Risk Management Ability Research Articles

Opinion of Women on Possession of Land and Risk Handling Capacity

Abstract: This study shows that and ownership affects women's risk management abilities. This study was conducted in the two upazilas (Sub-districts) namely Delduar, and Nagorpur of Tangail district in Bangladesh. In this study, data were collected during the period of February 2024 to March 2024 from the studies areas. We found a favorable correlation between women's land ownership and risk management. Larger landholding women were more confident and competent in managing financial, environmental, and social hazards. This higher risk handling capability is due to land assets' financial stability, the ability to invest in risk-reducing technology or practices, and community bargaining power. This study shows that property ownership gives women a greater buffer against economic uncertainty and allows them to take proactive risk mitigation strategies. Data also shows that landowner women are more likely to have diverse income sources, minimizing their market susceptibility. They also use support networks and community risk management efforts more. The study concludes that land ownership improves women's risk management. Women acquire economic stability and risk management leverage by obtaining land holdings. This shows that land access regulations might boost women's resilience and risk management. Future study should examine how land ownership affects risk management and how to give disadvantaged women land access.

The Impact of Risk Management on the Financial Performance of the General Insurance Companies in Bangladesh

Objective: Proper risk management abilities are the keys to financial success of any company. The purpose of this study is to explore the relationship between risk management activities and the financial performance of listed general insurance firms in Bangladesh. Methodology: To investigate the impact, secondary data have been collected from the annual reports of the 42 insurance firms listed in the DSE for a five-year period (2017–2021). ROA, the depended variable has been used as a proxy of the financial performance, while risk management performance of the firms was analyzed using credit risk, market risk, operational risk, liquidity risk, underwriting risk, solvency risk management capacity of the companies separately and aggregately. Findings: The regression result shows that solvency risk management has significant positive impact on financial performance while credit risk, operational risk and underwriting risk management activities also have positive impact on firm performance. In addition, while the risk management has been considered in a holistic approach considering all the risk management tools in aggregate, the regression analysis reveals that comprehensive risk management initiatives show significantly positive effect on firm performance of insurance firms. Practical Implications: This study underscores the need for effective risk management strategies within companies to boost their financial results. Originality: Very few papers in Bangladesh have explored this area in Bangladesh specially in insurance sector. Findings of this study in the context of Bangladesh contribute to the literature gap and help the management to take necessary initiatives to improve performance. Limitations: Selecting short time period for analysis and using ROA as the only dependent variable for performance measurement are the major limitations of this study.

An analysis of risk control in the financial sector using big data technology

The rapid development of information technology has ushered in a new era of big data in the financial industry, which has provided financial institutions with a plethora of novel tools for risk management, while also playing a pivotal role in the industry's risk control landscape. This paper analyzes the public data of existing financial institutions, combines relevant literature, and employs big data technology to assess and identify risks, which aims to explore how big data technology can improve the risk management ability of the financial industry. In addition, the paper examines the challenges and problems faced by big data technology in the application of financial risk control, including data privacy and security, technology costs, and the demand for specialized talents. The research demonstrates that big data technology improves the speed and accuracy of risk identification, but also points out the challenges of data privacy and security management. Future risk control models need to better integrate big data analytics with traditional risk management methods, thus promoting the further development and application of big data technology in the field of risk control.

XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring

Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples.

The Effectiveness of Cyber Threat Intelligence in Improving Security Operations

The purpose of this research was to comprehensively evaluate the effectiveness of Cyber Threat Intelligence (CTI) in enhancing security operations, while simultaneously identifying the various barriers to its adoption. Additionally, the study aimed to provide potential solutions to mitigate the identified barriers, to ensure successful adoption of CTI.A systematic review was undertaken to identify the main factors of enhanced security operations. Relevant questions and statements were then developed from these factors and a questionnaire was created using Google Forms. These questionnaires were then distributed via email to a sample size of 50 information technology professionals. These results were then analyzed using Microsoft Excel and SPSS. Overall, the research revealed that companies who used CTI reported significant gains in threat detection and response, risk management and threat-hunting abilities, which in turn lead to enhanced security operations. According to the research, several factors prevented organizations from adopting CTI. Among these were technological, regulatory, ignorance-related, and lack of executive support. Finally, to tackle these identified barriers the following were proposed adopting comprehensive awareness and education programs, the formation of an Executive CTI Steering Committees, structured CTI training and skills development programs, technology assessment and modernization initiative-based initiatives, proactive compliance, and legal strategies.

The Influence of Openness on the Risk Management of "X" University Students Who Use Artificial intelligence in Malang City

This research was conducted with the aim of exploring the level of openness and risk management abilities possessed by "X" University students in Malang City regarding the development of artificial intelligence (AI) technology in the millennial era. Using a quantitative approach and probability sampling method, 377 respondents became research subjects. The research results show that the majority of students show a sufficient level of openness (78.26%), although their risk management towards AI tends to be high (47.5%). Further analysis revealed that openness had a significant influence on students' risk management abilities (p < 0.05). In conclusion, these findings highlight the need for a balanced approach between openness to innovative technology such as AI and awareness of the importance of risk management in dealing with its impact, especially for academic communities such as students at "X" University in Malang City.

Take JD as an Example to Discuss the Impact of Digital Intelligence on Supply Chain Risk Management

The complex and changeable global economic situation forces supply chain enterprises to strengthen risk management to deal with all kinds of emergencies. In the existing research, the impact of supply chain risk is still lack of in-depth exploration. From the perspective of intelligent transformation of enterprises, this paper takes JD as an example to analyze the problems existing in the risk management of the three supply chains of commodities, logistics, and finance, and then puts forward the corresponding solutions to the different problems in the three supply chains. This paper analyzes that the transformation of enterprise number intelligence can improve the efficiency of supply chain coordination and improve its risk management ability by establishing a shared information platform, but the imperfection of technology and the immaturity of technology application will also bring more unprecedented risks to enterprises.

Analysis of College Students' Entrepreneurship Under E-Commerce Environment

According to the relevant survey, the success rate of college students' entrepreneurship is lower than 5% for a long time, which is mainly due to the low cognitive ability of college students' entrepreneurship risk and the lack of knowledge related to risk management. The research results show that college students are more worried about and attach importance to the direction of the project, the source of funds, the change of the market and the corresponding knowledge and skills, but pay less attention to social resources and social skills. On the whole, college students' risk management ability is medium, so at the end of this article, relevant suggestions are put forward for the improvement of college students' entrepreneurial risk management ability.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.

Representing and discovering heterogeneous interactions for financial risk assessment of SMEs

Small and medium-sized enterprises (SMEs) generally have weak risk management abilities, yet their financial risk assessment faces unique challenges due to limited information about those firms. The emergence of network data constructed from the interactions between the SMEs and other stakeholders createsan opportunity to enhance the assessment of SMEs’ financial risks. While existing studies have extracted features from thenetwork data to utilize structural information, they overlook the heterogeneous interactions among the focal firms and other entities in the network. This research aims to fill the above gaps by proposing a represent-then-discover framework based on graph neural networks. The framework comprises an entity-importance based graph contrastive model (EIGC) that represents firms as the embeddings of their heterogeneous interactions and a novel subgraph-distillation based frequent interaction mining model (SDIM) that discovers heterogeneous interactions that signal firms with or without financial risks. The firm embeddings are first trained following an unsupervised paradigm and thenused to build models for predicting financial risks. Our empirical evaluation results not only demonstrate the superior performance of EIGC over state-of-the-art methods but also discover unique interaction patterns that distinguish normal SMEs compared to financial risk SMEs. This study contributes an effective framework for assessing and explaining SMEs’ financial risk in the fintech era. The proposed EIGC provides a promising avenue for constructing predictive features by leveraging network data and graph neural networks.

Risk Management: Anticipating and Reacting in StarCraft

Managing risk with imperfect information is something humans do every day, but we have little insight into the abilities of AI agents to do so. We define two risk management strategies and perform an ability-based evaluation using StarCraft agents. Our evaluation shows that nearly all agents mitigate risks after observing them (react), and many prepare for such risks before their appearance (anticipate). For this evaluation, we apply traditional causal effect inference and causal random forest methods to explain agent behavior. The results highlight different risk management strategies among agents, others strategies that are common to agents, and overall encourage evaluating agent risk management abilities in other AI domains.

Risk Management of Small and Medium-sized Enterprises under the Epidemic

At the beginning of 2020, the sudden outbreak of covid-19 not only causes harm to the human body health, but also produces some influence on the normal operation of Chinese enterprises, especially affecting the financial rotation of small and medium enterprises. Small and medium-sized enterprises have made a great contribution to the economic development of our country. It is very necessary to manage and research its financial risk. Therefore, this paper analyzes the impact of the epidemic on small and medium-sized enterprises, as well as the background and risk factors of the epidemic, and finally puts forward the impact measures of the outbreak on the financial risk management of small and medium-sized enterprises, hoping to further improve the management system of small and medium-sized enterprises and help them improve their risk management ability.

Stock Selection and Market Timing Ability of Nepalese Mutual Funds

This study evaluates the performance of Nepalese mutual fund managers’ stock selection and market timing abilities. This study performed the investigation on the weekly data of 5 mutual funds from December 28, 2017 to March 2, 2023 spanning 270 weeks. To serve our objective, the stock selection skills of the fund managers were assessed using average return, Sharpe ratio, Treynor ratio, Information ratio and Jensen’s alpha. The study found strong evidence supporting risk management ability in all of the mutual fund schemes as demonstrated by beta coefficient below unity and significant stock selection ability in 2 out of the 5 mutual fund schemes. Furthermore, the positive values of alpha although not statistically significant indicates stock selection ability of the fund managers. On the other hand, Treynor and Mazuy’s Unconditional Market timing model was employed to test the market timing skill of fund managers. The test results did not indicate any significant positive market timing skills but one of the mutual fund schemes exhibited statistically significant negative market timing. Thus, the Nepalese fund managers aptly demonstrated stock selection ability but they did not demonstrate any positive market timing skills indicating that fund managers typically relied on stock selection to outperform the Benchmark Index rather than market timing skills. Int. J. Soc. Sc. Manage. Vol. 10, Issue-3: 47-54.

Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon

Ecological agriculture projects have achieved a growing development in the context of low carbon. However, because of the great difference in these issues from traditional types, there exist risks in progression quality and sustainability. To better identify the risk, this paper proposes a novel hybrid approach that integrates the analytic hierarchy process (AHP) with technique for order preference by similarity to an ideal solution (TOPSIS), as well as an improved support vector machine (SVM) based on the brainstorming algorithm (BSO). First, a risk evaluation index framework is developed and elaborated in terms of the natural environment, society, market economy, management, technology, and finance. Then the traditional assessment can be derived from AHP with TOPSIS. In addition, BSO is applied to improve SVM for rapid computation. Finally, a case study is implemented to verify the accuracy of the proposed technique. In this research, based on the low-carbon perspective, artificial intelligence algorithm and risk assessment are introduced into the field of ecological agriculture project management, which is conducive to the rapid and effective evaluation of ecological agriculture project risk. It can improve managers’ risk awareness and risk management ability, reduce investment blindness, and help ecological agriculture projects achieve healthy and sustainable development under the background of low carbon, thus contributing to the development of a low-carbon economy.

How does digital finance affect consumer online shopping: A comprehensive analysis based on econometric model

We construct a comprehensive analytical framework to dissect the relationship between digital finance (DF) and consumer online shopping (COS) and its transmission mechanism based on panel data of 31 Chinese provinces from 2013 to 2020, applying spatial econometrics and mediation effect model. The conclusions are as follows: DF has a positive spatial spillover effect on COS, which means that DF not only stimulates local COS, but also can stimulate COS in neighbouring regions. DF encourages consumers to spend online by increasing residents' income, improving payment convenience, easing credit constraints, and improving residents' risk management ability.

GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)

This study aimed to explore and compare the application of current state-of-the-art machine learning techniques, including bagging (Bag) and rotation forest (RF), to assess landslide susceptibility with the base classifier best-first decision tree (BFT). The proposed two novel ensemble frameworks, BagBFT and RFBFT, and the base model BFT, were used to model landslide susceptibility in Zhashui County (China), which suffers from landslides. Firstly, we identified 169 landslides through field surveys and image interpretation. Then, a landslide inventory map was built. These 169 historical landslides were randomly classified into two groups: 70% for training data and 30% for validation data. Then, 15 landslide conditioning factors were considered for mapping landslide susceptibility. The three ensemble outputs were estimated with a receiver operating characteristic (ROC) curve and statistical tests, as well as a new approach, the improved frequency ratio accuracy. The areas under the ROC curve (AUCs) for the training data (success rate) of the three algorithms were 0.722 for BFT, 0.869 for BagBFT, and 0.895 for RFBFT. The AUCs for the validating groups (prediction rates) were 0.718, 0.834, and 0.872, respectively. The frequency ratio accuracy of the three models was 0.76163 for the BFT model, 0.92220 for the BagBFT model, and 0.92224 for the RFBFT model. Both BagBFT and RFBFT ensembles can improve the accuracy of the BFT base model, and RFBFT was relatively better. Therefore, the RFBFT model is the most effective approach for the accurate modeling of landslide susceptibility mapping (LSM). All three models can improve the identification of landslide-prone areas, enhance risk management ability, and afford more detailed information for land-use planning and policy setting.

Research on Liquidity Risk Management of Commercial banks under the impact of Fed rate hike

Liquidity risk refers to the risk of insolvency, credit decline, market value decline and other consequences that commercial banks cannot obtain enough working capital when paying debts or maintaining business operations. In the information society, liquidity risk has become a huge obstacle to the development of commercial banks and even to their survival. And in the most aggressive Fed rate hike cycle since March 2022, liquidity risk management should be a top concern for regulators and commercial banks. This paper studies the course of interest rate hike of the Federal Reserve, its impact on commercial banks, the current situation and difficulties of liquidity risk management of American commercial banks, and puts forward countermeasures and suggestions accordingly: commercial banks should strengthen the liquidity risk management ability, adjust and optimize the deposit and loan structure, strengthen the liquidity risk management awareness of managers, and so on.

Research on the Rural Mutual Aid Elderly Care Model of “Time Bank”

This paper elaborated on the concept of “Time Bank” and mutual support pension, introduced its development at home and abroad, and analyzed the possibility and necessity of implementing this model in rural areas of China. During the operation of the “Time Bank” mutual support pension model, there were problems such as a lack of publicity and participation, incomplete legal system, low risk resistance, and limited sustainable development due to insufficient investment. It is suggested that the promotion of “Time Bank” mutual support pension in rural areas should focus on improving publicity channels, enhancing mutual aid awareness, improving the legal system to enhance risk management ability, and raising funds through multiple channels to ensure sustainable development by combining multiple models.

Trust Relationship with Suppliers, Collaborative Action, and Manufacturer Resilience in the COVID-19 Crisis.

Frequent emergencies highlight the importance of corporate resilience, and relationship governance behaviors have received much attention for improving firms' resilience. This study uses structural equation modeling to test hypotheses based on trust theory and synergy theory. It investigates the impact of the behavioral operations management approach of collaborative action on manufacturers' resilience in Chinese manufacturing firms during the COVID-19 pandemic. Environmental uncertainty is used as a moderating variable to explore its role between the trust relationship with suppliers and collaborative action, which are two types of relationship management behaviors. The results show that collaborative action positively affects each of the three stages of manufacturer resilience. Moreover, collaborative action mediates between the trust relationship with suppliers and the different dimensions of manufacturer resilience. Environmental uncertainty does not moderate the facilitative effect of the trust relationship with suppliers on collaborative action. This study examines the relational governance behavior of firms in the context of a particular Chinese culture to build a bridge between relational governance behavioral science and firm resilience management. This study tries to provide inspiration for managers to improve the risk management ability of manufacturing enterprises by using relationship management theory and behavioral science.

Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model

Supply Chain Finance (SCF) is a new type of financing business carried out by commercial banks on the basis of supply chain management, which effectively promotes the healthy development of the supply chain. As the most typical mode of SCF, accounts receivable financing mode can use the part of accounts receivable occupying working capital for financing, which is widely used. In order to effectively manage the credit risk in the Supply Chain Finance and maintain the healthy operation of the supply chain, this paper proposes a supply chain financial credit risk prediction model based on PCA-GA-SVM. First, principal component analysis (PCA) is used to reduce the dimension of the original index system, and then genetic algorithm (GA) is used to optimize the parameters of support vector machine (SVM). Finally, the principal components selected by PCA are input into the GA-SVM model for training, and the final prediction model is established. The running results show that the prediction performance of PCA-GA-SVM model is better than that of SVM and GA-SVM models. It has a good generalization ability, which can be used as a reference for commercial banks to improve the credit risk management ability of Supply Chain Finance and is conducive to the sustainable development of supply chain finance business.