Based on annual data for 31 emerging and developing economies in the period 2000–2020, this paper explores the impact of global economic policy uncertainty on episodes of extreme international capital inflows. Following previous researches, we identify episodes of surges and stops. The results show that the global economic policy uncertainty can significantly increase the probability of surges and decrease the probability of stops in emerging and developing economies. The heterogeneity tests show that these effects vary with economic growth, financial development, the degree of economic globalization and global liquidity. Above effects are significant in the groups with higher economic growth, higher financial development, higher economic globalization and higher global liquidity. Further analyses show that as global economic policy uncertainty rises, its impact on surges and stops gradually declines. In addition, global economic policy uncertainty has a significant negative effect on the surge in advanced economies, which could confirm the above conclusion to a certain extent.

In the era of the rise of big data, if insurance companies can effectively and reasonably use existing data to tap more potential information, it can not only improve work efficiency but also achieve precise marketing to customers, thereby saving costs. At the same time, China pays more and more attention to financial insurance, so it is of great significance to use customer information to explore the purchase behavior of financial insurance. This paper mainly analyzes the factors that affect the purchase of financial insurance from the aspects of individuals and families and provides references for insurance marketing. This paper selects the results of the China Comprehensive Social Survey in 2020 as the empirical sample data of this paper and sets whether to purchase financial insurance as the target variable. The characteristic variables were screened, and the 10 variables selected after screening were finally used to build the model. Next, the data are divided into training set and test set, and a decision tree learning model is established on the two data sets at the same time. The classification results of the model are evaluated according to the classification evaluation indicators. Finally, the decision tree analysis results provide suggestions and strategies for the construction of insurance marketing recommendation methods.

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