Journal Description
International Journal of Financial Studies
International Journal of Financial Studies
is an international, peer-reviewed, scholarly open access journal on financial market, instruments, policy, and management research published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: CiteScore - Q2 (Finance)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 28.1 days after submission; acceptance to publication is undertaken in 5.4 days (median values for papers published in this journal in the first half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.3 (2022);
5-Year Impact Factor:
2.1 (2022)
Latest Articles
Sentiments Extracted from News and Stock Market Reactions in Vietnam
Int. J. Financial Stud. 2023, 11(3), 101; https://doi.org/10.3390/ijfs11030101 - 07 Aug 2023
Abstract
News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT,
[...] Read more.
News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors are concerned with the daily news by testing the movements of the market before and after the news is released. The results of the analysis show that there is an insignificant difference in the stock price as a response to the news. However, negative news sentiments can alter the variance of market returns.
Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
►
Show Figures
Open AccessArticle
The Effect of Capital Structure on Firm Value: A Study of Companies Listed on the Vietnamese Stock Market
Int. J. Financial Stud. 2023, 11(3), 100; https://doi.org/10.3390/ijfs11030100 - 04 Aug 2023
Abstract
This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods,
[...] Read more.
This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods, such as ordinary least squares (OLS), fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS), the impact of capital structure on key financial indicators, namely, return on assets (ROA), return on equity (ROE), and Tobin’s Q, is assessed. The findings indicate that the debt ratio exhibits a positive influence on ROA, ROE, and Tobin’s Q, with Tobin’s Q displaying the most pronounced impact (0.450) and ROA showing the weakest impact (0.011). However, the long-term debt ratio does not significantly affect firm value. Interestingly, both short-term and long-term debt ratios have negative effects on ROA, ROE, and Tobin’s Q, with the most substantial impact on Tobin’s Q reduction (0.562). Based on these research outcomes, the authors offer valuable recommendations to companies, investors, business leaders, and policymakers to make informed decisions in selecting an optimal and sensible capital structure.
Full article
Open AccessArticle
Uncovering the Effect of News Signals on Daily Stock Market Performance: An Econometric Analysis
Int. J. Financial Stud. 2023, 11(3), 99; https://doi.org/10.3390/ijfs11030099 - 04 Aug 2023
Abstract
The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods,
[...] Read more.
The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal that political and global news has a significant impact on KSE-100 index. Blue chip stocks are considered safer investments, while short-term panic responses often overshadow rational decision-making in the stock market. Investors tend to quickly react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting stock market fluctuations compared to the simple OLS method.
Full article
(This article belongs to the Special Issue Macroeconomic and Financial Markets)
►▼
Show Figures
Figure 1
Open AccessArticle
The Changing Landscape of Financial Credit Risk Models
by
and
Int. J. Financial Stud. 2023, 11(3), 98; https://doi.org/10.3390/ijfs11030098 - 04 Aug 2023
Abstract
►▼
Show Figures
The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it
[...] Read more.
The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it becomes necessary to understand how these techniques work and how they can be applied. The second factor is financial crises. Where predictive models view the future as a reflection of the past, financial crises can violate this assumption. This creates a new field of research on how to adjust predictive models to incorporate forward-looking conditions, which include future expected financial crises. The third factor considers the impact of financial technology (Fintech) on the future of predictive modelling. Fintech creates new applications for predictive modelling and therefore broadens the possibilities in the financial predictive modelling field. This changing landscape causes some challenges but also creates a wealth of opportunities. One way of exploiting these opportunities and managing the associated risks is via industry collaboration. Academics should join hands with industry to create industry-focused training and industry-focused research. In summary, this study made three novel contributions to the field of financial credit risk models. Firstly, it conducts an investigation and provides a comprehensive discussion on three factors that contribute to rapid changes in the credit risk predictive models’ landscape. Secondly, it presents a unique discussion of the challenges and opportunities arising from these factors. Lastly, it proposes an innovative solution, specifically collaboration between academic and industry partners, to effectively manage the challenges and take advantage of the opportunities for mutual benefits.
Full article
Figure 1
Open AccessArticle
Impact of Liquidity and Investors Sentiment on Herd Behavior in Cryptocurrency Market
Int. J. Financial Stud. 2023, 11(3), 97; https://doi.org/10.3390/ijfs11030097 - 31 Jul 2023
Abstract
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of
[...] Read more.
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.
Full article
Open AccessArticle
Opening a New Era with Machine Learning in Financial Services? Forecasting Corporate Credit Ratings Based on Annual Financial Statements
by
and
Int. J. Financial Stud. 2023, 11(3), 96; https://doi.org/10.3390/ijfs11030096 - 30 Jul 2023
Abstract
Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the
[...] Read more.
Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the assessment of companies’ financial situations using annual statements. Particularly, it is necessary to check whether these ML models achieve better results compared to statistical methods. Due to the multi-class classification problem when forecasting corporate credit ratings, the development, monitoring, and maintenance of ML-based systems are more challenging compared to simple classifications. This problem becomes even more complex due to the required coordination with financial regulators (e.g., OECD, EBA, BaFin, etc.). Furthermore, the ML models must be updated regularly due to the periodic nature of annual statements as a dataset. To address the problem of the limited dataset, multiple sampling strategies and machine learning algorithms can be combined for accurate and up-to-date forecasting of credit ratings. This paper provides various implications for ML-based forecasting of credit ratings and presents an approach for combining sampling strategies and ML techniques. It also provides design recommendations for ML-based services in the finance industry on how to fulfill the existing regulations.
Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
►▼
Show Figures
Figure 1
Open AccessArticle
Do Share Repurchases Crowd Out Internal Investment in South Africa?
by
and
Int. J. Financial Stud. 2023, 11(3), 95; https://doi.org/10.3390/ijfs11030095 - 27 Jul 2023
Abstract
Researchers in developed countries have questioned whether share repurchase activity influences internal investment. The aim of this study was to investigate the relationship between share repurchases and internal investment (defined as capital expenditure, employment expenditure, and research and development) in South Africa, as
[...] Read more.
Researchers in developed countries have questioned whether share repurchase activity influences internal investment. The aim of this study was to investigate the relationship between share repurchases and internal investment (defined as capital expenditure, employment expenditure, and research and development) in South Africa, as little was known about this relationship in developing countries. A quantitative research methodology was followed, employing the data of South African listed companies during the 2002–2017 period. A significant negative relationship was noted between share repurchases and employment expenditure when considering all companies, while high-growth companies exhibited a significant negative relationship between share repurchases and capital expenditure. The negative relationships could indicate that companies increase share repurchases to the detriment of internal investment (especially employment). Alternatively, it may imply that share repurchase and internal investment decisions are determined simultaneously, with companies decreasing internal investment and increasing share repurchases in the absence of identifiable profitable projects (or increasing internal investment and decreasing share repurchases when growth opportunities are available). These findings could be useful to shareholders, corporate governance regulators and activists. Given the high unemployment and income inequality in South Africa, the results support a call for the improved regulation of share repurchases to ensure effective monitoring.
Full article
Open AccessReview
Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications
by
, , , , and
Int. J. Financial Stud. 2023, 11(3), 94; https://doi.org/10.3390/ijfs11030094 - 26 Jul 2023
Abstract
►▼
Show Figures
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of
[...] Read more.
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.
Full article
Figure 1
Open AccessReview
The Sustainability of Investing in Cryptocurrencies: A Bibliometric Analysis of Research Trends
Int. J. Financial Stud. 2023, 11(3), 93; https://doi.org/10.3390/ijfs11030093 - 25 Jul 2023
Abstract
This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular
[...] Read more.
This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular subjects and research trends. Between 2014 and 2021, 1442 papers on cryptocurrencies were published in the Web of Science core collection, the most authoritative database, although only a tiny percentage evaluated ESG factors. One of the most common criticisms of cryptocurrencies is the pollution derived from energy consumption in their mining process and their use for illicit purposes due to the absence of effective regulation. The study allows us to suggest future research directions that may be beneficial in illustrating the environmental effect, studying financial behavior, identifying the long-term sustainability of cryptocurrencies, and evaluating their financial success. This study provides an in-depth examination of current research trends in the field of cryptocurrencies, identifying prospective future research directions.
Full article
(This article belongs to the Special Issue Digital and Conventional Assets)
►▼
Show Figures
Figure 1
Open AccessReview
Board Compensation in Financial Sectors: A Systematic Review of Twenty-Four Years of Research
Int. J. Financial Stud. 2023, 11(3), 92; https://doi.org/10.3390/ijfs11030092 - 24 Jul 2023
Abstract
►▼
Show Figures
We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments
[...] Read more.
We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments over time are evaluated. With the identification of a final sample of 87 research papers indexed in Scopus, we identify research gaps to provide insight into future research following a systematic method. The results revealed that the United States of America received the broadest research interest, along with cross-country research. While the literature lacked to provide investigations for other countries of the world. Although the effect of compensation on organizational outcomes (performance and grow) is still unclear in the literature, several factors have been introduced as key drivers of the compensation, including the country’s level of development, the development of equity markets, the development of banking system, its dependence on foreign capital, collective rights empowering labor, the strength of a country’s welfare institutions, employment market forces, and social order and authority relations. On a theoretical level, agency theory has been most popular in the literature, along with providing multiple theoretical frameworks with agency theory as a slack resources theory, managerial talent theory, and managerial power theory. This is the first research to our knowledge that used a systematic review (SR) of literature to give a complete and comprehensive evaluation of the literature on board compensation in the financial sector. The current study documents the flow of literature on the board’s compensation in the financial sectors over 24 years and establishes future research opportunities.
Full article
Figure 1
Open AccessArticle
The Market’s View on Accounting Classifications for Asset Securitizations
by
Int. J. Financial Stud. 2023, 11(3), 91; https://doi.org/10.3390/ijfs11030091 - 11 Jul 2023
Abstract
Prior research has examined how investors view asset securitizations, and shows that investors treat securitizations as borrowings, even when GAAP treats them as sales. Upon the adoption of two new accounting standards relating to securitizations, some off-balance-sheet securitized assets were consolidated back onto
[...] Read more.
Prior research has examined how investors view asset securitizations, and shows that investors treat securitizations as borrowings, even when GAAP treats them as sales. Upon the adoption of two new accounting standards relating to securitizations, some off-balance-sheet securitized assets were consolidated back onto firms’ balance sheets. This study investigated how investors viewed assets that firms consolidated under the new standards and those that firms left unconsolidated. I found that investors differentiated between these two types of securitizations, treating the consolidated assets as borrowings and the unconsolidated assets as sales. I conclude that the new accounting standards are more consistent with equity investors’ views of securitizations. I also found that, for the consolidated assets, investors did not distinguish between securitizations going through two different accounting structures. Lastly, this study provides evidence on one information channel that investors use to distinguish between securitizations that may have the economic substance of borrowings versus sales.
Full article
Open AccessArticle
Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust
Int. J. Financial Stud. 2023, 11(3), 90; https://doi.org/10.3390/ijfs11030090 - 10 Jul 2023
Abstract
This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership,
[...] Read more.
This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined.
Full article
(This article belongs to the Special Issue Literature Reviews in Finance)
►▼
Show Figures
Figure 1
Open AccessArticle
The Retained Earnings Effect on the Firm’s Market Value: Evidence from Jordan
Int. J. Financial Stud. 2023, 11(3), 89; https://doi.org/10.3390/ijfs11030089 - 04 Jul 2023
Abstract
The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms
[...] Read more.
The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms in the Jordanian context using unbalanced panel data analysis for a sample of 2281 firm years covering the period from 2010 to 2021. The results of the pooled sample indicated a strong positive significant effect for dividends per share. However, the retention per share indicated a negative significant effect on the firm’s market value. The other robustness analysis for the two sub-samples and the financial and non-financial sub-samples indicated the same results, consistent with the pooled sample for the two main explanatory variables.
Full article
Open AccessArticle
Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data
Int. J. Financial Stud. 2023, 11(3), 88; https://doi.org/10.3390/ijfs11030088 - 03 Jul 2023
Abstract
Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step.
[...] Read more.
Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly.
Full article
(This article belongs to the Special Issue Digital and Conventional Assets)
►▼
Show Figures
Figure 1
Open AccessArticle
Can Digital Financial Inclusion Promote Women’s Labor Force Participation? Microlevel Evidence from Africa
by
and
Int. J. Financial Stud. 2023, 11(3), 87; https://doi.org/10.3390/ijfs11030087 - 03 Jul 2023
Abstract
Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from
[...] Read more.
Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from the Global Findex database, 2021 edition, based on nationally representative surveys of 29 African countries. The Probit model estimation methodology is used to examine the empirical results. Our findings reveal that financial inclusion via the digital channel is positively associated with women’s labor force participation more than the traditional channel. A significant and positive impact of formal financial services channels on the level of women’s participation in the labor market was uncovered. Our research has shown that women face a variety of obstacles when it comes to accessing financial services, both through traditional channels and digital means. These barriers include nonvoluntary obstacles in traditional financial inclusion channels. However, as a woman’s income level increases, the intensity of these barriers decreases. When it comes to digital financial inclusion, women often face a unique set of obstacles, such as the high cost of mobile financial services, lack of money, and lack of access to a cellphone. The study contributes to the existing literature by investigating the impact of digital financial inclusion on women’s labor force participation in African countries and identifying barriers that hinder women’s digital financial inclusion based on individual-level data. It suggests that African policymakers should increase women’s financial inclusion through digital channels to improve their participation in the labor market.
Full article
(This article belongs to the Special Issue Digital Financial Inclusion)
Open AccessArticle
The Role of Social Banking in the Success and Sustainable Business Continuity of SSMEs
by
, , , and
Int. J. Financial Stud. 2023, 11(3), 86; https://doi.org/10.3390/ijfs11030086 - 28 Jun 2023
Abstract
►▼
Show Figures
The technological developments in the social economy have significant implications for social banks and are optimistically changing the way social retail banks conduct their business. Social banks can invest in social services for small- and medium-sized enterprises (SSMEs) either to acquire a strategic
[...] Read more.
The technological developments in the social economy have significant implications for social banks and are optimistically changing the way social retail banks conduct their business. Social banks can invest in social services for small- and medium-sized enterprises (SSMEs) either to acquire a strategic advantage or out of strategic necessity. With the assistance of a mathematical model, this study tries to identify SME service channels and assess potential impacts on social deposit banks’ performance. In the first stage, the proposed model estimates the predictive capacity of interpretive accounting variables (financial ratios) versus the interpreted accounting variable (future quarterly earnings before taxes (EBT)). Then, in the second stage, the SSME service channels were added to the earnings before tax model in terms of profitability measure, which informs corporate earnings before operating the business to account for the income tax attributed to it for the purpose of estimating their impact on the performance of social banks. According to our findings, the banks are investing in SME services just to validate their investments in SME services as a strategic necessity. SSMEs services do not provide any strategic advantage to any banks in terms of financial or accounting performance or efficiency since the banks are already efficient. Investing in SMEs is a tool for preserving their strategic positions. Therefore, the contribution of this study is focused on the fact that it highlights the impact of financing the social deposit banking industry on institutions, while most studies analyze the vice versa interaction.
Full article
Figure 1
Open AccessArticle
The Effects of Internal Governance Factors on Lending Portfolio Composition in Islamic Banks
Int. J. Financial Stud. 2023, 11(3), 85; https://doi.org/10.3390/ijfs11030085 - 26 Jun 2023
Abstract
Recent studies indicate that lending portfoliocomposition in Islamic banks is concentrated towardsdebt-based lending portfolio; however, the ideal lending portfoliocomposition in Islamic banks should be an equity-based lending portfolio. This article explores the effects of the internal governance factors on lending portfolio compositionofIslamic banks
[...] Read more.
Recent studies indicate that lending portfoliocomposition in Islamic banks is concentrated towardsdebt-based lending portfolio; however, the ideal lending portfoliocomposition in Islamic banks should be an equity-based lending portfolio. This article explores the effects of the internal governance factors on lending portfolio compositionofIslamic banks in the GCC Region. The internal governance factors investigated are board of directors’ characteristics (size and independence), Shariah supervisory board attributes (size and cross-membership), and ownership structure (family and government). The generalized least squares (GLS) method is used to examine the relationship between the study variables. The results indicate that two characteristics of the board of directors, size and independence, and two attributes of the Shariah supervisory board, Shariah board size and Shariah board cross-membership, have significant effects on lending portfolio composition of Islamic banks in the GCC Region. However, the rest of the internal governance factors have no effects on lending portfolio composition of Islamic banks in the GCC Region. These significant results add new contributions to the literature in the area of internal corporate governance of Islamic banks. The article concludes with suggestions for regulators and policy makers in the GCC Region with regard to the ideal characteristics of the board of directors and the optimal attributes of the Shariah supervisory board in Islamic banks as well as directions for future studies in this area of research.
Full article
(This article belongs to the Special Issue Islamic Finance Performance during Pandemic and Future Agenda)
►▼
Show Figures
Figure 1
Open AccessArticle
Assessing the Effective Use of State Property: Accounting and Analytical Support and Analysis Methodology
by
, , , and
Int. J. Financial Stud. 2023, 11(3), 84; https://doi.org/10.3390/ijfs11030084 - 26 Jun 2023
Abstract
►▼
Show Figures
The effective use of state property is one of the topical issues of economic policy affecting the interests of all segments of society. The need to comply with the principle of the effective use of budgetary funds is enshrined in Article 34 of
[...] Read more.
The effective use of state property is one of the topical issues of economic policy affecting the interests of all segments of society. The need to comply with the principle of the effective use of budgetary funds is enshrined in Article 34 of the Budget Code of the Russian Federation. However, while studying the existing system of financial management in Russian practice, it was revealed that the current methodological approaches do not fully solve the tasks enshrined in the budget legislation. This is primarily due to the lack of proper accounting and analytical and methodological support for the relevant management procedures. Thus, the Federal State Information and Analytical System of the Federal Property Management Agency “Unified System of State Property Management” was used as the information base. However, this information system has some significant shortcomings, such as (1) the lack of a single regulatory act on the register at all levels of government; (2) the duplicated information about the property; and (3) there are no indicators and criteria in the register that would reflect effective property use, etc. Secondly, the approaches used to assess effective state property use are based on industry standards (health, education, etc.) in relation to the property and specific equipment necessary for the provision of public services. In this regard, the purpose of this study is to improve the concept and methodology for analyzing effective state (municipal) property use. The main areas for improving the methodology are (1) to develop a unified register of state property as the main source of accounting and analytical information; (2) to assess the property which state bodies and state institutions need to perform their functions and powers in full and of appropriate quality; and (3) to develop unified approaches for assessing the effective state (municipal) property use. As a result, the authors developed proposals introducing a unified register of the state (municipal) property, which includes indicators characterizing the physical state, recognition in accounting, and forms of the property disposal and use, which can be the basis for information and analytical support for assessing the effective state (municipal) property use at all levels. The study represents a system of indicators for assessing the effective state (municipal) property use, which consists of (1) indicators of the property disposal and use that make up the state treasury and (2) indicators of the property disposal and use of economic entities in the public sector. The results of the study are confirmed by empirical studies using the example of public institutions in the field of higher education and executive authorities.
Full article
Figure 1
Open AccessArticle
Microfinance, an Alternative for Financing Entrepreneurship: Implications and Trends-Bibliometric Analysis
by
, , , and
Int. J. Financial Stud. 2023, 11(3), 83; https://doi.org/10.3390/ijfs11030083 - 23 Jun 2023
Abstract
Microfinance has become one of the most important financing alternatives for business start-ups, especially for vulnerable groups in poor regions. Microfinance provides access to financial products, especially to people who have been excluded from the traditional financial system. However, the mainstream literature on
[...] Read more.
Microfinance has become one of the most important financing alternatives for business start-ups, especially for vulnerable groups in poor regions. Microfinance provides access to financial products, especially to people who have been excluded from the traditional financial system. However, the mainstream literature on microfinance shows its impact on poverty alleviation, but it is not yet well developed to understand its dynamizing role in the entrepreneurial sector. There is still a large gap in the literature on analyzing microfinance as a financing alternative, so this paper seeks to find this relationship in the literature. A bibliometric analysis is applied, both of the performance of the publications and a word co-occurrence analysis during the period 2017–2022. The articles indexed in the Web of Science have been considered and systematized through the SCIMAT software v1.1.04, developed by the Soft Computing and Intelligent Information Systems Research Group, University of Granada, Granada, Spain. Microfinance institutions, education, entrepreneurship, organizational performance, business microcredits, and women microentrepreneurs have been identified as driving themes to be considered in future analyses. At the end of the document, the proposed future lines of research are presented. In addition, the results show the growing interest of the academic community in the topic, with 2022 being the year with the highest number of articles published on the topic.
Full article
(This article belongs to the Special Issue Literature Reviews in Finance)
►▼
Show Figures
Figure 1
Open AccessArticle
Disclosure of Key Audit Matters: European Listed Companies’ Evidence on Related Parties Transactions
Int. J. Financial Stud. 2023, 11(3), 82; https://doi.org/10.3390/ijfs11030082 - 21 Jun 2023
Abstract
►▼
Show Figures
The growing expenses, dependence on IT for business operations, and growing requirements regarding related party transaction (RPT) reporting impose the need for increased attention to this area. The paper’s objective is to examine the nature of RPTs, identified by auditors as a key
[...] Read more.
The growing expenses, dependence on IT for business operations, and growing requirements regarding related party transaction (RPT) reporting impose the need for increased attention to this area. The paper’s objective is to examine the nature of RPTs, identified by auditors as a key audit matter (KAMs), challenges and solutions to problems related to risk management, and the detection of factors affecting audit quality. The research methodology is qualitative, with an analysis of the level of disclosure of KAMs reported by auditors from the Related Parties category, grouped by type of auditors, their opinion, year, country, and fields of activity. Data were collected from the Audit Analytics database and filtered by category KAM: Related parties, period 2013–2021. The selection resulted in 111 companies reporting 248 KAMs related to RPTs, from which most were reported in 2017–2019. Of these, nearly two-thirds were reported by auditors from the Big4 category. Most KAMs were reported by companies in the U.K., Germany, and France, and the industries with the most KAMs were finance, insurance, and real estate. In conclusion, there are factors that can affect audit quality due to the reporting of RPTs, but by identifying them, the audit process can be better managed, thus increasing its efficiency.
Full article
Figure 1
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Economies, IJFS, JRFM, Sustainability
Environmental Social Governance (ESG) Disclosure and Financial Markets
Topic Editors: Shaista Wasiuzzaman, Wan Masliza Wan MohammadDeadline: 24 December 2023
Topic in
AI, BDCC, Economies, IJFS, JTAER, Sustainability
Artificial Intelligence Applications in Financial Technology
Topic Editors: Albert Y.S. Lam, Yanhui GengDeadline: 1 March 2024
Conferences
Special Issues
Special Issue in
IJFS
Macroeconomic and Financial Markets
Guest Editor: Hachmi Ben AmeurDeadline: 11 August 2023
Special Issue in
IJFS
Cross-Cultural Corporate Governance, Firm Performance and Firm Value
Guest Editor: Brian BoltonDeadline: 30 September 2023
Special Issue in
IJFS
Financial Econometrics and Machine Learning
Guest Editors: Sahbi Farhani, Muhammad Ali NasirDeadline: 31 December 2023
Special Issue in
IJFS
Making Green from Green: The Truth about Sustainable Finance
Guest Editor: Saurabh AhluwaliaDeadline: 31 May 2024