scholarly journals Early Warning of Financial Risk Based on K-Means Clustering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhangyao Zhu ◽  
Na Liu
Keyword(s):  
Data Processing ◽  
Early Warning ◽  
Financial Risk ◽  
Clustering Algorithm ◽  
Indicator System ◽  
Scientific Basis ◽  
Risk Prevention ◽  
Cluster Center ◽  
Risk Indicator ◽  
Study Results

The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.

scholarly journals Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-6
Author(s):  
Jingyi Liu ◽  
Jiaolong Li
Keyword(s):  
Deep Learning ◽  
Risk Analysis ◽  
Foreign Investment ◽  
Financial Distress ◽  
Textile Industry ◽  
Financial Risk ◽  
Short Term Memory ◽  
Indicator System ◽  
Risk Indicator ◽  
Huge Impact

With the decline of China’s economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China’s globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry’s foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.

Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory

2020 ◽  
pp. 1-11
Author(s):  
Qiaoying Ding
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Financial Risk ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Theory ◽  
Fuzzy Comprehensive Evaluation ◽  
Real Time System ◽  
Enterprise Risk ◽  
Early Warning Management

The financial market is changing rapidly. Since joining the WTO, our country’s financial companies have faced pressure from dual competition at domestic and abroad. The complex internal and external environment has forced financial enterprise managers to improve risk prevention awareness, early warning and monitoring, so as to responding to emergencies and challenges in the financial market. However, traditional forecasting and analysis methods have problems such as large workload, low efficiency, and low accuracy. Therefore, this article applies intelligent computing to the forecast of financial markets, using related concepts of fuzzy theory and Internet intelligent technology, and proposes to establish a model system for financial enterprise risk early warning management and intelligent real-time monitoring based on fuzzy theory. This article first collected a large amount of data through the literature investigation method, and made a systematic and complete introduction to the related theoretical concepts of fuzzy theory and financial risk early-warning management, has laid a sufficient theoretical foundation for the subsequent exploration of the application of fuzzy theory in financial enterprise risk early warning management and intelligent real-time systems; Then a fuzzy comprehensive evaluation method that combines the analytic hierarchy process and fuzzy evaluation method is proposed, taking a listed company mainly engaged in automobile sales in our province as a case, the company’s financial risk management and modeling experiment of the intelligent real-time system; Finally quoted specific cases again, used the fuzzy comprehensive evaluation method to carry out risk warning and evaluation on the PPP projects of private enterprises in our province, and concluded that the project risk score is between 20-60, which is meet the severe-medium range in the risk level. Research shows that the use of fuzzy theory and modern network technology can make more accurate warnings and assessments of potential and apparent risks of financial enterprises, greatly improving the safety of financial enterprise management and reducing the losses caused by various risks.

scholarly journals Evaluating risks-based communities of Mafia companies: a complex networks perspective

2021 ◽  
Author(s):  
Nicola Giuseppe Castellano ◽  
Roy Cerqueti ◽  
Bruno Maria Franceschetti
Keyword(s):  
Organized Crime ◽  
Financial Risk ◽  
Data Driven ◽  
Size Analysis ◽  
Risk Indicator ◽  
Financial Risks ◽  
Risk Profiles ◽  
Crime Organization ◽  
Similarities And Differences ◽  
Risk Ratios

AbstractThis paper presents a data-driven complex network approach, to show similarities and differences—in terms of financial risks—between the companies involved in organized crime businesses and those who are not. At this aim, we construct and explore two networks under the assumption that highly connected companies hold similar financial risk profiles of large entity. Companies risk profiles are captured by a statistically consistent overall risk indicator, which is obtained by suitably aggregating four financial risk ratios. The community structures of the networks are analyzed under a statistical perspective, by implementing a rank-size analysis and by investigating the features of their distributions through entropic comparisons. The theoretical model is empirically validated through a high quality dataset of Italian companies. Results highlights remarkable differences between the considered sets of companies, with a higher heterogeneity and a general higher risk profiles in companies traceable back to a crime organization environment.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

scholarly journals Risk assessment of ice dams for water diversion projects based on fuzzy fault trees

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mengkai Liu ◽  
Xiaoxia Dong ◽  
Hui Guo
Keyword(s):  
Risk Assessment ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Northern China ◽  
Indicator System ◽  
Water Diversion ◽  
Risk Indicators ◽  
Scoring Method ◽  
Study Results ◽  
Dam Risk

AbstractIce dams are among the important risks affecting the operational safety and water conveyance efficiency of water diversion projects in northern China. However, no evaluation indicator system for ice dam risk assessment of water diversion projects has been proposed. Therefore, in this paper, based on the formation mechanism of ice dams, the risk assessment indicator system and the possibility calculation model of ice dams were both proposed for water diversion projects based on the fuzzy fault tree analysis method. The ice dam risk fault tree constructed in this study mainly includes three aspects: ice production, ice transport, and ice submergence conditions. Eighteen basic risk indicators were identified, and 72 minimum cut sets were obtained by using the mountain climb method. Eight risk indicators were determined as the key risk indicators for ice dams, including meteorological conditions, narrowed cross section, sluice incident, erroneous scheduling judgment, ice cover influence, flat bed slope, control structures, and ice flow resistance of piers. Then, the canal from the Fenzhuanghe sluice to the Beijumahe sluice of the Middle Route of the South-to-North Water Diversion Project was taken as the research object. Combined with the expert scoring method, the ice dam risk probability of the canal was determined to be 0.2029 × 10−2, which was defined as a level III risk, which is an occasionally occurring risk. The study results can support ice dam risk prevention and canal system operation in winter for water diversion projects.

scholarly journals Distribution and density of Lutraria rhynchaena Jonas, 1844 relate to sediment while reproduction shows multiple peaks per year in Cat Ba-Ha Long Bay, Vietnam

2020 ◽  
Vol 15 (1) ◽  
pp. 721-734
Author(s):  
Do Manh Hao ◽  
Do Trung Sy ◽  
Dao Thi Anh Tuyet ◽  
Le Minh Hiep ◽  
Nguyen Tien Dat ◽  
...  
Keyword(s):  
Sustainable Management ◽  
Fishery Management ◽  
Scientific Basis ◽  
Sediment Type ◽  
Medium Sand ◽  
Management Measures ◽  
Sediment Types ◽  
Study Results ◽  
Long Bay ◽  
Benthic Substrate

AbstractLutraria rhynchaena Jonas, 1844 is of great commercial interest, but its reserves have dramatically declined over recent decades. Therefore, there is an urgent need of scientific basis to propose effective fishery management measures and improve artificial aquaculture of the clam. In this study, we investigated the distribution and density of L. rhynchaena, sediment characteristics, and established the clam’s reproductive cycle through monthly observations from August 2017 to July 2018. The study results showed that distribution and density of clams are related to sediment types, and the sediment type of medium sand is likely the best benthic substrate for the clams. The spawning of clams occurred throughout the year with three spawning peaks in January, April and September. For the sustainable management of the clam resource in Cat Ba-Ha Long Bay, the fishery authorities can issue a ban on harvest of the clam in spawning peak months in January, April and September.

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