Dynamic Fractal Asset Pricing Model for Financial Risk Evaluation

The financing cost of Supply Chain Management (S CM) is decreased by the emergence of supply chain finance. There is a deficiency in risk in terms of strategy and practice due to the growth of a diversified supply chain financial area. Numerous automated risk assessment frameworks are developed using deep learning and machine learning techniques in recent years to address these issues. Hence, industries are faced with the supply chain sustainability risk that is emerged from various resources. In existing research works, the field of supply chain finance is very limited due to the lack of integration. However, it does not address the small and medium sized enterprise which is used in the real time economy. The purpose of this survey is to conduct a literature review on estimating the financial risks associated with SCMs. This study includes various literature works considered for the financial risk evaluation, where the evaluation of deep learning techniques, performance analysis and their challenges while performing the financial risk evaluation are researched. However, the conventional techniques are presented with diverse challenges in estimating financial risks, and this review paper is highly useful for future research work. This research analyzes the estimation techniques, dataset utilized for evaluating the financial risks, implementation tools and performance metrics involved in the evaluation models. Moreover, various research gaps in the existing research works are explored which helps to show the enhanced performance of the supply chain financial risk evaluation model. Here, it explores various financial risk analyses which will be helpful for future research. Finally, the overall challenges and future works on the financial risk evaluation models are discussed to support future works.

Finance is becoming more important in the national economy. Maintaining financial stability is critical not only for the financial industry’s prosperity and development, but also for a country’s political, economic, and social development. This paper will look at the mechanisms that cause systemic risk to develop and evolve, as well as how to measure systemic financial risk in multiple dimensions. To begin, create a system for evaluating financial risk in a systematic manner. Second, using the AHP and CRITIC methods to determine various indicators and market weights, create a systemic financial risk evaluation model based on the system dynamics model, and calculate the system from 2010 to 2019 comprehensive financial risk index. Finally, simulation research is conducted using the system dynamics model of systemic financial risk, and the simulation results are analyzed. The findings show that China’s financial risk has been gradually increasing since 2016, with relatively small fluctuations in risk state.

To prevent the opportunities and challenges brought by unexpected economic situations, each enterprise must identify the sources of financial risk and establish an early warning mechanism for financial risk. In view of the shortcomings of traditional financial risk source identification, this paper adopts the CRITIC (Criteria Importance Through Inter-criteria Correlation) method to achieve objective weighting of indicators and calculate the enterprise risk value. MLP (Multilayer Perceptron) is used to quantitatively analyze the relationship between indicator data samples and risk values to establish a financial risk evaluation model. In this paper, macroeconomic risk indicators and business environmental risk indicators are used as the two main categories, and 12 indicators are selected as evaluation factors to apply the model to a wider range of corporate financial risk assessments. The results show that: this training model is more robust, with an error of only 8.86%. Among them, the loan interest rate (0.121) and total operating income (0.086) have a greater impact on corporate financial risks, and companies need to establish a complete financial risk early warning system based on the assessment results.

Financial risk is the most significant and comprehensive reflection form of the business risk. How to utilize these fuzziness financial targets to the financial risk's appraisal, becomes now the key point on financial risk appraisal. In the foundation of analyzing the origin of thermoelectricity generation business finance risk, this paper establishes thermoelectricity generation business finance risk assessment model based on the fuzzy mathematics, and carries on the confirmation using the data of Datang International Power Generation Co., Ltd. Through analytical study, based on fuzzy mathematics' financial risk evaluation model, the paper proves the feasibility that in our country thermal power enterprise finance risk assessment and the management, provides the help in enhancing the enterprise core competitiveness for our country thermal power enterprise under the electric power organizational reform's background.

Purpose Of the Study: Evolution of the business environment has forced the financial institutions to turn around and change their own business models in order to retain their loyal customer base. The commercial banks have certainly faced their own fair share of challenges when they had to introduce innovations in their products and processes and simultaneously strengthen their financial health with sustained risk governance techniques and decisions. Thus, the purpose of the study is to understand the importance of sustainability of firm value for a banking business, and how financial attributes and risk governance factors can impact the farm valuation of Indian commercial banks. A Brief Literature Review: Over the years authors have tried studying the relation between firm value and various financial attributes. In certain instances, risk governance techniques like the capital adequacy ratio have also been introduced in various studies. In a study, the author tried to study the relationship between financial risks and firm value and examine the moderating effect that capital adequacy has on the relationship between financial risk and firm value. The results of the study indicated that a higher capital adequacy ratio increases form value and has a moderating effect on financial risk and firm value (Jagirani et al., 2023). Similarly, certain authors over the years have also tried to find out the effectiveness of Risk Committee on the financial success of certain quoted banks in African countries. (Odubuasi et al., 2022) focused specifically on risk committee diligence committee composition committee diversity, committee expertise, committee size and return on equity of the country’s selected from Africa. The fixed effect model employed by them reported that the impact of the diligence and composition of risk committee on bank performance in certain African countries was highly significant. Thus, they concluded that risk committee effectiveness, its diligence, its composition and various leverage factors should be pivotal in the formulation of risk management committee of various organisations, and especially banks. The Methodology Adopted: Content analysis and panel data has been employed in the study to understand the implications of the financial attributes and risk governance factors on the sustainability in the firm value of Indian commercial banks. Financial attributes like Net Interest Margin, Cash Deposit ratio, Volume of digital transactions, etc. and risk governance attributes like the presence of a chief risk officer, the activism of the risk committee, etc. have been used in the study. Tobin’s Q has been taken as the proxy of firm value in the study as well. The hierarchical regression analysis has been the economic modelling technique used in the study to achieve the study purpose. Study Hypothesis: Since hierarchical regression modelling has been used in the study, the aim of the study is to assess whether the overall model of firm value of the banking business gets better with the gradual introduction of certain financial attributes variables and risk governance variables or not. Accordingly, with each step and introduction of certain attributes and variables into the model, research hypothesis in the study has been developed. Results & Findings: The study is an attempt at introducing a functional model with some significant financial ratios, some crucial ratios that define the characteristics of the bank and the risk governance framework with the firm value. The logic behind using the Tobin’s Q as a proxy of firm value is that unlike several other financial ratios that solely focus on the book value of the variables; the Q ratio takes the market capitalization value of the banks into account in the study. Through the study, almost all the explanatory variables like CDR, ROE, ROA etc. showed a positive and significant relationship with firm value in the first 2 models whereas on the other hand, introducing risk governance in the third model made it too complicated. As a result, the model was proven insignificant and unproductive unlike the first 2 models. Limitations: There is a lot of scope for further research on this particular subject as the model could be developed in a lot of ways to explain how different variables may it be financial ratios, risk governance or financial innovativeness rating etc. could impact the firm value which in turn impacts the managerial decision making of senior executives and the economy as a whole and further help in making the banking business is sustainable. Implications: The study gives us a glimpse of certain financial attributes and various risk governance factors that could be used to maintain and sustain the firm value of commercial banks in India. It is certainly essential to focus on the sustainability of firm value of commercial banks, for the reason that banks are the lifeblood of an economy and help act as the mediating factor between deposits, savings and investment in the economy.

The dynamic of a financial asset's returns and prices can be expressed using a deterministic process if there is no uncertainty about its future behavior, or with a stochastic process in the more likely case when the value is uncertain. Stochastic processes in continuous time are the most used tool to explain the dynamics of a financial asset's returns and prices. They are the building blocks with which to construct financial models for portfolio optimization, derivatives pricing, and risk management. Continuous-time processes allow for more elegant theoretical modeling compared to discrete-time models and many results proven in probability theory can be applied to obtain a simple evaluation method. Keywords: Brownian motion; Poisson process; jump processes; Levy processes; stochastic process X =; gamma process; exponential process; variance gamma; process; stable process; standard CTS process; generalized tempered stable; process; modified tempered stable; process; standard MTS process; normal tempered stable; process; standard NTS process; normal inverse Gaussian; standard NIG process; Kim-Rachev tempered stable; standard KRTS process; rapidly decreasing tempered stable; standard RDTS process; Brownian motion; Independent increments; Stationary increments; time-changed Brownian motion; The Modified Tempered Stable Processes with Application to Finance; conditional expectation; change of measure; Esscher transform; Brownian Motion and Stochastic Calculus; Risk Assessment Decisions in Banking and Finance

With rapid development of China's market economy, business activities face many risks. The core of the overall management and control of risk is the company's financial risk management. In order to prevent financial risks caused by business management crisis and promote corporate healthy steady sustainable development, the focus on financial risk management and control is a possible series of financial problems and risk to predict and control in the future. In this paper, based on the financial risk characteristics of Chinese enterprises and the management and control situation, in the new period, we strengthen risk awareness, improved scientific financial decisions and build financial risk management and control system by analysing impact factors of financial risk in China to put forward the corresponding management and control strategies.

In recent years, digital currencies based on blockchain technology have brought about a boom in the research of encrypted digital currencies with the design concept of peer-to-peer trading and decentralization, but due to the lack of supervision and obvious speculative characteristics of the digital currency market, the sharp fluctuations in the market can easily trigger investor sentiment fluctuations, which in turn will lead to social instability and even financial system risks. In this paper, through the analysis of the financial risk source factors of blockchain digital currency, the evaluation index is established, the risk evaluation index system is constructed, and then the CART classification algorithm is used to analyze it and evaluate and test the model. The characteristic factor structure obtained by the CART algorithm in this paper better explains the financial risk characteristics of blockchain digital currency and gives relatively reliable identification results. The results show that the CART decision tree classification method is effective and has a high accuracy rate, which classifies the financial risks of blockchain digital currency, and the method has excellent adaptability and matchability for the classification of risk problems.

With the deepening of economic globalization, Chinas economy and the global economy are becoming increasingly interdependent and closely linked, resulting in a more complex environment for domestic enterprises and heightened financial risks. To enhance the risk resilience of enterprises, the research methods for assessing financial risks are becoming more diverse. Traditional financial risk analysis methods, such as the single-argument model, have certain limitations in the practical application of enterprise financial risk evaluation. These methods cannot overcome the restrictions of time, region, and industry, and their application value is not fully realized. To better assist enterprises in addressing the complexities of financial risks, fuzzy hierarchical analysis is applied to the traditional hierarchical analysis method under fuzzy optimization conditions. This method focuses on indices of measurable comparability, facilitating a more reasonable and objective financial risk evaluation of enterprises, especially when comparing different companies in the new energy vehicle industry and conducting a longitudinal comparison of Company A. Fuzzy hierarchical analysis integrates qualitative judgment with quantitative analysis, using triangular fuzzy numbers to generate a judgment matrix. The results are transformed into an objective fuzzy set, enabling the quantification and structuring of complex system indicators and improving the rationality and accuracy of the enterprises financial risk evaluation.

Environmental, social and governance (ESG) ratings (scores) provide quantitative measures for socially responsible investment. We consider ESG scores to be a third independent variable—on par with financial risk and return—and incorporate such numeric scores into dynamic asset pricing. Based on this incorporation, we develop the entire investment process for the ESG market: portfolio optimization and efficient frontier, capital market line (the market portfolio), risk-assessment measures and hedging instruments (options). There is currently no riskless asset available in such an ESG market; to address this, we develop the so-called shadow riskless rate, applicable to markets having only risky assets. We believe this to be the first paper that fully develops, under a single dynamic pricing framework, the entire investment process for an ESG market. As there are significant differences in methodologies developed by providers of ESG scores, we do not take the position that data from any single agency are to be favored. Consequently, we utilize ESG scores from Refinitiv in the manuscript’s empirical studies and redo all computations using S&P Global RobeoSAM ESG scores.

The short term risks in finance cannot be suitably analyzed by using the classical risk theory, and a way to circumvent this pitfall is to use fractional processes. The higher the risk is the higher the order of the fractal is. Recently, a model of complex-valued fractional Brownian motion of order n has been suggested in the literature, and the purpose herein is to examine which kind of results it can provide in mathematics of finance. After a short background to this model, one comments on fractals in finance, and then one suggests a general model of complex fractals for stock market. Various state variables can be identified with the parameters so involved in this model, and mainly, one can introduce a new concept of stock market time different from the standard absolute physical time. As an application, a Black-Scholes equation of order n is derived.

Subjective and objective weights of indexes are determined respectively by G1 and entropy methods in the paper, furthermore, optimal weight is determined based on the Combination Weighting Method by minimizing the summary of the square of deviation. Finally, construct the financial risk evaluation model applied to the real estate and rank the order of related companies according to the comprehensive risk value. Contributions of this paper lie in three aspects: Firstly, the established financial risk evaluation index system overcomes the shortcomings of the existing one in indexes' calculating. Secondly, the growth rate and cash inflows indexes are included in the appraisal system. Thirdly, Combination Weighting Method is used to guarantee the minimization of the overall deviation.

This paper combines industry characteristics of photovoltaic enterprises, selects 14 financial indicators to construct an enterprise performance evaluation indicator system, and calculates the comprehensive financial performance value in combination with the indicator weights. A financial performance evaluation model is then constructed using the PSO-LMBP method. The effectiveness of the proposed model in financial performance evaluation is effectively verified through a case study, providing a reference for financial performance evaluation in other industries.

Supply chain financial credit evaluation mechanism under the background of big data

Significant changes under the 1996 FAIR Act and trade agreements are occurring in the U.S. farm sector. The changes will affect both the source and the magnitude of the business and financial risks that farmers will be required to manage. The objectives of this study were to investigate farmer plans regarding business expansion, timing of expansion, and expected financing of that business expansion and to analyze their financial services and risk management strategies. The study was based on data from a nationwide survey of subscribers to Top Operator farm magazine. The data were categorized into six groups for analysis: demographics, intergenerational transfer, debt characteristics, lender relationships, business expansion, and farmer expectations of their lenders. Statistical techniques were used to evaluate differences among farmers across regions of the country, types of farm businesses, and sizes of farm businesses.