Field Of Finance Research Articles

The Application of Service Ijarah in Islamic Finance

This research aims to explore the current application of Service Ijarah in Islamic Finance across three major sectors of Islamic commercial finance: Islamic banking, Islamic Insurance, and Islamic capital market. Furthermore, this study identifies potential challenges and risks associated with the use of Service Ijarah by comparing it with alternative Shariah contracts. This research employs a qualitative desk review research method to delineate the practical application of the Service Ijarah. The data was primarily collected from the official websites, product disclosure sheets, and annual reports of Islamic finance and banking institutions in different jurisdictions. Additionally, other secondary sources were used, including Shariah Standards, policy documents, journal articles, and other forms of reports. At least 15 Islamic banks globally have used the contract for financing services such as education, marriage, travel, home renovation, green technology, professional services, and Hajj & Umrah in countries including Oman, UAE, Malaysia, Maldives, Bangladesh, Bahrain, Tanzania, and Nigeria. The contract has yet to be practically applied within the Islamic capital market and Islamic insurance (Takaful). However, there is potential for the structuring of service-based Ijarah Sukuk and the development of Takaful products to cover service-ijarah-based financings. The absence of guidelines on Service Ijarah and the difficulty of finding reliable service providers for custom jobs may present challenges for the banks. This research is expected to be beneficial for industry practitioners seeking to explore a contract that remains relatively underutilised in the Islamic finance field and Shariah and regulatory authorities. It also examines the potential for service-based Ijarah Sukuk and AAOIFI's role in facilitating the development of Service Ijarah-based products.

The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability.

Pengaruh Pendapatan Asli Daerah, Dana Alokasi Khusus dan Dana Alokasi Umum Terhadap Pengalokasian Anggaran Belanja Modal Di Pulau Jawa

This research aims to analyze the influence of Regional Original Income (PAD), Special Allocation Funds (DAK), and General Allocation Funds (DAU) on the allocation of capital expenditure budgets on the island of Java. Capital expenditure is an important component in regional budgets that contributes directly to infrastructure development and improving public services. This study uses panel data from districts/cities on Java Island during the 2015-2020 period. The analytical method used is panel data regression with a fixed effects model (FEM) approach to identify the influence of independent variables on the dependent variable. The research results show that PAD has a positive and significant influence on capital expenditure. This indicates that regions with higher PAD tend to allocate more budget for capital expenditure. Meanwhile, DAK also has a positive and significant effect on capital expenditure, which shows that funds specifically allocated by the central government have succeeded in increasing investment in fixed assets in the regions. On the other hand, DAU was found to have a negative but insignificant effect on capital expenditure, indicating that general funds received from the central government did not directly encourage an increase in capital expenditure. This research provides important implications for regional policy makers in increasing PAD and utilizing transfer funds from the central government more effectively to accelerate the development of infrastructure and public services. Apart from that, the results of this research can also be a reference for further research in the field of regional finance and public budget management.

Role Transformation of National Sharia Board-Indonesian Ulama Council (DSN-MUI) in the Dynamics of Fatwa Determination in the Modern Era

The role transformation of the National Sharia Board Dewan Syariah Nasional (DSN); one of the organizational structures in Indonesian Ulama Council Majelis Ulama Indonesia (MUI) in the dynamics of fatwa determination in the modern era is an important topic to be studied in the context of rapid social, economic, and technological change. This article aims to analyze how DSN-MUI adapts to the challenges of modern times and develops a methodology for setting fatwas that is relevant and responsive to the needs of contemporary society. Through a comprehensive literature review, this article highlights the evolution of the DSN's function from the establishment of conventional fatwas towards a more dynamic and inclusive approach, including in the field of sharia finance, halal products, and other social problems. This transformation reflects DSN-MUI's efforts to remain relevant and provide accurate and applicable guidance for Muslims in Indonesia in facing modern issues.

Credit Risk Management and Financial Performance of Listed Commercial Banks in Kenya

Commercial banks are concerned with the provision of credit facilities in form of loans and advances to customers. These loans and advances are expected to be repaid by customers in line with the agreement reached with their bankers. Customers’ default in the repayment of loans and advances at the agreed period may lead to bad and doubtful debts and this can affect the financial health, profitability and going concern status of the bank. This study empirically explored the effect of credit risk management on the financial performance of nine listed commercial banks in Kenya for the period, 2018-2022. Credit risk management as the independent variable, was surrogated by two parameters- loans to deposit ratio and Non-performing Loans ratio. Return on Assets was used as a proxy for financial performance of the listed commercial banks in Kenya. The study utilized the information asymmetry theory and the theory of moral hazard and adverse selection. A correlational research design, using secondary data derived from listed commercial banks’ annual audited reports and information from Nairobi Securities Exchange was used in this study. The target population of the study was the nine listed commercial banks in Kenya. A pilot study was carried by performing the normality and multi-collinearity tests from the sample data collected for purposes of assessing the reliability and validity of the model. A census method was adopted in data collection since the target population was manageable. Collected data was analyzed using the Statistical Package for Social Sciences Version 28 where both descriptive and inferential statistical methods were used to describe and depict the results (Mean, Standard Deviation, Correlation, Regression and Analysis of variances). The findings of the study revealed a statistically significant regression effect and indicative of accomplished prediction of financial performance of the listed commercial banks through the F-calculated value which showed that the model was significant. Loan to deposit ratio explained .4% (r =.280) of financial performance of listed commercial banks in Kenya and non-performing loans ratio -36.5% (r=-.681) of financial performance of the listed commercial banks in Kenya. The study will be expected to add to the existing literature in the finance field and more importantly, the credit risk management in commercial banks and other related sectors. The study recommended that; management balance the ratio of financing between equity, debt and customer deposits to extend lending capacity, and generate high earnings volume; desist from holding capital without investing for improved financial achievement; management should maintain a moderate level of Loans and advances to deposit ratio since it is a measure of the banks’ ability to survive withdrawals, because higher rates lead to liquidity problems; give credit-risk management prominence in their strategic policies and finally, due to the continuous deterioration and accumulation of the nonperforming loans, management ought to strengthen loan recovery measures. Keywords: Credit Risk Management, Financial Performance, Listed Banks, Equity Multiplier Ratio, Capital Adequacy Ratio, Nonperforming Loans

Business Financing and Financial Performance of Small and Medium Enterprises in Turkana County, Kenya

The purpose of this study was to examine the effect of business financing on the financial performance of small and medium enterprises in Turkana County, Kenya. Specifically, the study: examined the effect of commercial loan financing on the financial performance of small and medium enterprises; established the effect of trade credit financing on the financial performance of small and medium enterprises in Turkana County. A descriptive survey research design was used. The study was anchored on the information asymmetry theory and the financial intermediation theory. The target population comprised 325 small and medium enterprises in Turkana County. Stratified random sampling was used to select 83 small and medium enterprises for the study. The study used questionnaires as survey instruments for data collection which were administered to group chairpersons/ managers. Analysis of data was done using descriptive and inferential statistics. The study findings revealed the existence of a statistically significant regression effect of independent variables on financial performance of Small and Medium Enterprises in Turkana County. The study employed the F-distribution (F = 32.980) in predicting the dependent variable which indicated that the model significantly predicted how the independent variables affected the dependent variable (Financial Performance of Small and Medium Enterprises in Turkana County) which indicated an overall statistically significant model with commercial loan financing 54.9%, (t~3.056) of financial performance; and trade credit financing 67.2% (t~4.381) of financial performance of small and medium enterprises in Turkana County, Kenya. The study further recommended on each of the variables for management and policy consideration in future decision-making processes. The findings were expected to be of great assistance to management of other businesses. The study was also expected to add to existing literature in the finance field. Keywords: Business Financing, Financial Performance, Commercial Loan Financing, Trade Credit Financing

Research on stock trend prediction based on grey wolf optimized RF and SVM models

The volatility of the stock market has a profound impact on the stability of the financial market, and then has a significant effect on the overall economic situation, so the accurate prediction of stock trend has always been the focus of research in the financial field. In this paper, the raw minute-level stock data of SSE 50 and CSI 300 in 2021 are normalized, and the historical training and test sets of daily stocks for model training and testing are established based on the datasets. The RF-SVM model based on the gray wolf optimization algorithm is constructed to compare the traditional support vector machine and random forest model, and the relevant performance indexes are calculated. In this study, the RF-SVM model based on gray wolf optimization is highly correlated with the characteristics of stock data, and the prediction accuracy of stock index data is more than 85%. The cross-validation score is over 0.77 and the overall weighted score is as high as 0.98. Compared with the SVM and random forest models under the default parameters, the RF-SVM model based on gray wolf optimization has an average advantage of 25% in performance indicators. The generalization ability is increased by about 26%; The fitting error was reduced by about 51.15%; The overall average score advantage is about 34%. These results prove that the RF-SVM model of gray wolf optimization has superior performance and significant optimization effect in stock prediction, and compared with the traditional grid search optimization algorithm, gray wolf optimization has excellent adaptability and can gradually obtain the global optimal parameter configuration.

A Global Review Of Tax Morale: A Bibliometric Analysis For Future Research

This research aims to analyze the development and direction of research on tax morality globally, so that current and future publication trends can be identified. The issue of tax morale is interesting to study in more depth, because it supports increasing tax compliance and optimizing state revenues. This research uses a bibliometric approach, analysis via VOSviewer and Ms.excell, and is sourced from Scopus metadata from 2002 to 2024 with 248 publications. The results of the global analysis show that research on tax morality has increased significantly in the last four years in the form of English language journal articles, the subject area of which is mostly in the fields of economics and finance. This research is growing rapidly in Europe and America if we look at the composition of authors and their affiliations. Based on network analysis, the topic of tax morale is divided into five large clusters, namely: informal sector, tax compliance, tax system, tax evasion, and trust. Future research topics that can be further developed related to tax morality are related to social norms and tax reform. Meanwhile, keywords that researchers can link in the future are tax compliance with entrepreneurship and institutional theory, as well as tax avoidance with tax knowledge, social norms, corruption and trust. The limitation of this research is that it only comes from the Scopus database and the keywords are only focused on the word "tax morale".

How oil price uncertainty influences corporate capital structure: evidence from BRIC countries

ABSTRACT This paper examines how corporate capital structures in emerging markets – Brazil, Russia, India, and China (BRIC) – were impacted by oil price uncertainty (OPU) from 2007 to 2019. We find that the relationship between OPU and firm leverage in BRIC markets is nonlinear and U-shaped. The extent to which OPU affects both short- and long-term leverage varies across nations. These findings are robust to endogeneity tests. Furthermore, while the OPU effect on corporate leverage is lessened in high-growth enterprises, it is strengthened in materials and energy companies. From an OPU perspective, this paper contributes intriguing knowledge of the corporate finance field.

THE IMPACT OF ARTIFICIAL INTELLIGENCE RECOMMENDATIONS ON INDIVIDUAL INVESTOR DECISIONS

Purpose- Although artificial intelligence technology is a new technology, it affects every aspect of our lives by finding a very fast field of activity. Artificial intelligence technology, which also shows its effect in the field of finance, is seen to have many applications. There are many alternatives in the investment markets, it will take a long time to make a profit in the markets and a certain amount of knowledge is required. People cannot master the data in all markets when they will invest, but technological developments provide the opportunity to invest by storing each data or observing their changes. This study aims to investigate the effects of artificial intelligence technology on individual investor decisions. Methodology- The study consists of individual investors who live in Diyarbakır province and generally make investments. The questionnaire prepared in accordance with the scope of the study was applied to 1800 participants using face-to-face survey method. The 22 statements prepared in accordance with the scope of the study were applied to the participants. The questionnaires that 1616 participants answered correctly and accurately were included in the scope of the study. In order to ensure the reliability of the study statements, Cronbach's Alpha was calculated and this rate was determined as 91%. The answers given to the study statements were transferred to the tables as a result of the analyses. The transferred information was tried to be interpreted. In addition, frequency tables, t-test and anaova analysis were used in the analysis of the study data. Findings- Thanks to artificial intelligence algorithms, which is one of this technology, it analyses the data in the market and enables the investor who wants to invest to trade in the market by giving buy and sell orders. Thus, artificial intelligence technology allows the investor to make more profitable investments by guiding the investor. Conclusion- As a result, it is possible to say that the individual investors participating in the research do not have sufficient knowledge about artificial intelligence technologies, but they have an interest in investing using artificial intelligence technologies. In addition, it has been determined that the older the age, the lower the education level, the higher the income level and the married investors are insecure about investing using artificial intelligence technology.

Public Financial Control – Financial Policy Instrument of the State

Abstract The main objective of this paper is to provide an objective approach, starting from the current legislation, on the financial control system and the extent to which it can be an instrument of financial policy. At the same time, this paper captures the structure of the financial control system in Romania with brief considerations of the forms of financial control that make it up, starting from the community acquis in the field of financial control. In the current economic-social context, the existence of financial control standards is required, which are guaranteed by the Romanian state through the creation of a set of laws that govern this field. The pragmatic approach to financial control is vital for establishing its objective, based on specific management methods and techniques, and with the help of which the consolidation of order, discipline, the spirit of responsibility, as well as the efficient management of the economic and financial resources managed by the entities can be achieved public. Financial control must ensure the good functioning of all activities in the economic sphere and is called to act systematically in order to identify or prevent possible deviations and deficiencies, increase the efficiency in the use of resources, preserve the integrity of the heritage and, last but not least, in understanding the way in which legality is respected in the economic and financial field.

Analysis of Financial Data Issues in Real Estate Enterprises During the Era of Big Data

In the era of big data, financial data management has become an indispensable part of modern enterprise management for real estate enterprises, characterized by diverse sources, formats, quality, and security. By providing precise data analysis, it helps companies grasp the financial context and leads them towards more scientific and efficient management paths. In the future, as technology continues to advance, big data will play a greater role in the financial field, driving enterprises to achieve continuous innovation and development. To excel in the financial management of real estate enterprises in the big data era, it is essential to cultivate advanced data analysis talents, adopt new analytical tools, enhance the application of results, and fully leverage the advantages of big data.

Experimental Finance: Introduction to the Special Issue in the QJF

We delineate the field of experimental finance. This is done along two dimensions. To delineate the methodological dimension, we use the accepted view in the community. To delineate the thematic (research questions) dimension, we use a comparison with experimental economics with a historical perspective and a brief bibliometric analysis. The article further introduces, motivates, and provides context to the articles published in the special issue. These articles also serve to illustrate the delineation of the field proposed in the first part.

Completeness of provided information as a key aspect of successful counterparty verification completeness of provided information as a key aspect of successful verification of counterparty for its financial stability and reliability

The article presents the results of the analysis of Russian and foreign information and analytical systems conducted by the author of this article in the context of their methods and volumes of information collection. As a result of the study, a conclusion was made about the completeness and nature of the information provided to the user by information and analytical systems, as well as current shortcomings were highlighted and features that require further improvement and efficiency improvement in the use of such tools in the field of business and finance were emphasized.

Unveiling interest rates and Islamic banking activity: empirical evidence from Saudi banks

Purpose The purpose of this paper was to explore the impact of interest rate shocks on the deposits and financing of Islamic and conventional banks in Saudi Arabia. Design/methodology/approach The authors use impulse response functions (IRFs) and variance decomposition (VDC) analyses over the period 2008Q1–2020Q2. Findings The IRFs showed that increasing interest rates reduce loans and conventional deposits. For Islamic banks, the deposits are more affected by interest rate changes than the financing. The VDC analysis found that deposits contribute up to 61% of Islamic financing variations, compared to only 25% in conventional lending ones. Originality/value This research contributes to the field of Islamic economics and finance by providing empirical evidence on how interest rates likely impact Islamic and conventional deposits and financing in Saudi banking system.

MODELLING STOCK PRICES OF A BANK WITH EXTREME VALUE DISTRIBUTIONS

The study investigates the application of Extreme Value Theory in modelling stock prices, aiming to capture the tail behaviour and extreme movements that conventional distributions often fail to represent accurately. The use of Extreme Value Theory has gained considerable attention in the field of finance due to its ability to model rare events, such as financial crises or market crashes. By incorporating Extreme Value Theory, researchers aim to improve risk management, portfolio optimization, and pricing of financial derivatives. In this study, the Log-normal, Weibull, Gamma, and Normal distributions were used to model the stock price closing data, with a specific focus on extreme value distributions. Both graphical explorations and goodness-of-fit criteria were considered together to evaluate the suitability of these distributions. When assessing the data, it was observed that the Weibull distribution provided the best fit for the given stock price closing data.

Entrepreneurship Education Structure and SMEs Performance amongst Managers in Port Harcourt Metropolis, Rivers State, Nigeria

The importance of entrepreneurial education skills in supporting countries’ economic growth and development has been recognized by experts in the field of accounting, finance, sociology and economics. Today, the issue of entrepreneurship skills is becoming so increasingly important that it has turned into a priority for developing countries including Nigeria. This study empirically explores the relationship between entrepreneurship education skills and venture performance amongst managers of small and medium scale firm in Port Harcourt metropolis. Descriptive survey design was adopted and correctly completed 400 sets of questionnaire were used. Series of statistical analysis were applied including mean, aggregate mean, standard deviation, and Spearman rank correlation with the aid of statistical package for social sciences, version 25. The empirical results indicate that psychomotor education significantly relates to profitability and market share. We therefore conclude that entrepreneurship education structure relates to small and medium enterprises performance amongst managers in Port Harcourt Metropolis, Rivers State, Nigeria, and recommend that policy makers should consider introducing entrepreneurship in primary and secondary schools through integration to other subjects or as a stand-alone subject. This could function as an introductory stage which will prepare students for more advanced entrepreneurship education structure in the small. Policy makers should also create conducive entrepreneurship education centers within the business eco-system to enable traders attend free workshops when scheduled. Traders can be pre-informed about the benefits of attending/participating in business workshops through social media before slating any.

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

Green bonds and environmental performance: The effect of management attention

AbstractGreen bonds have attracted significant interest in the business and financial sectors, yet the environmental performance outcomes among issuing firms have been uneven. Drawing on the attention‐based view framework, this study examines the role of corporate green bond issuance in directing attention to environmental issues across organizational levels. Additionally, we investigate how internal conditions, such as growth, profitability, and indebtedness, influence the translation of attention into improved environmental performance. Using a matched sample of 160 paired firms from 23 countries and nine sectors, our results emphasize the significance of both green bond intensity and internal conditions in shaping how green bonds impact environmental performance. These results contribute to the fields of sustainable finance and attention‐based view theory, offering new insights into the influence of green bonds on firms' environmental performance.

STUDY ON IMPACT OF INTEREST RATES ON THE STOCK MARKET

This study set out to investigate and focus on the effect of interest rates on stock markets, namely the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE), with the aim of increasing knowledge. All of the secondary data included in this study came from reliable exchanges and was collected over a five-year period between 2019 and 2023. The Reserve Bank of India [RBI], which collects daily and monthly prices of several stock indexes, was another source of interest data. The Unit Root test, Johansen’s Co-integration test, and ARCH models were examined using the statistical program EViews 12, were employed to calculate the outcomes and determine whether there was a long-term positive or negative association between the variables. For this study, interest rates such as bank rate, repo rate, and reverse repo rate are measured as independent variables, and stock prices such as BSE Bank, and NSE Bank are measured as dependent variables. This study will be useful to investors, financial managers, and other experts in the field of finance.