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An indicator system for financial monitoring is known as a financial risk early warning system. This system makes use of statistical data to make predictions about the size of the possibility of shocks occurring at a certain point in time. Examples of shocks include a regional or national economic crisis or a collapse of the stock market. The early warning system for enterprise financial risk presents additional challenges, such as data spikes and uncertainty in the integration of risk information. It is the responsibility of the Financial Crisis early warning system to analyze and summarize significant data on the financial status of an organization, as well as to provide technical support for the purpose of making financial decisions via the use of Data Mining Technologies (DMT and other related technologies). It has been determined that the financial warning model that is based on Decision Tree (DT) integration is more accurate. This suggests that the model has the potential to increase the correct identification rate of companies that are experiencing a financial crisis, provided that the overall warning accuracy is enhanced. Hence, the proposed method, the DMT-DT, using early warning systems, includes automated solutions used to track borrowers’ credit condition to help monitor and appraise credit portfolios. Deloitte can assist in developing, implementing, improving, and maintaining various indicators for detecting early warning signals of potential financial situations. People in the community and individuals in danger can be better prepared to take timely and appropriate action with the help of people-centered early warning systems, that aim to lessen the likelihood of harm to people, property, and the environment.

Rotationally Invariant Estimators (RIE) are a new family of covariance matrix estimators based on random matrix theory and free probability. The family RIE has been proposed to improve the performance of an investment portfolio in the Markowitz model’s framework. Here, we apply state-of-the-art RIE techniques to improve the estimation of financial states via the correlation matrix. The Synthesized Clustering (SYNCLUS) and a dynamic programming algorithm for optimal one-dimensional clustering were employed to that aim. We found that the RIE estimations of the minimum portfolio risk increase the Active Information Storage (AIS) in the American and European markets. AIS’s local dynamic also mimics financial states’ behavior when estimating under the one-dimensional clustering algorithm. Our results suggest that in times of financial turbulence, RIE estimates can be of great advantage in minimizing risk exposure.

Most illegal Ponzi schemes are ultimately out of control and lead to systemic financial risk. Risk education and precaution are similar to mass random immunization of epidemic spreading. In this study, the effect of random immunization strategy is evaluated based on the potential-investor–divestor (PID) spreading model in both homo- and inhomogeneous social networks. Fund flux function and system balance function are formulated. The zero point of system balance is used as the collapse point. The peak value of balance, the total number of investors involved and the total amount of principal involved are defined to compare the immunization effects in various scenarios. Mathematical derivation and numerical simulation show that the random immunization takes effect by postponing the peak position of the system balance as well as suppressing the peak values of the system balance. This kind of positive effect helps reduce the scheme’s scale of total number of investors involved and total amount of principal involved. The random immunization is more powerful towards the schemes with small spreading rate than those with medium and high spreading rates. Hence, it is suitable for the concentrated regulation on a large amount of small scale and slow spreading schemes in bulk.

Financial fraud is more likely to spread and produce serious and adverse results through social networks. This study investigates four protection strategies: the uniform protection strategy, the random protection strategy, the targeted protection strategy, and the acquaintance protection strategy based on the potential-investor-divestor (PID) model. The simulation results show that the targeted protection strategy is the best solution for both ER and BA networks. The random protection strategy is the least efficient solution, as it requires spreading a large number of anti-fraud messages to achieve a relatively good performance. The acquaintance protection strategy performs closely to the targeted protection strategy in terms of social dynamics. However, the uniform protection strategy is better than the acquaintance protection strategy, as it involves fewer victims when it collapses. This study suggests that the regulators should protect investors from financial fraud collapses by promoting the financial literacy education and regulating the behaviors of influential people.

The price of carbon emission rights in the market fluctuates greatly due to various factors from economy, finance, and climate. For an enterprise that needs to conduct carbon trading activities, it is extremely necessary to fully grasp the price of carbon trading in the future period. Thus in this paper, a deep neural network-based assistive decision model for financial risk prediction in carbon trading market is proposed for this purpose. Specifically, the dynamic risk spillover effects of domestic and international carbon trading markets are studied, and a frontier time-varying model is utilized to measure the risk spillover effects. Then, the deep neural network is used for quantitative research and to construct an intelligent decision scheme that outputs financial risk prediction results. Four perspectives, energy price, climate environment, carbon market price, and macroeconomics, are selected as the input to analyze the influence factors of carbon emission rights price. Finally, several linear regression models are adopted as the baseline methods for comparison, and experimental results show that the proposed method can achieve better prediction performance compared with baseline methods.

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises.

We propose a model that integrates investment, underwriting, and consumption/dividend policy decisions for a nonlife insurer by using a risk control variable related to the wealth-income ratio of the firm. This facilitates the efficient transfer of insurance risk to capital markets since it allows to select simultaneously investments and underwriting volume. The model is particularly valuable for business lines with significant exposure to extreme events and disaster risk, as it accounts for features usually depicted during negative economic shocks and catastrophic events, such as Levy-type jump-diffusion dynamics for the financial log-returns that are in turn correlated with insurance premiums and liabilities, as well as worst-case scenarios in which policyholders in the insurance portfolio report claims with the same severity simultaneously. Using the martingale method, we determine an optimal solvency threshold or wealth-income ratio, and investment strategy that maximizes the expected utility from dividend payouts that follows a (possibly stochastic) consumption clock. We illustrate the main results with numerical examples for log- and power-utility functions, and (bounded variation) tempered stable Levy jumps.

In the last decade there has been a significant increase in the use of derivatives as a vehicle to manage financial risk. The sudden spurt of derivatives has resulted in the Financial Accounting Standards Board (FASB) being forced to develop new standards for quantification and disclosure. The financial standard of interest to this study is Statement of Financial Accounting Standards (SFAS 133). SFAS 133 requires all derivatives, without exception and regardless of the accounting treatment for the underlying asset, liability, or transaction, to be recognized in the balance sheet as either liabilities or assets. SFAS 133 entitled Accounting for derivative activities and hedging (and SFAS 137, which postponed the implementation of SFAS 133 until June 2000) is different from prior standards in that it requires recognition as opposed to mere disclosure in the notes. The justification given for implementing SFAS 133 was to increase transparency to investors. In this study we empirically investigate this issue with particular focus on whether SFAS 133 provides incremental information above that provided by reported earnings, book value, and proxies for omitted variables. We study commercial banks since they are among the most frequent users of large-scale derivative contracts and their use has increased significantly over the last two decades, and in particular over the last five years. Our findings indicate that information regarding total derivative contracts, when disclosed in the financial statements as required by SFAS 133/137, is value relevant to investors. However, investors view this information negatively, perhaps attributing this to higher risk. Losses on holding derivatives are viewed positively and gains are viewed negatively.

In this paper, an enterprise financial risk indicator system is established to warn about the financial risk of enterprises. First, the related knowledge of financial risk and its measurement is introduced. Next, the financial risk indicator system of small- and medium-sized enterprises (SMEs) is established based on back propagation neural network (BPNN). The rough set theory is adopted to simplify the indicator. Finally, the BPNN model is used to predict the financial situation of SMEs. The results show that in the 490th iteration, the performance of the BPNN-based financial risk early warning system for SMEs can reach the optimal and meet the accuracy requirements of initialization. The error of the enterprise financial risk early warning model converges to the target error, so the calculation result is credible. The actual output after training is close to the expected output. By judging the actual output value, it can be known that the financial risk status of SMEs in 2016, 2017 and 2018 is of low alarm. This exploration has a certain preventive effect on the financial risk of enterprises and provides a basis for the rapid development of enterprises.

Accurate forecasting of stock prices not only guides investor behavior but also assesses financial risk and promotes balanced economic and social development. This paper uses a dynamic factor-enhanced model averaging method to forecast the daily closing price of the Shanghai Composite Index, maximizing the use of valid information by weighting the forecast values of different models. Firstly, the common factor is extracted from the smoothed original explanatory variables; then the dynamic factor augmented model selection method and the model averaging method based on different criteria are used to predict different lag orders of the common factor and the explanatory variables, and the effectiveness of the dynamic factor augmented censored group cross-validation model averaging method is verified using multiple predictor error indicators as well as the DM test. The experimental results show that the dynamic factor augmented censored group cross-validation model averaging method has better prediction results and is more robust.

It is shown that the axioms for coherent risk measures imply that whenever there is a pair of portfolios such that one of them dominates the other in a given sample (which happens with finite probability even for large samples), then there is no optimal portfolio under any coherent measure on that sample, and the risk measure diverges to minus infinity. This instability was first discovered in the special example of Expected Shortfall which is used here both as an illustration and as a springboard for generalization.

Financial market instability and losses driven by changes in stock prices, currencies, interest rates, and other factors are the primary causes of economic risk. One of the risk types with the highest priority for every business is financial risk. The consumer electronics manufacturing sector’s focus on rising technology is driving important growth and includes manufacturers of smartwatches, stylish home products, and smart speakers. Risks can arise from the inability to meet functional requirements and business expectations throughout the life cycle, from original formation to final disposal, while supplying competitive electronic products. All of this highlights the necessity and potential of thorough study in the field of financial risk in economic growth. With the help of owners and managers of top electronic manufacturing industries in India, this study’s goal is to examine and evaluate several aspects of financial risk in economic benefits. The main factors of the financial risk covered under the study include liquidity risk, market risk, credit risk, and operational risk. Financial risks also arise from a combination of macroeconomic factors, including changing interest rates on the market and the potential for default by sizable businesses or industries. Financial stability is of the utmost importance to a commercial enterprise to maintain its position and status in the commercial environment. All of this demonstrates the value and need for rigorous research in the area of financial risk affecting the performance of the organization. This study intends to analyze several components of financial risk in consumer electronic goods manufacturers in India. Various aspects discussed in the study revolving around financial risk management are an important factor and demand the maximum attention of the organization.

This paper evaluates the disclosures about derivative financial instruments provided by the 30 high-profile companies tracked in the Dow-Jones Industrial Average (DJIA-30). We discuss investors' needs for information on financial risk, document how the DJIA-30 implemented the requirements of FASB Statement No. 133, "Accounting for Derivatives and Hedging Activities" (SFAS 133), analyze the usefulness of SFAS 133 requirements, and comment on recent controversies over derivatives reporting. We also examine how the DJIA-30 complied with the SEC requirements for qualitative and quantitative information about the market risks attributed to their derivatives positions, and the impact of SFAS 133 adoption on these disclosures.

We find the DJIA-30 generally increased their derivatives' disclosures after adopting SFAS 133, consistent with its requirements. However, the disclosures are not as informative as one might expect, given SFAS 133's detailed requirements. Many companies now omit the previously required table of notional amounts, making it more difficult to assess their exposure to financial risk. Moreover, SEC requirements allow three formats for reporting quantitative information, only one of which is the tabular approach that helps users understand exposure. Because few companies use the tabular approach, disappearance of notional amounts is more serious. Generally small in recognized financial effects, derivatives and hedging activities are combined with other items in the financial statements, thereby complicating analyses of their impact. Footnote disclosures isolating derivatives performance tend to be incomplete and disconnected. Finally, we find that required 12-month forecasts of unrealized derivatives gains and losses reclassified from accumulated other comprehensive income to earnings miss the mark by a wide margin, rendering them unreliable in forecasting future earnings effects.

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

This paper addresses a deficiency in the existing literature by examining the time–frequency domain association between economic risk (ER) and financial risk (FR) for the Mexico, Indonesia, Nigeria and Turkey (MINT) nations using dataset from 1984 to 2018. To the authors’ awareness, the relationship between economics and finance has not been thoroughly investigated in the context of risk for the MINT nations. As a result, the outcomes of this research are anticipated to provide an insight on and initiate a fresh discussion regarding the financial-economic nexus. The Breitung and Candelon (BC) causality and the wavelet coherence (WTC) techniques are used to inspect the combined time–frequency causal interrelationship between FR and ER, in accordance with the study goals. The findings from the wavelet revealed the following: (i) a one-way causality exists from ER to FR in Mexico; (ii) a one-way causality exists from FR to ER in Indonesia; (iii) a unidirectional causality exists from ER to FR in Nigeria and (iv) a one-way causality exists from FR to ER in Turkey. Furthermore, the BC causality outcomes validate the WTC outcomes. The study findings are critical for both researchers and macroeconomic policymakers and can be utilized to make appropriate measures, if necessary, by adopting alternative or more appropriate financial and economic decisions.

Residential “Nonpayment risk” for water utilities — the risk of revenue loss from residential customers not paying water bills — is a financial risk for water service providers that remains poorly understood. Current rate setting strategies do not explicitly consider nonpayment risk and are generally informed by past payment histories. We develop a new heuristic model to categorize and evaluate water utility pricing (rate setting) strategies that are responsive to the effects of nonpayment (i.e., delinquency) on water utility revenues. The model is the first attempt, to our knowledge, to theorize the impact of residential nonpayment on utility revenues. The method draws on the theory behind the Kelly Criterion, a strategy developed in the mid-20th century now used by investors in portfolio management. The results of our thought exercise show that even excessive nonpayment levels (50% each year) do not negate the effectiveness of rate increases for revenue generation, but that nonpayment management can provide revenue benefits. Without political motives, utilities with high nonpayment may be inclined to continue raising water rates, unless higher water rates result in higher nonpayment levels. As such, we highlight the need to understand “nonpayment elasticity”: the change in nonpayment due to changes in water rates. We illustrate how increased nonpayment elasticity can decrease the percent of potential revenue collected, particularly when water rates are increased substantially. The simple model provides a method to evaluate the financial sustainability of elevated nonpayment rates in water utility management and financial risk analysis.

The rapid changes in the economic situation and the complex market environment make the cross-border e-commerce industry, as an important part of the market economy, face many challenges and risks in the development process. The particularity of its transaction and the imperfect tax mechanism have virtually increased the financial management risk. The research constructs the financial risk analysis model with the help of the support vector machine (SVM) and fuzzy theory. Through algorithm test and empirical research, it is found that the average accuracy of the optimized SVM on the selected data set is more than 90%, and after parameter optimization, the change of model fitness tends to be stable with the increase of iteration times, which greatly improves the search ability of sample data, and the accuracy of financial data classification with high risk is 46.8%. In the empirical research, the model established by fuzzy SVM can effectively eliminate the irrelevant index data, and the prediction accuracy of investment risk and operation risk has reached 80% or more. In the prediction of financing risk and tax risk, its accuracy has been improved by 12.4% compared to that before use.

The first objective of this research is to explain and analyze the financial indicators of the Islamic banking sector in the Middle East and North Africa (MENA) countries before and over the COVID-19 pandemic period, and the second objective is to explore the key determinant that might affect Islamic banks performance before and during COVID-19 pandemic period. Orbis Bank Focus database and annual financial reports are used to collect financial information of Islamic banks in MENA countries over two years: 2019 and 2020. Descriptive statistics, t-test, and multiple regression are employed to analyze the financial structure and performance of Islamic banks before and during COVID-19 pandemic period. The results of this study reveal that there is a sharp drop in financial indicators in Islamic banks during the pandemic period, liquidity risk, bank size, managerial efficiency ratio, and oil price shocks are the determinants of Islamic banks profitability before the appearance of COVID-19. The credit risk, bank size, liquidity risk, managerial efficiency, inflation, and oil price shocks are the determinants of Islamic banks profitability during the pandemic period. Finally, there is no significant impact of GDP and capital structure on Islamic banks profitability before and during the COVID-19 pandemic period.

Costs and benefits generally matter more if they are sooner rather than later. So it is normal in public policy and project appraisal to “discount” the future. If $1.05 in a year’s time is counted equally with $1 today, this is applying a “discount rate” of 5% per year…

Diversification strategy has always been the focus of enterprises. Many enterprises have changed their models and adopted diversified management methods to improve their market competitiveness. However, not all companies can bring profit growth. For companies, a diversification strategy is like a double-edged sword. If it is not used properly, it will also lead the company into a financial crisis. This paper takes LeTV Group as the research object to discuss the financial risks and consequences of corporate diversification strategies and uses LeTV’s financial data to analyze the economic consequences of LeTV’s diversification strategy financial risks. The study finds that due to the mismatch of capital allocation, limited profitability, and insufficient funds as support, the financial risks of LeTV have increased. In response to a series of problems, this paper proposes measures to solve the financial risk control of LeTV’s diversification strategy. It is realistic and necessary to study the diversification financial risks and consequences of LeTV Group, hoping to provide a reference for the diversification strategy layout of other companies.