Financial Risk Research Articles

Impact of Debt Composition and Accounting Conservatism on Financial Distress in Emerging Markets

Aims: This study investigates the impact of debt composition, specifically short-term and long-term debt, on corporate financial distress, focusing on the moderating role of accounting conservatism. It aims to understand how firms in emerging markets, particularly Kenya, can achieve financial stability through prudent debt structuring and conservative accounting practices. Study Design: The research adopted an explanatory research design. Place and Duration of Study: The study utilized financial data from firms listed on the Kenyan Securities Exchange between 2008 and 2021. Methodology: The study analyzed a sample of 45 firms trading at the Nairobi Stock Exchange over 14 years from 2008 to 2021. The study used panel logistic regression, a statistical method for analyzing data with multiple observations over time, to test its hypotheses. Results: The results show that both short-term and long-term debt increase the likelihood of financial distress, with accounting conservatism further intensifying the risk for firms heavily reliant on short-term debt while mitigating it for those with long-term debt. This suggests that conservative accounting practices, which promote early recognition of losses, may exacerbate financial distress for firms heavily reliant on short-term debt. In contrast, for long-term debt, accounting conservatism has a mitigating effect helping firms manage their debt obligations more effectively over time. This highlights a nuanced role of accounting conservatism, where it increases the pressure on firms with short-term debt while providing stability for those with long-term obligations. Conclusion: The findings suggest that corporate managers in emerging markets should carefully balance short- and long-term debt to minimize financial distress risks. Additionally, adopting conservative accounting practices can serve as a safeguard, providing early warning signals for financial trouble and enhancing corporate stability. Managers should therefore carefully balance short-term and long-term debt to reduce financial distress while adopting conservative accounting practices to enhance financial transparency and risk management. Policymakers can support firms by promoting responsible debt management and offering incentives for conservative financial reporting to strengthen overall financial stability.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

Exploring AI's Role in Enhancing Risk Assessment Models in Financial Quantitative Trading

The application of Artificial Intelligence (AI) algorithms to financial risk management has fundamentally transformed the nature of risk and the tools being used to control and manage it. This paper examines how AI is deployed to manage market risk, including volatility forecasting, portfolio optimisation, stress testing, and sentiment analysis. While many of these models are well-accepted across the industry and underpin many front-office and risk-management processes, they are often limited in dynamic market environments where traditional linearity doesnt exist and real-time data is king. AI has brought advanced tools that can be applied to large and complex data sets, and that adapt quickly as market conditions change. Reinforcement learning algorithms are now deployed to optimise portfolio weights dynamically. Natural-language processing is also used to augment sentiment analysis, which can be leveraged to assess potential risk. Other forms of AI can be deployed to address challenges that come with more advanced AI models. Explainable AI models provide the opportunity to produce financial outcomes that are more transparent and less opaque while also helping to validate regulatory compliance. Analyzing the applications of AI case studies sheds light on why financial professionals must continue to embrace AI tools, many of which are designed to help them manage risk in an increasingly volatile market environment.

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin’s pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.

Predictors of investment intention in real estate: Extending the theory of planned behavior

This paper explores the factors affecting the investment intention of individual real estate investors utilizing the extended theory of planned behavior. With the help of self-administered questionnaire, data from 366 individual investors from India was collected. This data was analysed using two-step structural equation modelling. While significant direct effect of attitude, external influence, financial self-efficacy and perceived financial return was found, interpersonal influence, perceived financial risk, facilitating conditions and financial awareness had no significant direct impact on investment intention. Upon checking the mediating effect of attitude on the factors, all factors influenced investment intention through attitude except facilitating condition and financial awareness. It was also observed that attitude stands out as the most important aspect due to strongest influence on intention directly and also providing mediation to all variables except two. The study guides policymakers and investment institutions to develop strategies and utilize resources in a direction that can bring out a positive outcome by strengthening real estate investors’ investment intentions. It brings out the fact that financial confidence should be boosted by enabling investors to handle and manage their finances which can bring in a positive attitude for investing.

Managing Financial Risks of Global Companies Through Corporate Social Responsibility: The Specifics of Sustainable Employment in Developed and Developing Countries

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune “Global 500” rankings for 2021–2023. As a result, the consequences of global companies’ CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries.

Sustainability Assessment of the Performance of Parmigiano Reggiano PDO Firms: A Comparative Analysis of Firms’ Legal Form and Altitude Range

Geographical indications (GIs), protected by the European Union with the collective marks of PDO (protected designation of origin), PGI (protected geographical indication), and TSG (traditional specialty guaranteed), play an important role in the social and economic system. They not only guarantee food needs, but promote correct consumer information, protect local food, and play a role in the environmental and social sustainability of rural areas. In Italy, Parmigiano Reggiano (PR-RE) PDO cheese is ranked second in foods with the GI protection mark by turnover. This research aims to assess the financial sustainability of the firms registered in the PR-RE PDO consortium using financial statement (FINSTAT) analysis. Financial ratios (FR) and the EM-Score were applied to assess firms’ performance, financial risk, and credit score. The analysis distinguished firms by legal form, cooperative and non-cooperative, and altitude range—plain hill and mountain. The main findings of the research were as follows: (1) a better performance of lowland non-cooperative firms and lower financial risk, (2) a longer duration of the inventory cycle of cooperative firms, and (3) a greater financial risk in mountain cooperatives. The results provide indications for improving firms’ performance and for designing financial instruments for the sector. To our knowledge, this is the first research to carry out an analysis of all the available FINSTATs of firms in the PR-RE PDO sector.

The Usage of Paylater Among College Students: The Role of Self-Control, Hedonistic Conformity, and Family Financial Education

Research aims: The increasing phenomenon of the unwise use of Paylater in society, even among college students, is interesting to review further. This study aims to test the role of self-control and hedonistic conformity with the moderation of family financial education on the use of Paylater by university students.Design/Methodology/Approach: Primary data was obtained through a survey involving 117 respondents who have or are currently using the Paylater service. Furthermore, the data was processed using the Partial Least Squares (PLS) method.Research findings: This study produced the following findings: hedonistic conformity was proven to have a positive effect on the use of Paylater in the full sample and in two sub-samples. This finding is in line with the TPB modification used in this research, hedonic conformity as a proxy for subjective norms has been shown to contribute positively to the use of Paylater among colleege students. Meanwhile, the role of family financial education has not been proven to moderate the regression path of self-control and hedonic conformity on the use of Paylater.Theoretical Contribution/Originality: This study contributes to enriching the literature on personal financial management, especially regarding the use of Paylater based on internal and external factors of students.Practitioners/Policy Implications: These results offer recommendations for personal financial management, especially for university students, to avoid the risk of being trapped by Paylater by paying attention to their social community and prioritizing family financial education.Research Limitations/Implications: Respondents were mostly undergraduate students, so this study cannot explain the dynamics of financial behavior among undergraduate and postgraduate students who have different demographic characteristics. Therefore, future research needs to analyze different sample groups to enrich the scientific perspective. Furthermore, to provide depth of analysis, this study proposes to add other internal aspects of the individual to the TPB theoretical framework (e.g.: perception of usury and financial risk tolerance). Last but not least, regarding the development of indicators from the family financial education variable that does not yet include personal debt management education items, hence this indicator can be added as a proxy for this variable to obtain in-depth measurements.

Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks

Background Spin-offs play a significant role in organizational development strategies, particularly in Islamic banking, by fostering entrepreneurship, innovation, and Shariah-compliant management practices. Indonesia stands as a pioneer in implementing the dual banking system and has established a spin-off policy to foster the growth of Islamic banking. This study investigates whether the spin-off decision has a significant impact on financing risk in Indonesian Islamic banks. Methods Financing risk is measured by the non-performing financing ratio, while the spin-off decision is represented by a dummy variable equal to 1 for the post-spin-off period and 0 for the pre-spin-off period. This study utilizes data from semi-annual reports of 35 Indonesian Islamic banks and analyzes it using a dynamic panel model with the Generalized Method of Moments (GMM). Results The findings reveal that spin-offs significantly reduce financing risk, thereby enhancing the financial resilience and boosting investor appeal. Notably, this implies that Islamic banks operating as Islamic windows exhibit a higher level of financing risk compared to fully-fledged Islamic banks. Furthermore, a noteworthy pattern emerges that spin-off Islamic banks with substantial assets demonstrate greater risk in comparison to their counterparts with more modest assets. System GMM also confirmed the result. Conclusions Islamic banks can significantly reduce their financing risks by establishing independent Islamic banks, or spin-offs. Unlike Islamic windows, which are typically integrated within conventional banks and face higher risk levels, standalone Islamic banks offer greater flexibility and control over their operations.

Urban-scale power decarbonization using a modified power purchase agreements framework based on Markowitz mean-variance theory

Urban power decarbonization is essential in the fight against climate change, yet current research often neglects the financial risks faced by investors and the shifting demands of consumers in liberalized electricity markets. This study addresses these gaps by proposing a modified Markowitz Mean-Variance Portfolio (MVP) theory, integrated with the Low Emissions Analysis Platform (LEAP), and a deep learning model. On this basis, an urban energy transition framework centered on Power Purchase Agreements (PPAs) is proposed and developed. The framework is validated considering a case study in Kitakyushu, Japan, highlighting its potential in accelerating power sector decarbonization and achieving net-zero emissions by 2038. Additionally, the internal rate of return (IRR) remains stable between 14.5 % and 19.6 % across seven other cities. While the framework reduces long-term cash flow volatility, its effectiveness hinges on industrial electrification efficiency and regional energy self-sufficiency. The findings indicate that relying solely on renewable energy for low-carbon transitions is unrealistic. Furthermore, green hydrogen could emerge as a viable alternative to fossil fuels, potentially replacing batteries for long-term energy storage. Future research should explore cross-regional energy trade and establish legal frameworks for long-term energy transactions to bolster urban energy transition resilience across diverse geographic and economic contexts.

Stregthening Financial Risk Governance and Compliance in the U.S.: A Roadmap for Ensuring Economic Stability

Stregthening Financial Risk Governance and Compliance in the U.S.: A Roadmap for Ensuring Economic Stability

Problems and prospects of methodological features of accounting and audit of import operations

In the context of globalization and integration of world markets, import operations occupy an important place in the activities of enterprises, forming a significant part of their economic processes. Effective management of these operations requires accurate and timely accounting and high-quality auditing, which becomes especially relevant in modern conditions. The study of methodological features of accounting and auditing of import operations is a relevant and timely area that helps improve the quality of financial reporting and management decisions. The purpose of the study is to analyze the problems and prospects of methodological features of accounting and audit of import operations, as well as to develop recommendations for their improvement. The study examined the main methodological approaches, existing problems and challenges, and proposed solutions aimed at increasing the efficiency and reliability of accounting and auditing in a global economy. The improvement of methodological approaches to the accounting and auditing of import operations will improve the accuracy and reliability of financial reporting, reduce the risks of non-compliance with regulatory requirements, as well as improve management decision-making and the overall financial stability of companies. The development and implementation of effective accounting and auditing methods that comply with international standards and best practices will minimize financial risks and increase the competitiveness of enterprises in the global market. A study of the problems and prospects of methodological features of accounting and auditing of import operations has revealed a number of key issues that require attention and solutions. The main challenges are the complexity and diversity of regulatory requirements, currency fluctuations, the diversity of imported goods and services, difficulties in assessing and recognizing imported goods, and the lack of qualified specialists.

A new approach to assessing a company’s financial indicators: taking into account business and financial risks

There are two main methods for estimating the value of assets. The first method is the well-known CAPM (Capital Asset Pricing Model), which uses the risk-free rate as the initial return and takes into account only the business risk associated with investing in a specific asset, and not in the market as a whole. The second method is associated with the use of one of two main theories of capital structure (Brusov–Filatova–Orekhova (BFO) theory and Modigliani–Miller (MM) theory), which take into account only the financial risk associated with the use of debt financing and allow the calculation of all financial indicators of a company of arbitrary age (BFO theory) or perpetuity one (MM theory). The article develops a new approach related to the use of the company’s current profitability, taken from the annual report, as a seed profitability of an asset (company). As part of the new approach, a methodology has been developed that makes it possible to calculate all the main financial indicators of a company within the framework of capital structure theories, taking into account both financial and business risks. Transition from CAPM to a new methodology significantly improves the accuracy of the estimate. The new approach, unlike CAPM, has forecasting capabilities.

A Novel Method for Rapid Screening of Salmonidae Ingredients and Accurate Detection of Atlantic Salmon (Salmo salar) Simultaneously Using Duplex Real-Time PCR Coupled with Melting Curve Analysis

The substitution of ingredients with Salmonidae, particularly Salmo salar, has led to widespread reports of financial losses and health risks globally, emphasizing the urgent need for the development of a rapid and precise method for species identification. The aim of the present study was to develop a novel method for the rapid screening of Salmonidae ingredients and the accurate detection of S. salar simultaneously using multiplex real-time PCR coupled with melting curve analysis. Specifically, primer sets specific for S. salar and Salmonidae were cross-confirmed. Moreover, the reaction system and conditions of a real-time duplex PCR were optimized, and the proposed methodology was verified, proving that the assay has good specificity and sensitivity. Clear and distinguishable melting peaks, with expected Tm values of around 80 °C (S. salar) and 84 °C (Salmonidae), were observed for twelve products, proving the presence of S. salar. However, four products were not derived from S. salar, but they could have belonged to another species within the Salmonidae family due to the presence of only one specific melting peak at a Tm value of about 84 °C. Therefore, the novel assay in the present study allows for the fast and accurate screening of Salmonidae ingredients and the detection of S. salar simultaneously.

Influence of External Stakeholder Involvement and Risk Perception on Affordable Housing: A Survey on the Implementation of Affordable Housing Project in Anderson-Ofafa Estate, Kisumu City (Kenya)

The significance of stakeholder involvement and risk forecasting in project management has been documented in literature and in studies all over the World. One such study was carried out in Anderson-Ofafa estate, Kisumu City. The main goal of the study was to assess the influence of external stakeholder involvement and risk perception on the implementation of the project. The stakeholders showed resistance to the housing project.The study aimed to establish the risks the project is exposed to based on stakeholder attitude and their influence on project performance, and whether the stakeholders were involved so as to provide solutions to problems bedeviling the project and to generate new knowledge. A survey of general respondents/ intelligent laypersons of age 18 years and above totaling 384 and key informants numbering 16 proportionately sampled from a population of 174,145 people found out that there was a significant correlation between stakeholder involvement, risk perception and project performance. In addition, the study established the occurrence of socio-political,regulatory and financial risk factors which had a perceived high influence of 50% and above, moderate influence of 39% and a very high influence of above 50% respectively.Updated risk register had a perceived high influence of 50% and above. Socio-political risk factors significantly influenced the project performance as was hypothesized by the alternative hypothesis of this study.Financial risk factors had the greatest impact.The stakeholders were involved in the project but were still not satisfied with its implementation.The results are expected to assist the project stakeholders align their goals with that of the project to foster sustainability.The practical or managerial implications is explained as follows- there are documented potential consequences and outcomes of stakeholder actions that have contributed to risk factors that will have extended effect on all stakeholders; including project beneficiaries, project providers, project influencers, and the project governance team. Interventions recommended include; Project sponsors to focus on strategies that manages risk perception and returns expectations, Project manager to reduce project scope, public sector to put measures that guarantee stability in laws and regulations and, the project should be infused into the new affordable housing program by the Kenya Kwanza government to benefit from the new government initiated housing levy fund.

Skewness impact through distributional evaluation (SITDE) method: a new method in multi-criteria decision making

In this article, we propose the Skewness Impact Through Distributional Evaluation (SITDE) method for multi-criteria decision-making. Unlike traditional methods ignoring criteria distribution asymmetry, SITDE enhances the decision quality. We use a case-study on electric vehicle selection to illustrate the efficiency of SITDE relative to conventional methods. The comprehensive decision support with SITDE across multiple thresholds is an important aspect of a detailed and balanced decision-making virtue as our results show in case the skewness effects are included. Theoretical backgrounds, typologies practical applications and statistical justifications to confirm the accuracy of SITDE in regarding to limit complexities MCDM scenarios are explained this paper. SITDE enhances a robust decision-making process by accounting for the inherent biases of symmetric assumption models. Moreover, SITDE proves to be extremely useful in cases for dealing with asymmetrical data distributions and skewed datasets humanitarian cause as infected situations are always overwhelming. This broad applicability indicates the potential for wider use with respect to different domains, such as in environmental management and financial risk assessment. Future studies should investigate the adequacy or insufficiency of its fusion with different MCDM methods and explore if this approach would apply a more efficient and valid insight that might enhance decision making in multiple sectors.

Analysis Of Financial Statement of Chinese Real Estate Enterprises-Take One Certain Company as An Example

Real estate enterprises play an important role in the economic development of China, which are directly influence the economy. Different from other enterprises, real estate enterprises have the features of high leverage, large capital demand, slow repayment and long investment period. In recent years, the central government has implemented a series of policies, real estate enterprises facing severe financial risks. It’s important for enterprises to identify and prevent the financial risks. As a representative of China's highly leveraged and large-scale real estate enterprises, the company continuously violates the "three lines and four levels" policy when it is implemented. This article analyses the financial risks of this company by collecting the company’s financial statement data from the past five years, using ratio analysis and Z-value model. By researching the data, this article finds that the company has weak debt paying ability, operational ability, and profitability in recent years, also facing high financial risks.

Volatility spillovers and time-varying correlations across stock and commodity markets: An assessment during the epidemic crisis era

The financial and economic stability of the business world is greatly damaged by a number of crises over the years. These crises also change and affect stock markets around the globe. This study examines the nonlinear behavior of well-known stock markets in the US, Europe, Canada, and Asia (S&P 500, Nikkei 225, CAC40, DAX30, Bombay Stock Index, Hang Seng Index, and Canadian Stock Index). We also investigate the commodity indexes (Gold, Bitcoin, Brent Crude Oil, and West Texas Crude Oil). The transmission of returns and the volatility propagation pattern across stock-commodity markets during the COVID-19 era is analyzed using the VAR DCC MEGARCH model. The results demonstrate that correlations intensify and become more complicated during the COVID-19 period, especially between financial market indices and oil commodity indices. However, the volatility spillover statistics during the pandemic period show a definite rise in the uncertainty of financial market indices due to index instability. The analysis also shows that financial markets experience a substantial leverage effect during the pandemic crisis. According to the research, Bitcoin is crucial for reducing portfolio risk during the health crisis, and gold proves to be the best option for investors to diversify their financial risk.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.