Financial Risk Assessment Research Articles

Advanced financial market forecasting: integrating Monte Carlo simulations with ensemble Machine Learning models

<abstract><p>This paper presents a novel integration of Machine Learning (ML) models with Monte Carlo simulations to enhance financial forecasting and risk assessments in dynamic market environments. Traditional financial forecasting methods, which primarily rely on linear statistical and econometric models, face limitations in addressing the complexities of modern financial datasets. To overcome these challenges, we explore the evolution of financial forecasting, transitioning from time-series analyses to sophisticated ML techniques such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Our methodology combines an ensemble of these ML models, each providing unique insights into market dynamics, with the probabilistic scenario analysis of Monte Carlo simulations. This integration aims to improve the predictive accuracy and risk evaluation in financial markets. We apply this integrated approach to a quantitative analysis of the SPY Exchange-Traded Fund (ETF) and selected major stocks, focusing on various risk-reward ratios including Sharpe, Sortino, and Treynor. The results demonstrate the potential of our approach in providing a comprehensive view of risks and rewards, highlighting the advantages of combining traditional risk assessment methods with advanced predictive models. This research contributes to the field of applied mathematical finance by offering a more nuanced, adaptive tool for financial market analyses and decision-making.</p></abstract>

Audit Quality Control and Financial Risk Prevention Strategies for CPAs under Multi-Objective Optimization Calculation Considering Sustainable Development Strategy

Abstract Audit functions as the “guardian” of the capital market by overseeing economic activities and providing forensic analysis on the major projects and financial transactions of the entities it examines. The sustainability of auditing as a profession hinges critically on audit quality. This study aims to investigate the characteristics and elements that influence audit quality control, employing modern risk-oriented audit theory to formulate a financial audit risk assessment process. The research uses a multiple linear regression model to analyze factors affecting audit quality control among certified public accountants (CPAs). The model analyzes data from 513 A+H share listed companies, with parameters estimated using the Singular Value Decomposition (SVD) method within an overall least squares framework. Furthermore, the Particle Swarm Optimization (PSO) algorithm, a technique within the multi-objective optimization algorithm suite, is utilized to refine parameters in the manipulability Jones model. This adjustment is intended to better quantify the level of earnings management of the audited entities, thus enhancing the reflection of financial audit risks. Findings from the study reveal that the coefficient reflecting the impact of a CPA’s practice experience on audit quality control is 0.249. Additionally, it was observed that a 1% deterioration in the financial condition of an audited entity correlates with a 0.505% decrease in audit quality. For high-risk clients classified under ‘Special Treatment’ (ST), the range of practice years for CPAs is predominantly between 6 to 14 years, which may not fully meet the clients’ auditing needs. The study suggests that tailored audit surveys should be conducted for various client types prior to audit engagements. This strategic approach would enable better allocation of CPA resources, thereby enhancing audit quality control and the capability to mitigate financial audit risks.

Financial Risk Assessment of Environmental Protection Enterprises under the Background of Carbon Peak and Carbon Neutrality

Financial Risk Assessment of Environmental Protection Enterprises under the Background of Carbon Peak and Carbon Neutrality

Logistics financial risk assessment based on decision tree algorithm model

In order to solve the financial risk problem of small and medium-sized logistics enterprises, a log algorithm is used to measure the financial risk of logistics. Combined with the actual situation of logistics finance, through the analysis of randomly selected logistics finance data, quantitatively extract important financial risk indicators, build a credit rating model based on decision tree C4.5 algorithm, and conduct empirical research on the model. The empirical results show that the quantitative financial risk indicator extraction method combined with decision tree C4.5 algorithm credit rating model has high accuracy in the evaluation of logistics finance risk. Therefore, based on the existing logistics financial risk assessment methods and models, the decision tree analysis technology is applied to analyze and make decisions on the credit status of credit applicants, and select companies with good credit, so as to reduce the financial risk of logistics enterprises and improve the efficiency of financial risk management.

Research on Financial Risk Assessment of Xpeng Motors

Research on Financial Risk Assessment of Xpeng Motors

Sustainable Financial Risk Assessment and Management System Construction based on Big Data Analysis

In today's financial industry, traditional risk assessment models can no longer meet the needs of complex and dynamically changing financial markets. Therefore, building a sustainable financial risk assessment and management system based on big data analysis is particularly important. This system can predict and mitigate financial risks by analyzing large-scale datasets, improving the risk management capabilities of financial institutions, and providing the scientific basis for formulating relevant policies. Applying big data technology can greatly enrich the dimensions and depth of risk assessment. By collecting and processing a large amount of data from different channels, including social media, transaction records, market dynamics, etc., risk signals that traditional methods cannot observe can be revealed, allowing financial institutions to more accurately identify and predict potential risk points. The analysis model based on big data can achieve real-time risk monitoring. These models can automatically process real-time data promptly and alert potential risks, allowing financial institutions to quickly respond to market changes and take appropriate risk control measures. Building such a system is not without challenges. The quality and integrity of data, privacy protection, and the accuracy and transparency of analytical models are all issues that need to be focused on.

ASSESSMENT AND WAYS TO MINIMIZE THE FINANCIAL RISKS OF THE BANKING SECTOR UNDER FINANCIAL INSTABILITY

The article is devoted to the assessment of financial risks of the banking sector, their impact on the activity of banks and their minimization in conditions of economic instability. The banking sector is a key link that ensures the development of the financial system, but banking activity is risky. Therefore, it is important to study banking risks and ways to minimize them in order to increase the efficiency and stability of the banking system. The article provides a classification of banking risks in accordance with domestic literature. Factors due to which banking institutions are exposed to risks are reflected. Financial risks of the banking sector affect the net profit, capital of the bank. Therefore, the impact of risks on the financial stability and efficiency of Ukrainian banks was analyzed. The bank's regulatory capital is one of the most important indicators of its activity, the main purpose of which is to cover the negative consequences of various risks. During the entire studied period, there is a tendency to increase this indicator, which is positive. Due to external risks, namely the difficult military and economic situation, the profit of Ukrainian banks decreased. The credit risk, which is one of the most significant in banking activity, is characterized. The causes of credit risk in banking are given. The dynamics of the assets of Ukrainian banks, the volumes of loans granted, were studied. It was concluded that in modern conditions of banking activity, there is a slight slowdown in lending to the population and an increase in the volume of loans granted to business entities. To reduce credit risks, commercial banks use various methods to protect against credit risk: diversification, limiting, insurance, risk distribution. The banking sector cannot exist without risk, so it is necessary to look for ways to minimize them. Minimizing the level of financial risks in the banking sector involves the implementation of risk management measures and methods. Measures to minimize banking risks include preventive and protective measures. Banking risk management methods include: methods of avoiding and accepting banking risks. The article reflects the peculiarities of these measures and methods. Keywords: financial risks, banking sector, bank profit, credit risk, bank assets, bank risk minimization.

Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty

Risk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME) based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities. In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications.

IPSAL: Implementation of the module to generate the Sobol sequence and indices

Sensitivity and uncertainty analysis hold significant importance across a range of applications, spanning from industrial problems to climate change, financial risk assessment, as well as mathematical and computational models. These analyses involve identifying influential input parameters in models to comprehend their impact on the output. Sensitivity analysis can be performed locally, examining parameter effects at a fixed value, or globally, evaluating the model across a range of parameter values. The Sobol method stands as a robust approach for global sensitivity analysis, employing a Sobol sequence to create samples more uniformly within the input parameter space, thus enabling efficient exploration of model inputs. This paper aims to introduce a computational implementation in Scilab to generate the Sobol sequence for utilization in sensitivity analysis through the Sobol method. A test case was applied to generate Sobol sequences and discuss the obtained results.

Analysis of the Impact of Digital Currency on Traditional Banking and Financial Stability

Digital currency, as a representative product of blockchain technology, such as Bitcoin and Ethereum, has begun to have a far-reaching impact on traditional banking business, but also brought new challenges to financial stability. This article studies the financial risk assessment and pre-alarm model based on support vector machine (SVM) algorithm, aiming at accurately assessing the risks in digital currency market and providing timely pre-alarm. Through comparative experiments, it is found that the algorithm has obvious advantages in dealing with high-dimensional and nonlinear problems, and can accurately classify and warn financial risks. The accuracy of the model is as high as 96.88%, and the average absolute error is reduced by 40.22%, which proves the effectiveness and superiority of the model. Generally speaking, the financial risk assessment and pre-alarm model studied in this article provides a scientific and effective method for digital currency market risk management, which has important application value. Regulators need to invest more resources and energy to monitor and analyze the dynamic changes in the digital currency market and reduce financial risks.

Search on Risk Assessment of PPP Project Financing Based on ISM-FAHP Model

With the rapid development of China's economy, the demand of large-scale infrastructure construction projects is increasing day by day, but the shortage of funds has become the bottleneck restricting the progress of projects. In order to solve this problem, the Chinese government has gradually implemented the PPP project financing model in the field of infrastructure to attract social capital to participate and share the risk. However, with the implementation of this model, the problem of financing risk has gradually become prominent. Therefore, this study aims to provide scientific theoretical support and practical suggestions for relevant decision-making through in-depth analysis of PPP project financing risk and application of ISM-FAHP model.

Construction of a Visual Platform for Higher Vocational Financial Education Combining Gaussian Hybrid Networks

Abstract In this paper, a Gaussian mixture network distribution finance has been carried out to assess the risk, which is used as a risk assessment tool for the visual platform of higher vocational financial education. Financial data is quantified and determined by determining the cumulative expected loss amount to establish the financial investment risk assessment function. The Activiti open-source workflow engine was utilized to remove complex financial data and configure the K-line as the platform’s data visualization tool. Finally, the financial education visualization platform was used to analyze the Gaussian distribution and K-line data of X stock, which verified the practicality of the platform, and the effectiveness of the platform was verified by taking the students of H higher vocational college as the sample of the teaching experiment. The results show that the influence coefficient of the platform teaching on the quality of the course is 0.856, and the influence coefficient on the learning interest is 0.887, which indicates that the visual platform teaching makes students interested and strengthens their cognitive level. The visual digital reform of teaching finance majors in colleges and universities is provided with a new reference direction by this paper.

Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective on Alternative Data

The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., “alternative data”) stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel “meta” experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes.

The Role of The Actuary in Assessing Financial Risks: a Case Study of Iraqi Insurance Companies

Objective: This study aims to examine the role of actuaries in assessing financial risks in insurance companies, their roles in applying financial risk assessment methods, and the authority granted to them for comprehensive risk assessment. Indeed, some companies may impede actuaries from performing their duties effectively by limiting their authority, causing obstacles and inability to perform their duties. Thus, the main objective of this paper is to assess to what extent actuaries could manage financial risk in insurance companies in spite of all the challenges and difficulties that they face.
 
 Methods: The study objectives are achieved by using data collected from a questionnaire distributed to employees of Iraqi insurance companies. A total of 165 forms were distributed, and 150 of them were retrieved and validated for statistical analysis.
 
 Results: The findings reveal a strong positive association between actuary’s dimensions and the dimensions of the financial risk assessment. This relationship reduces investment risks and enhances pricing and estimation processes.
 
 Conclusion: The positive association between all the actuary’s dimensions and the dimensions of the financial risk assessment highlights the active and essential role of actuaries in assessing both internal and external risks.

An analysis of financial risk assessment of globally listed football clubs

The global football market has grown in the past three decades, and football clubs' sustainable financial operations have gradually gained attention. This study aims to construct a financial risk assessment model applicable to the football industry, explore globally listed football clubs' overall financial operating characteristics, and analyse the leading causes of the club's financial crisis. We selected a sample of 24 currently listed football clubs worldwide and an exploratory factor analysis (EFA) model to construct the model of financial risk assessment for football clubs. The model identified and classified the financial risk components for listed football clubs, thus facilitating risk warning and prevention for modern professional clubs. This study found that football clubs are at higher financial risk overall, with the following general characteristics: (1) small amount of listed capital; (2) high asset-liability ratio; (3) low net profits and a large proportion of clubs make losses; and (4) weak asset liquidity. Finally, the study discussed the leading causes of the financial crises of football clubs in both external and internal dimensions, providing a reference for the self-sustainability of clubs and football authorities.

Развитие методики оценки финансовых рисков как фактор обеспечения экономической безопасности предприятий

Due to the increasing role of financial risks of various nature and orientation, the paper formulates an approach to defining this definition. Financial risks are decomposed in terms of various classification features. The key financial risks in relation to the companies that are representatives of the oil and gas sector as a pivotal sector for the Russian economy have been specified. The analysis of modern approaches to the assessment of fi-nancial risks allows to conclude the priority of using probit- and logit-regression scoring models for these aims due to the high level of accuracy and simplicity of interpretation of their results. Therefore, as part of the re-search, a scoring model has been developed, which provides the possibility of carrying out a comprehensive assessment of financial risk. Based on the analysis of corporate reporting of the leading domestic oil and gas companies, the proposed model was tested and calibrated, its final form and weight coefficients of parameters were determined. The results obtained from the analysis of financial risks make it possible to draw a conclu-sion about the fairness of the model and its high reliability.

Technology Innovation, Financing Risk and Control in Small and Medium-Sized Enterprises Supply Chain

With the development of supply chain finance, micro and small enterprises can effectively solve their financing difficulties and improve the flow of funds in the supply chain. This paper reviews the work done by past research in solving the financing problems of MSMEs, credit risk assessment and control, and the development of new technologies in supply chain finance. It discusses how technology affects the efficiency and security of supply chain finance and takes small and medium-sized enterprises as carriers to carry out a broader theoretical analysis of risk control and assessment. At the same time, the paper analyses the application of currently popular and advanced technologies, such as blockchain, artificial intelligence, and the Internet of Things, in solving the financing challenges of SMEs. In addition, the paper discusses and compares the risk assessment and control of micro and small enterprise financing and supply chain finance risk control and proposes corresponding solutions and models.

Application of Deep Learning in Regional Financial Risk Assessment and Prediction

As the economy enters the new normal, more and more risk problems are exposed in the financial market, and the market supervision system is still not perfect. Preventing financial risks has become the focus of domestic attention. Ubiquitous risks have increasingly become the "sword of Damocles" hanging over the main body of financial markets. Due to the differences in regional economic development, regional financial risks will become more complicated, which is likely to lead to a nationwide financial crisis. The application of DL (Deep Learning) has become the research frontier in the field of financial risk management, which will surely bring about subversive changes in the field of financial risk management. Through DL, we can quantitatively evaluate the financial risks in different fields, closely monitor the key industries and fields with the highest regional financial risk distribution, strengthen risk monitoring and analysis, promptly use risk warning letters, situation reports and other forms to prompt risks, prevent the spread and spread of risks in specific industries or fields, and do a good job in risk prevention and response. In order to effectively identify regional financial risks, timely understand and master the distribution and impact of various risks, and do a good job in risk monitoring and prompting.

Dynamic financial and monetary security risk assessment based on information service security assessment model and blockchain

With the rapid development of blockchain technology, the financial and monetary (FM) blockchain fields also began to collide. Therefore, this study aims to explore the relationship between the two fields and efficiently evaluate the security of financial information. Firstly, this study introduces the theoretical basis of blockchain in the dynamic linkage mechanism of FM and gives the overall framework of digital currency based on blockchain. Meanwhile, the relationship between blockchain and digital finance is empirically analyzed and designed. Secondly, the framework of financial information service security assessment (ISSA) is created using blockchain technology, and the frame of security risk evaluation is verified by taking electronic invoice information as the research object. Finally, the results show that: (1) foreign exchange (forex) events and major stock index decline events have a significant impact on the return of Bitcoin (BTC) in the short term. Moreover, more than 70% of uncertainty events will make BTC’s abnormal return (AR) significantly positive; (2) Under the influence of forex uncertainty events, only one BTC’s AR is remarkably negative in short order, while the other seven times are markedly positive. In the uncertainty events of major stock indexes, only two times are significantly negative, and the other six times are positive. This indicates that uncertain events in the short run make prominent AR of BTC; the proposed blockchain-based ISSA model and assessment index are scientific, feasible, and operable.

A deep learning model-based approach to financial risk assessment and prediction

Abstract This paper proposes a split-lending network model for bank credit risk, calculates whether a bank fails by simulating the changes in bank assets and liabilities over time, and adds the default rate calculation considering the characteristics of the default rate when a bank borrows. Meanwhile, the XGBoost-based classifier is used instead of random forest to improve the accuracy of classification, and the grcForest_XGB model is established. The activation function Sigmoid, the error function mean square error function, and the adam optimization function with the best effect at present are used to predict the accuracy of the grcForest_XGB model, and the different models are compared with the grcForest model for comparison experiments. The experiments show that the grcForest model has higher AUC and KS metrics of 0.8224 and 0.6368, respectively. The recall value is 0.8319, which ranks first among the six models. The Acc value is 0.9732, which is only 0.05 lower than LSTM, and is at a higher level. This study shows that the model is more accurate for risk assessment, can predict financial risks in advance, and make an effective assessment of financial risks.