Abstract
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.
Highlights
K-means is the most widely used clustering method so far, whose main idea is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; and 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 [7]
K-means clustering algorithm is to select K data as the initial centroid of each category and divide them into K categories according to the principle of one category with the smallest distance, and the divided mean values are judged according to the square error criterion function for determining whether the division is converged: if convergence, the algorithm is over; otherwise, continue to redivide and update the value of each cluster center successively until the optimal clustering result is obtained [8]
Conclusions is paper proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk earlywarning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and carried out an empirical experiments and its result analysis
Summary
Financial risk is the possibility of potential losses in direct investment caused by company loans, fiscal finance, and other economic factors and corresponding consequences of the economic shocks. e outbreak of financial crises often leads to currency depreciation, exchange rate fluctuations, market downturns, economic recession, and sometimes even the decline of neighboring businesses, countries, and even the world economy because of the contagiousness of financial risk or crisis [1]. erefore, the fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk earlywarning mechanism [2]. e expansions of transaction scale promote the development of the market, increase competition, and encourage enterprises to innovate. e 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 [3]. e objects in the same cluster are similar to each other and different from objects in different clusters, which is an unsupervised learning process. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk earlywarning, 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 carried out an empirical experiments and its result analysis. E detailed chapters are arranged as follows: Section 2 introduces the related works of similarity measure and item clustering; Section 3 proposes a K-mean-clustering-algorithm-based financial risk indicator system, including indicator selection and data processing; Section 4 constructs a financial risk earlywarning model based on the K-means clustering algorithm; Section 4 carries out an empirical experiments and its result analysis; Section 6 is the conclusion On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk earlywarning, 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 carried out an empirical experiments and its result analysis. e study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm. e detailed chapters are arranged as follows: Section 2 introduces the related works of similarity measure and item clustering; Section 3 proposes a K-mean-clustering-algorithm-based financial risk indicator system, including indicator selection and data processing; Section 4 constructs a financial risk earlywarning model based on the K-means clustering algorithm; Section 4 carries out an empirical experiments and its result analysis; Section 6 is the conclusion
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