents in the current study.

This paper focuses on the paradigm that explains the link between energy transitions and financial transformations. In particular, it attempts to shed some light on the following question: How did the transition from wood to coal affect financial development in late 18^th and early 19^th century Britain? The results demonstrate that the energy transition in Britain during the 1^st Industrial Revolution constituted an important moment in the country's financial development and can be used as an example of how an energy innovation can foster the financial development of particular countries or regions.

Development of Halal Tourism in Indonesia is the focus of the Indonesian government and MSMEs have an important role in supporting the development of halal tourism in Indonesia. This study aims to examine the relationship between marketing communication and Islamic financial literacy on Islamic financial inclusion and MSMEs performance in the halal tourism sector. A covariance-based SEM technique utilizing LISREL software was used to analyze the data from this investigation. Non-probability sampling was employed to collect the data, and the sample consists of 152 halal tourism entrepreneurs. We find that positive and significant association between Islamic financial inclusion and business performance. We also find that there are a positive and significant association between Islamic Financial Literacy and Islamic Financial Inclusion. Marketing communication and Islamic financial inlcuon a positive relationship but insignificant. This studi implies that to establish a halal tourist ecosystem for long-term development in Indonesia, commercial actors must lend their full support. This study demonstrates that they can thrive when MSMEs in the halal tourist ecosystem are backed by Islamic banking and Islamic rural banks. As a result, a more accommodating approach from Islamic banking is required to provide access to halal finance for business actors in Indonesia’s halal tourism ecosystem.

In exploring the intricacies of financial analysis, this study delves into the efficacy of traditional financial ratios versus cash flow ratios in foreseeing a company's fiscal well-being. By harnessing the PRISMA 2020 framework, an exhaustive and systematic dissection of diverse scholarly articles was embarked upon. The quest was to unearth the more reliable predictor of financial stability among these two sets of ratios. With a methodical examination of academic writings, this inquiry juxtaposed various models, scrutinizing their foresight capabilities. The revelations were telling; cash flow ratios emerged as more potent forecasters compared to their traditional counterparts. Intriguingly, models that blend both ratio types showed a marked improvement in predictive accuracy, hinting at a synergistic effect. This underscores the insight that while traditional ratios are informative, their amalgamation with cash flow ratios yields a richer, more rounded grasp of a company’s fiscal state. Conclusively, for stakeholders, especially investors, adopting this dual-ratio approach is pivotal for enlightened decision-making. This research enriches the financial analysis domain by spotlighting the salience of cash flow ratios, advocating for their integration with conventional methods for a sharper financial health appraisal.

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

The transport sector is pivotal and indispensable in our daily existence, being the exclusive conduit for the intercontinental conveyance of commodities and individuals. Hence, comprehending the phenomenon of business failure within the transport industry assumes paramount significance in delineating an effective competitive policy for this sector. The primary objective of this research paper is to execute a comparative investigation between statistical models forecasting business failure and models founded on artificial intelligence, within the context of the transport sector. This analysis spans the temporal expanse from 2014 to 2021 and encompasses the nations of Portugal, Spain, France, and Italy, aiming to ascertain superior efficacy among these models. The dataset employed for this endeavor encompassed a final cohort of 4866 companies, comprising 2881 that endured as going concerns and 1985 that succumbed to business failure. The models developed for this study exhibited the capacity to accurately categorize a proportion of companies ranging from 71% to 73%. Nonetheless, upon comparative scrutiny of these outcomes with those derived from the statistical models dedicated to business failure prediction, it becomes evident that the latter demonstrate an enhanced predictive prowess, manifesting fewer errors in the classification of the scrutinized companies.

Climate change and net zero are expected to have a significant impact on eco-nomic activity in developing countries, and thus, on the financial cash flow of organizations. In this paper, we propose a procedure for assessing the financial impact of climate change on organizational cash flow in developing countries. Our study contributes to the research on scenario-based climate change impact analysis. The applied research design is exploratory and mainly qualitative, constructing financial impact scenarios combined with narratives to determine the impact of climate change on individual cash flow line items. The narra-tives rationalize cash flow variations and enrich the estimates by adding expert knowledge, in the form of financial micro-stories and descriptions to the pro-posed procedure. We present a hypothetical citrus farming company in a de-veloping country to demonstrate the application of the proposed measurement procedure. The results obtained suggest that (1) the procedure is practical be-cause financial cash flows are known and widely used in financial analysis and because they can be implemented in a spreadsheet; that (2) the procedure pro-vides flexibility because it can be adapted to suit the specific needs of different companies, depending on a selected climate change scenario, the company’s exposure to climate change, its resilience and vulnerability; (3) that the prac-tice of thinking in terms of financial worst-case, base-case and best-case sce-narios, which reflect different levels of uncertainty, helps to model the finan-cial impacts of climate change and provides a forward-looking perspective on how much a company’s cash flow could change.

Of course, now we know that quantum mechanics has been a fundamental structure of our world since our universe came into being. However, it has been only a century since the experimental and theoretical discovery of quantum mechanics and its extensions into many implications and applications. In particular, there are implications across many disciplines that most likely will affect education, health, security, etc. Examples are given of the need for starting education as early as possible in schools, the use of nano-robots to deliver drugs targeted to specific molecular sites, to developing new cryptographic systems to safeguard our privacy.

The medical sector is of the utmost importance in every country for well-known reasons such as their economic development, national security, quality of life, and so on. Many researchers were dedicated to this topic, however, in Romania there are limited studies on this subject. One reason is that most of the businesses in this sector are individual enterprises or small family businesses. In our research paper we deploy several analysis methods to see the financial sustainability of companies operating in the medical industry. By analyzing a sample of 23931 companies and we have concluded that their performance has increase in the peak of the pandemic and that the financial stability is high. Our results are in line with other similar studies that were conducted in other regions.

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

Financial statements depict the condition of a company and need to be analyzed and evaluated to assess its performance. This research aims to analyze financial statements to describe the financial condition, measure business development over time, and evaluate the achievement of the com-pany's objectives. An optimization analysis of financial ratios is performed using the Weighted Goal Programming (WGP) model, which addresses multiple objectives by applying weights based on their priorities. The Analytical Hierarchy Process was used to determine the priority weights of deviation variables from each financial ratio target. Financial ratios were selected based on their impact on profit using factor analysis. The constructed WGP model aims to minimize deviations in Return on Assets, Operating Ratios, Operating Income Ratio, Total Assets Turnover, and Current Ratio. Computational calculations to solve the WGP model are per-formed using Python, with pseudocode provided. A case study on a company in the garment and textile sector was conducted and found that the Operating Ratio, Return on Assets, Operating Income Ratio, and Current Ratio still need improvement by developing strategies to achieve the targets. Sensitivity analysis was also employed to assess the resilience of the model in response to alterations in data.

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

Despite the expansion of financial institutions and proliferation of mobile financial services, reaching the unbanked and bringing them under formal financial services has become a policy concern in many developing countries. Due to the lack of financial accounts, unbanked people prefer informal, risky, and inconvenient mechanisms for receiving, sending and transferring money. Previous studies rely much on common intervention like no account maintenance and opening fees, easy documentation processes and money subsidies for opening financial accounts. This study aims to examine the impact of the motivational workshop on opening savings accounts through causality among the unbanked people in a setting where the respondents are unbanked despite having all the requirements and many institutional offers for opening savings accounts. We encouraged the unbanked people through a one-hour-long motivational workshop to open saving account. Based on our cross-sectional data and randomized controlled trial experiment among 505 unbanked Village Defense Party (VDP) members at Dhubil Union under Sirajganj in Bangladesh, we have the evidence that motivational workshop positively impacts opening ac-counts by 32.33 percent. However, the account opening rate differs in terms of respondent’s pref-erence for financial institutions. Our study also finds that unbanked people have the highest pref-erence for mobile financial services for opening accounts resulting in 15.33 percent. The result of the study has some policy implications for adopting effective strategies of financial access in many developing countries.

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

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

This study aims to determine the cash sales accounting information system that is suitable for online store applications. Using qualitative methods with the object of research, namely the online stores. Sources of data obtained from several online stores. Data collection methods used are semi-structured interviews, observation, and document analysis. From the analysis and design of the buying and selling cycle information system in online business development, it was found that the development of information systems is a very important stage in the efficiency and effectiveness of the buying and selling cycle. By implementing a good and correct accounting information system design on an online store, it can facilitate the recording and minimize the occurrence of errors/loss of data. In addition, with a good information system design, the division of tasks between various sections becomes clear.

Banking sector is generally taken out of sample while the sustainability performance, and the financial performance are compared with each other. The core aim of this study is to analyze the effect of the declarations made in the cope of sustainability reports on the financial performance in the banking sector. Seven banks were included in the study which were placed at least one time in BIST Sustainability Index in between 2010-2017 years. Environment, human resources, product liability and community involvement were determined as sustainability criteria and return on assets, return on equity and net interest margin were determined as financial performance criteria. Non-Parametric Statistic Tests and Panel Data Analysis were used for analysis and types, and the sizes of banks were selected as dummy variables. As a result, it is found that the declarations of sustainability reports have a significant effect only on return on assets and have no significant effect on return on equity and net interest margin. And also, when we analyzed the relationship of sustainability criteria and return on assets, we found that the declarations about environment and human resources have negative effects on return on assets.

Fintech facilitates financial inclusion by introducing new sustainable and accessible business models in developing countries. Open banking API technology is used by various fintech businesses in both developing and developed countries, with varying effects on financial markets. It changes the financial market distribution structure by separating financial product manufacturing and distribution and intensifying competition between traditional financial institutions and fintech companies. Fintech companies innovate by using this tool to create new business models. In December 2019, Korea established a standardized "open banking API platform," sharing an interbank payment network with fintech companies. It has since shown explosive growth, surpassing the trading volume of the UK, the country that introduced open banking APIs for the first time. This study analyzes new business models fintech companies created using this API technology. Based on existing literature, statistics from the platform operator (the Korea Financial Telecommunications and Clearings Institute), and investigations of 30 Korean fintech mobile applications, this study analyzes new fintech business models that provide financial services using this platform. These companies create sustainable business models by combining multiple APIs. Four representative business models (simple funds transfer, simple payment, cross-border remittance, and asset management) are analyzed to reveal how fintech companies create business models with open APIs.

In the context of the economic and political uncertainty associated with the 2019-nCoV pandemic, it is necessary to determine the socio-psychological factors involved in the transformation of the behavior of insurance consumers under the influence of a biogenic threat. This study measures financial anxiety and its impact on the insurance behavior of Russian citizens. The correlation, comparative, and regression analyses of the financial anxiety of Russian citizens cover three stages of observation: before the start of the 2019 nCoV pandemic (“FA up to 19 nCoV; N = 766), during the period of quarantine measures announced in Russia in March 2020 (“FA 19-nCoV-1”; N = 856), and after the relaxation of quarantine measures at the end of April 2020 (“FA 19-nCoV-2”; N = 963).Psychological analysis data were obtained from the online survey “Financial anxiety (in the context of insurance)”. The questionnaire is psychometrically reliable and easy to use. It includes five measurement scales: MR1—Physical manifestations of financial incentive anxiety, MR2—With money shortages and financial uncertainty, MR3—The value of insurance coverage, MP4—Financial Confidence, and MR5—Perception of insurance and investment risks. It was found that Russian citizens consider it important to have insurance coverage for a “rainy day”, and they showed confidence in the insurance market during the biogenic crisis. However, unfortunately, during the 19-nCoV-1 and 19-nCoV-2 periods, Russian citizens did not feel financially secure, unlike in the period before 19-nCoV. Women showed high scores for physical manifestations of financial anxiety and low financial confidence in the future, in contrast to men, regardless of the observation period.

In the context of economic and political uncertainty associated with the 2019-nCoV pandemic, it is necessary to determine the socio-psychological factors in the transformation of the behavior of insurance consumers under the influence of the biogenic threat. This study is devoted to measuring financial anxiety and its impact on the insurance behavior of Russian citizens. Correlation, comparative and regression analyzes of financial anxiety of Russian citizens cover three stages of observation: before the start of the 2019 nCoV pandemic (“FA up to 19 nCoV; N = 766), during the period of quarantine measures announced in Russia in March 2020 (“ FA 19- nCoV-1 "; N = 856) and after the relaxation of quarantine measures at the end of April 2020 (" FA 19-nCoV-2 "; N = 963). Psychological analysis data were obtained from the online survey "Financial anxiety (in the context of insurance)". The questionnaire is psychometrically reliable and easy to use, including 5 measurement scales: MR1. Physical manifestations of financial incentive anxiety, MR2. With money shortages and financial uncertainty, MR3. The value of insurance coverage, MP4. Financial Confidence, MR5. Perception of insurance and investment risks. Russian citizens considered it important to have insurance coverage on a “rainy day” and showed confidence in the insurance market during the biogenic crisis. But, unfortunately, Russian citizens during the 19-nCoV-1 and 19-nCoV-2 periods did not feel financially secure in the future, unlike the period before 19-nCoV. ”Women showed high scores for physical manifestations of financial anxiety and low financial confidence in the future, in contrast to men, regardless of the observation period.

This article examines illicit financial flows (IFFs) perpetuated in financial statements to develop a framework to curb IFFs. IFFs create opacity, impeding economic progress through investment deterrents and financial uncertainty. Through a comprehensive literature review and the synthesis of sets of qualitative propositions, the researchers previously developed a conceptual framework to address IFFs, and the purpose of the present article is to strengthen and validate the framework among stakeholders in the financial and audit sectors. Following a mixed inductive and deductive research approach and a qualitative methodological choice, the researchers conducted interviews among practitioners to enhance the framework, followed by a focus group to validate the framework. IFF challenges that emerged are tax evasion, for example, investments in untraceable offshore accounts, harming the economy, bitcoins not being subject to regulation everywhere in the world and being used by cryptocurrency criminals to transfer IFFs to nations with lax regulations. Internationally, IFF risks are also determined by geographical position, trade links and porous borders among countries emerged as further challenges, calling for entities to execute existing policies, improve tax enforcement methods, apply cross-border coordination, and practice financial reporting transparency aimed at combatting IFF practices. On the strength of these, the industry surveys significantly enhanced the conceptual framework.

This study investigated the integration of indigenous knowledge with modern Customer Relationship Management (CRM) practices in the Indian banking sector. Through a comprehensive analysis of secondary data from industry surveys, government reports, and academic studies (2018-2023), we explored how this integration impacts customer satisfaction, loyalty, and financial inclusion. Our findings reveal that banks that incorporate indigenous practices experience a 25% increase in customer satisfaction scores, 30% lower churn rates, and 20% higher cross-selling success. Financial inclusion metrics have improved significantly, with rural account ownership increasing from 70% to 85% (2018-2023). We identify five key areas of indigenous knowledge integration: personalized relationship management, community-centric banking, vernacular communication, cultural event-based products, and traditional value-aligned services. Despite the challenges in technology integration and staff training, the successful implementation demonstrates the potential of this approach. This study concludes that integrating indigenous knowledge with CRM practices leads to more culturally relevant and effective banking services, contributing to India's financial inclusion and economic development goals. This study provides a framework for banks to create inclusive strategies that are potentially applicable to other sectors in culturally diverse markets.

In this paper, we investigate the “statics and dynamics” return and volatility spillovers transmission across developed and developing countries. Quoted against the U.S. dollar, we study twenty-three global currencies over 2005 – 2016. Focusing on the spillover index methodology, the generalised VAR framework is employed. Our findings indicate no evidence of bi-directional return and volatility spillovers between developed and developing countries. However, a unidirectional volatility spillover from developed to developing countries is highlighted. Furthermore, our findings document significant bi-directional volatility spillovers within the European region (Eurozone and non-Eurozone currencies) with the British Pound (GBP) and the Euro (EUR) as the most significant transmitters of volatility. The findings reiterate the prominence of volatility spillovers to financial regulators.

The commitment of the EU to Corporate Social Responsibility (CSR) is projected in the EU law about annual reporting by businesses. Since EU member states further develop this framework by their own domestic laws, annual reporting with CSR information is not unified and just partially mandatory in the EU. Do all European businesses report CSR information and what public declaration to society do they provide with it? The main dual purpose of this paper is identifying the parameters of this annual reporting duty and studying the CSR information provided by the ten largest Czech companies in their annual statements for 2013-2017. Based on legislative research and the teleological interpretation, the current EU legislative framework with Czech particularities is presented and, via a case study exploring 50 annual reports, the data about the type, extent and depth of the CSR is dynamically and comparatively assessed. It appears that, at a minimum, large Czech businesses satisfy their legal duty and e-report on CSR in a similar extent, but in dramatically different quality. Employee matters and adherence to international standards are used as a public declaration to society more than the data on environmental protection, while social matters and R&D are played down.

The study explores the link between corporate social responsibility (CSR) and the financial performance of listed firms in the Philippines. This research used a longitudinal approach to investigate data from the Philippine Stock Exchange Index (PSEi) from 2019 to 2022. The environmental, social, and governance (ESG) framework assessed CSR performance, while financial performance was measured using return on equity and stock return. Firm profiles such as industry type, firm size, firm age, and auditor type were used as control variables. The researchers used firms' sustainability, annual, and governance reports as the primary data sources. The regression analysis results show insufficient data to support the idea that CSR performance affects the firm’s financial performance. It shows that only governance performance is positively correlated and significant with return on equity. Other components and overall CSR performance were found insignificant with return on equity and stock return. This study provides preliminary information on the link of CSR to the financial performance of listed firms in the Philippines. It extends the current literature on CSR performance and its link to financial performance in an emerging economy setting.

This study addresses a gap in the literature by conducting a comparative analysis of statistical and artificial intelligence techniques for predicting bankruptcy in small and medium-sized enterprises (SMEs). Using a comprehensive database of 7,104 Belgian SMEs, our findings demonstrate that intelligent techniques offer superior predictive accuracy for bankruptcy. Notably, our results indicate that a firm's position in the global value chain significantly affects prediction accuracy, with lower performance for firms operating upstream in the production process. This suggests that bankruptcy risk may spread upward along the value chain. This insight is particularly valuable for practitioners, as it highlights the importance of focusing on specific performance factors based on the type of firms they manage, advise, or collaborate with.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

The healthcare system is intricate and dynamic. For the purpose of organising and planning a system, where allocating funds appropriately is essential and therefore managerial skills play a critical role. A certain portion of each nation's GDP is allotted to the health sectors. Managers and decision-makers in the healthcare systems are using resources effectively, yet the quantity of funding they receive from the government or other stakeholders is insufficient sometimes. A sound understanding of both basic and advanced economics is necessary for the care management, to make cost-effective judgements in public health. Healthcare officials at all levels (regional, state, federal, or international), need to be knowledgeable about public health issues and the related modern economic climates. Business executives and healthcare managers need effective practical knowledge to handle partner-financial allocation (demand-supply chain) and handle economic volatility.

This paper examines the relationship between the ratio of independent board member to the financial performance of the company under the mediation of the three pillars of environment, society, and governance (ESG) in Taiwan. The study addresses a research gap between independent directors and corporate financial performance, an issue that is rarely discussed today. The author develops the hypotheses and tests them by applying panel data regression to the data tables taken from the Thomson Reuters Eikon database to analyze data on 173 Taiwanese companies that reported ESG from 2009 to 2021. The results show that the relationship between the ratio of independent directors and financial performance has a positive relationship. At the same time, the 3 pillars of ESG also clearly show the intermediary role in this relationship. Finally, this study provides additional insights into the relationship between the ratio of independent directors and financial performance in the Taiwan context.

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

To support the decision making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The results of the different algorithms were compared to each other using the metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

Hospital indebtedness is a complex and very diverse phenomenon. Thus, the goal of this study is the comparison of the financial performance of public hospitals in accordance with their ownership and size. The results of the research lead to the conclusion that the vast majority of public hospitals are indebted, and their ownership structure does not affect their financial condition. What is more, the statistical analysis depicted that large Marshall hospitals are less indebted than poviat-commune ones. In the group of medium-size hospitals, the situation was the opposite. Moreover, the study did not confirm the significant relationship between the size or ownership and the financial status of the hospital. The analysis conducted in the article is aimed at filling in the gap in studies comparing the indebtedness between different types of public hospitals.

While some researchers have suggested that the self-employment (SE) sector is a haven during a financial Crisis, others believe that SE is not necessarily the desired outcome, but an indicator that the labor market is tightening for some groups. Few researchers have compared the SE sector before and after the occurrence of a significant financial Crisis, especially in developed countries. This paper analyzes the determinants of entry into self-employment during the 2008 Spanish Crisis. Using data from the Encuesta de Presupuesto Familiar (EPF), results show that although the rate of SE did not experience a significant change during this time the Crisis affected people differently based on gender, with being females more affected than males. Results also suggest differences between Comunidades Autonomas in how the self-employment sector behaved during the Crisis.

The rapid expansion of Bangladesh's financial infrastructure, coupled with the increasing digitization of banking services, underscores the critical importance of cybersecurity. Machine learning and deep learning methodologies have emerged as potent tools in combating cyber threats such as intrusion, malware, and fraud. By harnessing artificial intelligence and data-driven analysis, these techniques enable real-time scrutiny of vast banking data, facilitating proactive defense against cyberattacks. Bangladesh's evolving financial landscape, characterized by governmental digitalization initiatives and a national financial inclusion strategy, heightens the imperative for robust cybersecurity measures. This paper explores the application of machine learning and deep learning techniques in bolstering banking cybersecurity within the context of Bangladesh's burgeoning digital economy.

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

This research explores the influence of nationality and financial literacy on satisfaction with neobank services among university students. Through a comparative analysis between German and international students, the study unveils that international students exhibit significantly higher satisfaction levels, suggesting that cultural differences and the requirement for international financial services play a crucial role Oishi et al., 1999. Although financial literacy was anticipated to have a strong impact, its effect on satisfaction did not reach statistical significance, indicating a nuanced relationship. This perhaps because the study sample of students partaking in the survey was mostly Graduate students in the field of Economics, Finance and Management. Demographic factors such as gender and age were found to be less influential compared to nationality and financial acumen Gelfand et al., 2011. These findings highlight the importance of tailored financial services and the potential of financial education in enhancing neobank satisfaction Gerrans et al., 2014. The study contributes to the understanding of consumer behavior in digital banking, offering insights for neobanks aiming to improve service adoption and customer loyalty within the university student demographic.

A more sustainable society and economy also implies a more sustainable behavior in the consumption of financial products. A possible change in focus can come from the demand side, so that more sustainable consumption of financial products have to go hand to hand with financial literacy. However, financial literacy, potential favoring of this sustainable behavior, is far from reaching an international consensus about its definition, object and scope. Two objectives are analyzed; the different interpretations of financial literacy in the academic literature, as well as its evolution and how, in what context and with what other concepts the term is used in social networks. Scientometric techniques and content analysis have been used to carry out a systematic review of literature and also NLP to analyze the comments on Social Networks. Critical moments are identified in the definition of financial literacy. Also ten sentiments are analyzed in social networks in wich positivity, trust, and anticipation predominate. Greater attention to this issue is necessary both from the private initiative and from public policies, so that financial literacy is an effective tool for a more sustainable behavior by consumers. Finally, a new definition is proposed based on our findings.

Financial capacity instruments are psychometric tools designed to evaluate individual decisional capacity based on financial decisions. As tests are complex and need special conditions for administration and evaluation, it is difficult to use them in daily geriatric clinics. Our scoping review objective was to evaluate existing financial capacity instruments from the perspective of simplicity and portability. We evaluated one English speaking knowledge database (Medline) using a dedicated MeSH terminology. The review yielded one independent instrument, The Semi-Structured Clinical Interview for Financial Capacity, that can be easy adapted for every-day clinical use. It is simple to understand and perform and do not need trained personnel for administration. It can be finalized in 15 minutes. Initially validated on 261 subjects (with different forms of cognitive impairment), it showed good accuracy and precision mainly in subjects with cognitive impairment. The test is less apt to detect early or fluctuating cognitive impairment. Simplicity, the main advantage of the test, allows gamification fact that increases portability. Familiar images (coins, money) that are used for performing simple tasks does not need complex translation and adaptation. In form of a game, the test is suitable for serial administration, increasing the chance for early capacity reduction detection. Results reflect a physician judgement related to the subjects’ capacity to understand and execute simple financial instructions and not financial proficiency scores. The main limitation of our review is that we investigated only one, English speaking, knowledge database. The scoping strategy generated a financial capacity instrument that can be used in geriatric clinics for early diagnostic of decisional capacity reduction. Further studies are needed to evaluate the reliability and validity of the test in conditions of serial administration and in populations having various financial experience.

This study investigates the pricing of exotic options, specifically Barrier and Asian options, through the application of Monte Carlo simulation. The research commences by establishing the theoretical foundations and closed-form solutions within the Black-Scholes framework. Subsequently, three distinct random walk models are introduced to generate underlying asset paths for simulation purposes. The antithetic variate technique is demonstrated to significantly reduce standard error and accelerate convergence in the pricing of both options, thereby enhancing the precision of Monte Carlo estimates. Notably, the selection of underlying random walk models is found to have a minimal impact on accuracy, suggesting avenues for further research in advanced variance reduction techniques. This study provides valuable insights into the pricing of exotic options, offering implications for finance professionals and researchers, and driving innovation in option pricing methodologies for dynamic financial markets. The research underscores the efficacy of Monte Carlo simulation and variance reduction techniques in pricing complex options, contributing to the advancement of financial modelling and derivatives pricing.

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

We model the financial markets as a game and make predictions using Markov chains estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/hers risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then we estimate the market’s mixed probabilities by defining Markov chains and utilizing its transitions matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. After all, we obtained a model for the financial markets that has an outstanding performance in terms of accuracy and profitability.

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

The effort to expand the U.S. study abroad programs to have a global impact has never been fully expressed by Higher Education Institutions (HEIs) and study abroad administrators. Study Abroad since its inception has not been a truly global platform. This article constructively points out the existing potential of Minority Serving Institutions (MSIs) to make the U.S. study abroad a truly global program capable of defending U.S. national interest in this era of inter-dependence among nations, especially as the global knowledge economy and cultural competence advantage take center stage. Study abroad is at a crossroads at the moment, new ideas must be developed for study abroad to justify the true potential of education abroad. Over two decades, four countries in Western Europe have been hosting the highest number of U.S. students in study abroad. There is no evidence that this trend will reach the entire globe as a reciprocity of international students in U.S. colleges and universities.

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

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

Previous studies have suggested that digitization of finance leads to growth in the economy in several middle- and high-income countries, but research in low- and lower-middle-income countries (LLMICs) remains limited. This research aimed to examine the trends, barriers, relationships, and prospects of finance digitization in LLMICs. This study used analytical and descriptive cross-sectional study designs. Sixty-six countries were purposively selected based on the available data on the Global Findex and the World Development Indicators databases. Descriptive analysis was used to identify the trends in the finance digitization indicators, correlation analysis was used to assess the nature of the association between finance digitization and the economy, and exponential smoothing was used to predict future outcomes in digital finance. The results indicated that the adoption and utilization of finance digitization increased in these countries, but the growth rates varied by region and income group. Low-income countries reported the highest rates of subscriptions to digital finance accounts and storing money in digital accounts, while lower-middle-income countries (LMICs) reported higher rates of inbound/outbound digital transactions, borrowings, and savings using digital channels. Sub-Saharan Africa reported the highest rates of digital finance account subscriptions, digital savings, and digital borrowings, while Europe and Central Asia reported the highest rates of in/out digital transactions. Barriers to digitization in these countries included poor access to the internet, high cost of digital finance products, and lack of a mobile phone, with low-income countries facing more challenges. The analysis revealed a negative significant relationship between finance digitization and Gross National Income growth and a positive significant correlation between finance digitization and other economic growth indicators (gross savings and Gross Domestic Product). Finance digitization was predicted to continue to rise in these countries for the next decade. The study recommended that policymakers should promote the adoption of digital finance and address the barriers and risks of finance digitization in LLMICs, as it had positive effects on economic growth.

The concept of public financial accountability, often associated with account-rendering, lacks a clear and comprehensive definition in the realm of accounting literature. Disentanglement the true essence of a phenomenon is essential, as formal definitions can only provide a superficial understanding. This study seeks to delve into the core principles of public financial accountability and their implications for financial reporting in the public sector. Applying a qualitative approach, data was gathered through in-depth interviews with 25 Nigerian scholars, professionals, and public affairs experts. The analysis reveals that the essence of public financial accountability lies in upholding citisens' trust in public officials, ensuring the responsible management of public financial resources for the greater public good, and effectively communicating financial decisions, actions, and outcomes to the public through a transparent reporting mechanism. This study sheds light on the fundamental nature of financial accountability in the public sector, enhancing our understanding of its significance in governance and financial reporting.

This study investigates the impact of financial integration on international dynamics from the perspective of volatility shocks. To achieve this, I employ an IRBC model with time-varying volatilities, recursive preferences, and a global bank. The model demonstrates that volatility shocks trigger precautionary saving incentives, but the specific effects vary based on the type of shock. In the presence of productivity volatilities, countries with a higher level of financial integration exhibit greater divergence in their business cycles, while financial integration tends to result in more synchronized business cycles in the face of financial volatilities. Disregarding volatility shocks would underestimate the impact of financial integration on the comovement of business cycles across countries. Furthermore, welfare analysis also indicates that financial markets play a crucial role in enhancing social welfare, regardless of the type of volatility.

The demand for renewable energy is increasing globally due to concerns about climate change, pollution, and the finite nature of fossil fuel resources, as renewable energy has been recognized as a significant factor in realizing sustainable development. The government of Saudi Arabia adopted the reduction of fossil fuel subsidies policy as a financial motivation for supporting both the production and consumption of fossil fuels. Therefore, this study aims to investigate the influence and shocks of Saudi’s financial development indicators on renewable energy consumption (REC). And to examine the track of causality between financial development indicators and REC. The study covers the annual data period of 1990-2021 and applies the Basic Vector Autoregressive model (VAR), Granger causality test, forecast error variance decomposition (FEVD), and impulse response function (IRF). The results imply that the financial development indicators have a significant positive impact on REC. The results of causality between REC and financial development indicators were conflicting. The results reveal that REC variation is explained by its innovative shocks and has a positive response to shocks in financial development. Authorities can encourage investment in renewable energy consumption by providing financial incentives also the governments can foster national and international partnerships between investors, policymakers, and industry stakeholders. Employing different determinants of financial development indicators and incorporating population factors in the REC function will be highly recommended for forming the renewable energy demand in Saudi Arabia.

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

This study aims to assess and identify the role of disruptive/digital technologies in financial innovation strategies as part of social innovations at both firm and country level. There are few studies of this type that "cross-examine" technical/social innovative capacity at the firm level vs. the same innovative capacity at the level of the world's major countries. Our proposed study brings some novel elements to the literature on this topic. First, the study synthesizes the factors/variables explaining technical/social innovative capacity as ranked by the GCI (Global Competitiveness Index) and GII (Global Innovation Index) at the country level and then correlates these variables with the factors explaining innovative capacity for the 50 companies in the BCG (Boston Consulting Group) ranking. Second, the study identifies three "driving forces" (digital technologies, managers and the market) as the main variables determining financial innovativeness at firm level. Third, based on the "cross" analysis of the information/data provided by the BCG study vs. the GII and GCI studies, the study suggests some ways to delineate and quantify financial innovation as part of social innovation (e.g., it is argued that 80% of the social innovation achieved annually by a firm relates to the financial relationships engaged by the firm with various categories of stakeholders). Finally, the study is also important from a pragmatic point of view as it suggests/proposes a number of principles that can be considered by managers for building a KM (knowledge management) and continuous innovation strategy. From a theoretical perspective, the study provides a starting point for further research aimed at explaining firm-level financial innovation through the massive use of disruptive technologies.

This study aims to determine the Implication of innovation, proactivity, risk-taking, artistic orientation, and financial literacy on creative economy businesses during the COVID-19 pandemic. This was conducted on 120 creative economy businessmen in Bandar Lampung City, which is a miniature of Indonesia with multiethnic cultures. The results showed innovation is not significant, but proactive attitude, artistic orientation, and financial literacy have a significant implication on the performance of creative economic businesses during the Covid-19 pandemic.

Distortion of financial statements is recognized as one of the most important issues in the field of accounting and auditing, which is also one of the most common issues today. In this regard, the present research was conducted, in which stock exchange information was used to investigate, predict, and model accounting distortions. For this purpose, financial performance, non-financial metrics, market-based metrics and commitment, or selection items were reviewed over a 6-year period. For collecting data of distorting companies, database of the Society of Certified Public Accountants in Iran was used and the information was analyzed using data mining methods (decision tree, neural networks, and Bayesian method). The results showed that analysis of financial statements҆ information has a high accuracy in determining and identifying the distorted financial statements. Using this information, it is possible to get better acquainted with the methods of document distortion and to take necessary measures in order to control and prevent administrative violations at national and international levels. Given frequent occurrence of these violations, artificial intelligence models can be used to identify these papers.

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

Methods of path integrals are used to develop multi-factor probabilities of bid-ask variables for use in high-frequency trading (HFT). Adaptive Simulated Annealing (ASA) is used to fit the nonlinear forms so developed to a day of BitMEX tick data. Maxima algebraic code is used to develop the path integral codes into C codes, and sampling code is used for the fitting process. After these fits, the resultant C code is very fast and useful for forecasting upcoming ask, bid, midprice, etc., when narrow and wide windows of incoming data are used. A bonus is the availability of canonical momenta indicators (CMI) useful to forecast direction and strengths of these variables.

Based on the seminal paper of Case, Quigley and Shiller (2013), we investigate the effects of financial and housing wealth on consumption. Using quarterly data from 1975 to 2016, for all States of U.S. economy, and a different methodology in measuring wealth, we report relatively greater financial effects than housing effects on consumption. Specifically, in our basic utilized model, the calculated elasticity for financial wealth is 0.060, while for housing is 0.045. The results are not in agreement with the ones obtained by Case, Quigley and Shiller. In an attempt to investigate the disparity we proceed by incorporating the introduction of the Tax Reform Act in 1986, which increased incentives for owner-occupied housing investments. Finally, due to distributional factors at work, and taking into account the pronounced uneven distribution of wealth we investigate the effects of wealth for 8 states that include the Metropolitan areas comprising of the well known Case-Shiller 10-City Composite Index. Now the housing effect on consumption is much stronger and larger than the financial effect. Additionally, we forecast the consumption changes at the time of the high rise and large drops in house prices for these states. Forecasts showed a recession from the fall of Lehman Brothers until the fourth quarter of 2011. These forecasts were not verified. Probably, the new techniques used by politics played an important role. We also find that extreme behaviors cannot be predicted.

Considering the impact of religiosity on the perceptions regarding life quality, in this paper we focus on the effects of the appurtenance to a religion on the standard of living in several economically developing countries (Turkey, Ukraine, Senegal and Morocco). The data have been collected using a survey carried out in 2012 and the empirical analysis was based on non-parametric tests and multinomial logistic regression. The results indicate there are differences between religious persons and atheists regarding gender, marital status, perceptions of daily life and standards of living. Females and officially married people or single people tend to be more religious. A person claiming to belong to a religion has 2-4-fold more chances to achieve a considerable improvement in the standards of living as compared to an atheist. Moreover, religious people from the analyzed countries are more optimistic about their life overall. Taking into account the sample’s characteristics and the countries chosen, we can claim that the results obtained are truly cross-cultural in nature. Moreover, most of the conclusions reached would be to some extent relevant to other developing economies of Eastern Europe, North Africa and Middle East.

This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.

Class Incremental Learning (CIL) has emerged as a vital machine learning paradigm, particularly suited for environments with continuous data streams, such as those encountered in the financial industry. CIL enables models to progressively acquire and accumulate new knowledge without retraining the entire dataset, making it highly efficient for dynamic financial markets. In this paper, we explore the application of CIL to financial data streams, where high-frequency, high-dimensional data, such as stock prices and cryptocurrency transactions, evolve rapidly. The challenges inherent in CIL, including catastrophic forgetting—where the model loses previously learned knowledge—and intransigence—where it resists learning new classes—are addressed through both regularization-based techniques and dynamic architecture expansion. We present a comprehensive analysis of the algorithms designed to mitigate these challenges and highlight their relevance in financial applications like algorithmic trading, portfolio management, risk assessment, and fraud detection. By demonstrating how CIL enables models to adapt to new market conditions and asset classes while retaining past knowledge, we illustrate its critical role in advancing real-time financial decision-making. Through a detailed examination of the methodologies and their applications, this paper aims to provide insights into the future of adaptive financial models using Class Incremental Learning. We have also summarized methodologies into a model optimization-based class and a data focused class.

Performance analysis of companies is critical for decision-makers in the financial sector, particularly when resources are limited. This study investigates the properties of various Multi-Criteria Decision-Making (MCDM) methods, critically comparing their results in evaluating the performance of consumer goods companies listed on the Nigerian Stock Exchange (NSE). The analysis utilizes financial ratios, with weights derived using Buckley's Column Geometric Mean Method, a fuzzy ranking approach. The study concludes by comparing TOPSIS, VIKOR, and ELECTRE methods, determining the final rankings of these companies, and interpreting these rankings based on financial ratios over a five-year period (2016-2020).