In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.
By using the input-output model and the data of China’s industrial segment industries, this paper measure the wastewater emission reduction intensity and the wastewater emission reduction of China’s industrial imports in 2012 and 2015. At the same time, it also uses the LMDI to analyze the impact of the imports changes in scale, the structural and the intensity of the wastewater emission reduction by the imports on China’s industrial wastewater emission. The results are as follows:(1) In 2012 and 2015, China’s industrial product import caused its industrial wastewater discharge to decrease by 15% and 14% respectively; (2) The decreases in the scale of the imports and the intensity of wastewater emission reduction lowered the reduction of the emission but the change in the structure of the imports improved the capacity of the reduction.
With the development of science and technology, the application of neural network model is more and more extensive. In order to avoid the outbreak of financial crisis, economists and policy makers around the world pay special attention to the timely warning of financial crisis. If we can make an accurate early warning of the economic crisis, the government and regulatory agencies will have more time to deal with the possible risks and avoid major interference to economic growth. Based on this, this paper studies the spatial aggregation analysis and systemic risk prevention of Internet Finance Based on neural network model. Based on BP neural network, this paper constructs a spatial econometric model by using spatial econometric analysis method, and analyzes the empirical results of financial spatial aggregation. The results show that the Moran’s I value of China’s Internet finance index in December 2019 is 0.372, P value is 0.001, Moran’s I value of Internet payment is 0.395, P value is 0.002, Moran’s I value of Internet monetary fund is 0.357, P value is 0.001, Moran’s I value of Internet investment and financing is 0.324, P value is 0.002, Moran’s I of Internet insurance is 0.313, P value is 0.001. The results show that there is spatial agglomeration in the development of Internet finance in China. In addition, this paper also studies the systemic risk of Internet finance, analyzes its causes and puts forward corresponding preventive measures.
Interbank offer rate is the interest rate at which banks lend money to each other in the money market. As a market-oriented core interest rate, Shibor can accurately and timely reflect the capital supply and demand relationship in the money market, and its changes will quickly transmit and affect China’s financial market. Therefore, the purpose of this paper is to predict and study the fluctuation and trend of Shibor. In this paper, the overnight varieties of Shibor were studied and predicted from two time dimensions, namely, daily fluctuation and monthly trend. In the prediction of overnight Shibor daily data, a comparison prediction model based on BP neural network algorithm was first established, and then WNN was applied in the prediction, and the effect was found to be better. When predicting the monthly mean value of overnight Shibor, nine indicators were selected and tested for correlation based on the factors affecting the trend of interest rate, and a regression model of support vector machine was established. Particle swarm optimization algorithm was used to improve the SVR algorithm, and the PSO-SVR prediction model was established to improve the prediction accuracy. The model could basically predict the trend of overnight Shibor. Furthermore, a prediction model of WNN based on cuckoo search (CS) optimization was proposed, which improved the prediction accuracy by 78% and fitted the daily fluctuation of overnight Shibor well.
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