With the growth of Chinese economy and the improvement of its international status, Chinese enterprises foreign direct investment has ushered in new development opportunities. Although scale foreign investment continues to expand and has broad development prospects, it also faces huge risks that domestic investment has never had. China and Central Asia are highly complementary economically. Strengthening direct investment in the Central Asian countries can effectually drive China’s economic development. Based on the quantitative evaluation and analysis from the perspective of big data, this paper uses a variety of methods to evaluate and explore the risks of China’s direct investment in Central Asian countries, and gives some policy suggestions. The research shows that the motivation of Chinese foreign investment cooperation is different from the marginal industry transfer in the traditional international investment theory. The problem of country risks in Central Asia is prominent. Chinese government must rely on the build of the Belt and Road to strengthen intergovernmental communication and exchanges, and establish a good cooperation mechanism to deal with the increasingly prominent problem of country risks. Further we will improve trade facilitation and expand the trade openness of Central Asian countries to China. To avoid risks, Chinese companies should invest under the guidance of the government and establish a complete investment chain for large projects. At the same time, we also need to seize the opportunity of the development of digital economy, and gradually establish a scientific and efficient investment model to effectively avoid the risk of direct investment.
Many economic variables are interdependent, restrictive, and influential. Finding the law of change between economic variables and influencing factors and expressing this law in mathematical expressions will bring great convenience to forecasting. A statistical analysis method that uses mathematical equations to determine the quantitative relationship between two or more variables. This is more commonly used when estimating and predicting the value of the dependent variable. The article analyzes the data on the National Bureau of Statistics website and uses the method of multiple linear regression to fit the graphs of economic indicators. Finally, the forecast data is analyzed in detail. We evaluated the modeling method of the prediction model and the credibility of the prediction data from a practical level.
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