Showing papers on "Financial risk published in 2022"

Financial Risk, Renewable Energy Technology Budgets, and Environmental Sustainability: Is Going Green Possible?

Abstract: Since the industrial revolution, countries have been facing the issue of climate change and environmental degradation. It is widely believed that the investment in research and development of renewable energy can play a pivotal role in fighting against climate change. However, the financial risk also increases, which can influence renewable energy technology R&D budgets and environmental sustainability. Nevertheless, the current literature is silent on the linkage between financial risk, renewable energy technology budgets, and environmental quality. Against this backdrop, this article attempts to explore the dynamic linkage between financial risk, renewable energy technology budgets, and ecological footprint under the Environment Kuznets Curve (EKC) framework in Organization for Economic Cooperation and Development (OECD) countries. For this purpose, yearly data from 1984 to 2018 is employed using the advanced panel data estimation methods that address the slope heterogeneity and cross-sectional dependence issues. The results indicate that improvement in the financial risk index significantly decreases footprints, and renewable energy technology budgets also promote environmental sustainability. Economic globalization poses a significant negative effect on the ecological footprint, while energy consumption adds to the footprint. Moreover, the findings validated the EKC hypothesis in OECD countries. In addition, a unidirectional causality is detected from financial risk to renewable technology energy budgets, while bidirectional causality exists between financial risk and ecological footprint, and between financial risk, and economic growth. Based on the empirical findings, policy suggestions are presented to promote environmental sustainability.

Financial risks and performance of conventional and Islamic banks: Do reputational risk matters?

Abstract: Purpose The purpose of this paper is to shed light on the reputational risk, which is elusive and difficult to measure due to the lack of its conclusive definition. Literature supports the notion that financial risks may translate into reputational risks that pose threat to bank performance. However, empirical investigations in this context are still at their nascent stage. Design/methodology/approach This study has used a panel dataset for the sample of 24 conventional and Islamic banks regarding the period 2007–2017 by using a structural equation model. Findings The results of this study show that reputational risk partially mediates the relationship between financial risks and the performance of conventional banks. However, for Islamic banks, the reputational risk remains insignificant as a mediator. This study provides significant implications to risk managers in banks, regulators and academics to understand the role of reputational risk linked to financial risks for the improvement of bank performance. Originality/value This study aims to add to the literature by measuring reputational risk through the shareholders reputational score index, which is used as a mediator to determine whether financial risks of banks affect the performance of conventional and Islamic banks in Pakistan.

Financial risk propagation between Chinese and American stock markets based on multilayer networks

Abstract: Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets.

Complex network propagation effect based on SIRS model and research on the necessity of smart city credit system construction

Abstract: In this paper, we first consider one of the interconnected enterprises in the economy as nodes in a complex network. Based on the SIRS risk contagion model, we describe the transmission process of credit risk between enterprises after the bursting of the stock market with the contagion process, and clarify their diffusion mechanism and effect. The propagation effect of SIRS model in complex network is simulated and analyzed. The results show that when the risk contagion intensity exceeds a certain threshold and the proportion of infected enterprises in the economy exceeds a certain level, it will inevitably lead to the insecurity of the whole economy. In most cases, the policy intervention of the regulatory authorities is necessary. If the crisis is allowed to infect, it is likely to induce the financial risk of the whole system; The construction of smart city credit system can use big data to solve the problem of information island, promote the co construction and sharing of data resources, assist the regulators to effectively prevent and block the transmission of credit risk, and nip the risk in the bud, so as to maintain social stability and economic security.

Sustainable Financing and Financial Risk Management of Financial Institutions—Case Study on Chinese Banks

Abstract: This study examines the relationship between sustainable financing and financial risk management of Chinese financial institutions, using data from Chinese banks. Financial risk management is a comprehensive measure of operating performance, asset quality and capital adequacy ratio. The structural vector auto-regression model determines the relationship between two variables. The positive shock of sustainable financing business negatively impacts the financial risk management of banks. In contrast, positive shock of banks’ financial risk management positively affects sustainable financing. Further subdivision of the sample revealed that sustainable financing does not always negatively impact the financial risk management of large state-owned banks. However, the positive shock of financial risk management reduces urban banks’ green credit proportions. The results are consistent whenever compared between the empirical outcome of the entire sample and the sample consisting of national joint stock bank accounts. This comparison helps eliminate the possibility of a biased outcome as a major portion of the sample is from a national joint-stock bank account. Apart from data limitations, the results of the sub-sample test are influenced due to the difference in deposit and loan interest rates, as well as different ownership structures of banks.

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

Abstract: In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect.

Does disability increase households’ health financial risk: evidence from the Uganda demographic and health survey

Abstract: In the last few years, there has been a worldwide commitment to protect the vulnerable individuals from higher financial risk through out-of-pocket (OOP) health expenditure. This study examines the influence of disability and socio-demographic factors on households' health financial risks in Uganda.We used nationally representative cross-sectional data from the Uganda Demographic and Health Survey (UDHS) collected in 2016 by the Uganda Bureau of Statistics (UBOS) in Uganda. We measured financial risk (households' health expenditure) by money paid for health care services. We estimated the "probit" model to investigate the effect of disability on health financial risk.A total of 19,305 households were included in this study. Almost 32% of households paid money for health care services access, among which 32% paid through out-of-pocket. Almost 41% of household heads were affected by disability. The majority (73%) of families went to the public sector for health care services. The mean age was 45 years (SD ± 15). We find that disability is significantly associated with the household financial risk (p < 0.01). The private sector's choice for health care services is likely to positively affect the financial risk compared to the public sector (p < 0.01). The wealthier the household was, the more money paid for health service was (p < 0.01).Our results indicated that disability and household socio-demographic characteristics were associated with health financial risk in Uganda. Identifying families with disability and experiencing difficult living conditions constitute an entry point for health authorities to enhance health coverage progress in low and middle-income countries.

Moderating the role of the perceived security and endorsement on the relationship between per-ceived risk and intention to use the artificial intelligence in financial services

Abstract: Advancement of banking and financial investment has led to the rapid expansion of services automation. The consistent increase of Artificial Intelligence (AI) usage in investment management implies the impending popularity of technology-based service. This study examined influencer endorsement and perceived security benefits as moderators to the relationship between perceived risk and financial AI services. Questionnaires were disseminated to 300 respondents who were customers with experience of using financial AI services in Jordan, and they were chosen through purposive sampling method. Structural equation modeling run using Smart-partial least squares (PLS 3.3.6) was employed in analyzing the data obtained from 220 completed questionnaires. The results show that perceived risk negatively affects financial AI services, while influencer endorsement and perceived security moderate the relationship between perceived risk and financial AI services. This study provides insight to companies on how to reduce perceived risk to encourage people to use business intelligence applications, as in the use of financial technology services.

Financial risk allocation and provider incentives in hospital–insurer contracts in The Netherlands

Abstract: Abstract In healthcare systems with a purchaser–provider split, contracts are an important tool to define the conditions for the provision of healthcare services. Financial risk allocation can be used in contracts as a mechanism to influence provider behavior and stimulate providers to provide efficient and high-quality care. In this paper, we provide new insights into financial risk allocation between insurers and hospitals in a changing contracting environment. We used unique nationwide data from 901 hospital–insurer contracts in The Netherlands over the years 2013, 2016, and 2018. Based on descriptive and regression analyses, we find that hospitals were exposed to more financial risk over time, although this increase was somewhat counteracted by an increasing use of risk-mitigating measures between 2016 and 2018. It is likely that this trend was heavily influenced by national cost control agreements. In addition, alternative payment models to incentivize value-based health care were rarely used and thus seemingly of lower priority, despite national policies being explicitly directed at this goal. Finally, our analysis shows that hospital and insurer market power were both negatively associated with financial risk for hospitals. This effect becomes stronger if both hospital and insurer have strong market power, which in this case may indicate a greater need to reduce (financial) uncertainties and to create more cooperative relationships.

De-Risking the Energy Efficient Renovation of Commercial Office Buildings through Technical-Financial Risk Assessment

Abstract: Energy efficiency in the building sector plays a key role in supporting European and global commitments against the current climate crisis. A massive adoption of deep renovation measures would allow a global reduction of energy need up to 36%, based on estimations. However, the market for building renovation is still limited, due to uncertainties associated with risk evaluation. This paper aims to suggest a method to evaluate the financial impacts of technical risks related to energy efficiency investments. Key performance indicators (KPIs) necessary to evaluate the investment risk associated with energy renovation have been defined based on an analysis of the correlation between technical and financial risks, and their originating factors or root causes. The evaluation has been carried out thanks to the EEnvest tool: a web-based search and match platform, developed within the EEnvest collaborative research project funded by the European Commission (EC). This evaluation methodology has then been applied to a case study, an office building located in Rome, for whom an energy efficient renovation project was already in place to reduce energy needs. The investment risk of the renovation project is calculated for two different scenarios: with and without risk mitigation measures being applied during the design, installation and operation phases. The results show the different technical and financial risk trends of these two scenarios, highlighting the benefits obtained by the implementation of mitigation measures.

Application of Artificial Intelligence in Financial Risk Management

Abstract: With the progress of the times, artificial intelligence (AI) technology is becoming more and more mature. Being widely used in China, AI plays an indispensable role in various fields. However, to further intelligent financial risk management, consistent innovation needs to be made in financial risk management system. A fire-new intelligent financial prevention and control system cannot be built without making the best of AI to strengthen the control and management of financial risk, which is not only a major topic but an inevitable trend of innovation in financial risk management. From the perspective of financial risk management, combined with relevant data research, this paper discusses the application status and potential risks of AI in financial risk management, puts forward corresponding countermeasures and suggestions, and finally analyzes the application requirements of AI in financial risk in the future, striving to improve China’s financial risk management system by offering some meaningful reference.

Analysis and Risk Assessment of Corporate Financial Leverage Using Mobile Payment in the Era of Digital Technology in a Complex Environment

Abstract: The study aims to improve the enterprise’s ability to respond to financial crises and find some countermeasures to prevent potential financial risks. The enterprise financial risk is assessed, and the automatic summary function of mobile payment platforms based on long short-term memory (LSTM) is performed to extract the structured data and unstructured texts from its annual report. On this basis, the early warning system model of financial risks is implemented and its accuracy is improved. The structured data and unstructured text in the company’s annual report are extracted. The enterprise financial risk early warning system model is constructed. The accuracy of the enterprise financial risk early warning system has been improved. Firstly, we use the convolutional neural network (CNN) to establish a financial risk prediction system using financial data and test various indicators of the system. Secondly, the financial annual report of the listed company is obtained from the Internet. The required financial statements are obtained in two ways. The first is to set high special treatment (ST) sample weights and delete some non-ST samples. The second is to delete punctuation marks, interjections, numbers, and so on and process the collected text data. The financial risk prediction model is established using the financial text, and the LSTM + attention mechanism is used to optimize the model. Finally, combining structured financial data and unstructured financial text to establish a forecasting model, the model uses LSTM. Combined with a single-layer neural network or CNN model, the comparison experiment is carried out in two ways. Experiments show that the CNN or LSTM attention mechanism cannot significantly improve the performance of the system only using financial data or texts. Using the financial data and financial text using the LSTM + CNN model, the F1 value reached 85.29%. Financial data and other indicators in the text have also been greatly improved, and the overall performance is the best. In summary, LSTM using financial data and financial texts combined with CNN to establish a risk prediction system can help investors and companies themselves find possible financial crises in listed companies as soon as possible and help companies deal with their financial risks in a timely manner.

Sustainable Metrics in Project Financial Risk Management

Abstract: The objective of this study is to propose an approach that would increase the efficiency and efficacy of project financial risk management. The starting point of this research is an original detailed list of project financial risk categories, as it was observed that financial risk is described in the literature far too generally. Following a survey of project managers, it is shown that all the identified project financial risk categories are significant and early warning signals may play an important role in their prevention or mitigation. Additionally, the main causes for project financial risks are identified and their importance assessed. Following a literature review on metrics-based and financial risk management in projects, as well as an analysis of the causes assessed in the survey, it is hypothesised that sustainability principles, combined with metrics-based management, may increase the efficiency and efficacy of project financial risk management. A corresponding method is proposed, which should be embedded into the traditional process of project financial risk management. This method consists of generating metrics with a warning and preventive potential for each combination of three elements (financial risk category, risk cause, sustainability principle). This approach introduces into project financial risk management elements going beyond the financial optics, which may considerably increase its potential.

Research on Technological Innovation Investment, Financing Constraints, and Corporate Financial Risk: Evidence from China

Abstract: Technological innovation is the source of generating new momentum. In the context of the financing constraints generally existing in China, it is particularly important to explore the impact of technological innovation investment on corporate financial risk, which also provides a risk identification perspective and development direction for enterprises. Based on the data of China’s small and medium-sized listed companies from 2010 to 2019, and from the perspective of moderating effect of financing constraints, this paper uses multiple regression analysis method to test the impact mechanism of technological innovation investment on corporate financial risk and the moderating effect of financing constraints in both. The results show that technological innovation investment can significantly reduce corporate financial risk, while financing constraints can significantly improve the level of financial risk. Financing constraints play a moderating role in technological innovation investment and financial risk. Heterogeneity test found that, compared with large-scale enterprises, small-scale enterprises’ technological innovation investment has a more significant impact on financial risk, and the moderating effect of financing constraints is also greater. Compared with private enterprises, state-owned enterprises’ technological innovation investment has less impact on financial risk, and the moderating effect of financing constraints is not significant.

A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning

Abstract: At present, there are widespread financing difficulties in China's trade circulation industry. Supply chain finance can provide financing for small- and medium-sized enterprises in China's trade circulation industry, but it will produce financing risks such as credit risks. It is necessary to analyze the causes of the risks in the supply chain finance of the trade circulation industry and measure these risks by establishing a credit risk assessment system. In this article, a supply chain financial risk early warning index system is established, including 4 first-level indicators and 29 third-level indicators. Then, on the basis of the supply chain financial risk early warning index system, combined with the method of convolution neural network, the supply chain financial risk early warning model of trade circulation industry is constructed, and the evaluation index is measured by the method of principal component analysis. Finally, the relevant data of trade circulation enterprises are selected to make an empirical analysis of the model. The conclusion shows that the supply chain financial risk early warning model and risk control measures established in this article have certain reference value for the commercial circulation industry to carry out supply chain finance. It also provides guidance for trade circulation enterprises to deal with supply chain financial risks effectively.

The impact of the COVID-19 pandemic on the global dynamic spillover of financial market risk

Abstract: The COVID-19 outbreak has greatly impacted the stability of the global financial markets. In the post-COVID-19 pandemic era, the risk contagion patterns of the global financial markets may change. This paper utilizes the conditional value-at-risk (ΔCoVaR) model to measure the risk level of the financial markets in various economies and uses the TVP-VAR-CONNECTEDNESS approach to construct a time-varying spillover index. Based on the dimensions of time and space, we explored the contagion path, contagion status, and contagion structure characteristics of global financial market risk before and during the COVID-19 pandemic. The results entail several conclusions. (i) The COVID-19 pandemic increased the spillover level of global financial market risk and the risk connectedness of financial markets in different countries. In addition, during the concentrated outbreak period of COVID-19, the risk spillover level in developing countries rose rapidly, while the financial risk spillover level in developed countries decreased significantly. (ii) The impact of the COVID-19 pandemic on the spillover of the global financial market risk is time-varying, and there is a strong correlation between the risk spillover level of the financial markets of the world and the severity of the COVID-19 pandemic. (iii) Due to the impact of the COVID-19 pandemic, Brazil, Canada, and Russia have become new risk spillover centers; in the post-COVID-19 pandemic era, China's spillover to developed countries has increased, and the financial influence of China has also gradually increased. In addition, the risk contagion capacity of financial markets among European countries is gradually converging. (iv) During the concentrated outbreak of the COVID-19 pandemic, the Americas were the main exporter of global financial market risk, while Europe played a role in risk absorption.

Two-Way Risk Spillover of Financial and Real Sectors in the Presence of Major Public Emergencies

Abstract: In order to study the two-way risk spillovers between financial and real industries under major public emergencies in the Chinese market from 2007 to 2020, the sample period of major emergencies was determined based on the value at risk (VaR) time series, and it was found that the impact of major emergencies would lead to the rise of systemic risks in the financial industry. Secondly, the real sectors are taken as the main research object to measure the value of systemic risk spillover by using DCC-GARCH, and it shows that the industry with significantly systemic vulnerability from the overall financial risk spillover is the real estate industry, material industry, and energy industry. The results of subdividing financial sectors show that the banking sector has the most significant contribution to financial risk spillover in the real sectors. At the same time, identify the systemically important industries with high spillover risk to the financial industry, namely, utilities, consumer discretionary and industrials. Among the financial sub-industries, the risk spillover to the securities industry from the real sectors is the most significant. Finally, it was found that the system vulnerability and importance characteristics of the real entity industry depend on the nature of events and have certain rules.