Data Analysis And Financial Risk Forecast Of Chinese Listed Banks' Stock Returns

Banks play an important role in financial institutions.As the main business object of financial market,it is important and meaningful to analyze the stability of banks and explore the relationship between banks.At present,in the era of data,mining the relationship between data sets is one of the significance of data analysis,and also one of the important ways to make intangible data assets become priceless assets.It is of great significance to apply the idea and method of data analysis to the research and construction of inter-bank relationship and the prediction of inter-bank risk communication.In this paper,we hope that through the acquisition of open data,we can build the network relationship between banks,and simulate the risk propagation in the network relationship,so as to get the different status of China's bank relationship and risk propagation.Therefore,16 Chinese listed banks are selected as the main research objects.Through the acquisition of the daily operation data published from 2015 to 2018,the closing price is selected as the original data of the study,and the logarithmic rate of return is obtained through the sorting and calculation of the closing price,and the logarithmic rate of return is taken as the research data of this paper.After obtaining the research data set,a series of method calculations will be carried out.In the aspect of data selection,using daily closing price as the unit of data acquisition can reflect the operation of the bank more timely,and expand the amount of research data.As the data of listed banks are updated in real time,the authenticity of the data is also ensured.In the aspect of network construction,the physical distance of data,i.e.Euclidean distance,is used to express the similarity between data sets,which is more intuitive.In the aspect of research and implementation,firstly,Euclid method is used to calculate the similarity matrix of inter-bank volatility,and a global coupling network is constructed,then hierarchical clustering analysis is used to classify the data.Secondly,on the basis of the global coupling network,we use the kruskar minimum spanning tree algorithm to extract the minimum path connecting 16 banks in the network.In this paper,we define the minimum path as the minimumpath of risk propagation.On this basis,the degree distribution and clustering degree of the network are calculated.Finally,the SIS model is used to simulate the risk propagation in the constructed minimum path network.It is found that there are many central points in the banking network,and the weight of the network plays an important role in risk propagation.Therefore,the paper puts forward the suggestions of hierarchical management for the bank's work;the bank should pay attention to its position in the network and strengthen its risk prevention and control ability.