Systematic Risk Analysis of Semiconductor Global Market Based on Deep Feature Fusion K-Means Algorithm

Abstract

As the global semiconductor industry has entered a new round of rapid growth, it has also entered a golden cycle of economic growth. Semiconductor companies increase their intrinsic value through financing, industry mergers and acquisitions, and venture capital searches. At the same time, market investors pay more attention to the intrinsic value of companies when looking for good investment targets. Therefore, the systematic risk assessment of the global semiconductor market has become a common concern of market investors and corporate management. In this context, this paper found a method that can assess the systemic risk of the semiconductor global market, which is to use the K-means algorithm based on deep feature fusion. This paper analyzed the algorithm in depth, analyzed the quantum space of tensors, and used the definition of cluster fusion to obtain the relationship between the projection matrices U and V. Experiments were carried out on the improved algorithm, and market research was conducted on a multinational semiconductor company A, which mainly included the basic statistics of the rate of return and the ACF and PACF coefficients of the rate of return series. Finally, the stock risk comparison of company A and company B in the same period was carried out. The experimental results showed that comparing the three items of compound growth rate, coefficient of variation, and active rate coefficient, the highest compound growth rate was 0.41, which came from Category 2, the highest variation coefficient was 2.31, which came from Category 10, and the highest active rate coefficient was 1.78, which came from Category 9. The experimental content was completed well.