Optimization of financial market risk prediction system based on computer data simulation and Markov chain Monte Carlo

Abstract

With the continuous development of the times, the business scope of the financial market is also expanding, and more and more unknown risks have emerged in the process of development, which has brought a certain impact to the financial market. The exchange rate fluctuation, interest rate and other factors related to the international financial market make financial market risk one of the important risks faced by securities companies. Therefore, securities companies need intuitive, accurate and effective quantitative management tools for financial market risk assessment to improve the overall risk management level of the company. Therefore, this paper constructs a financial market risk prediction system based on computer data simulation technology and Markov chain Monte Carlo algorithm. This system is mainly composed of three parts: financial risk management, data exchange management and background risk application services. The front end of the financial market risk prediction system is divided into risk monitoring, risk allocation, risk report, risk decision-making and system log modules according to functions. Data collection management functions mainly include data collection, data encryption, data conversion, data return and other functions. The measured results show that the system can predict the significant inflection points in most economic risks synchronously or in advance, and the forecast data show that the financial security index has an upward trend from bottom to top. In this paper, the Markov chain Monte Carlo algorithm is introduced into the financial market, through data simulation to predict the risks of companies with economic risks and make a small contribution to the market development.