Random Forest Method Based Research on the Anxiety Differences of Different Investors Facing Financial Product Default

Investors may experience anxiety when facing financial product defaults. There are significant differences in the level of anxiety among different investors. This difference may be related to the Socioeconomic attributes of investors. We collected the Socioeconomic attributes of investors as independent variables based on an investment institution and then measured the anxiety level of investors as the dependent variable on an anxiety scale. Then, a classification model for anxiety level was constructed using machine learning methods. The test results showed that the Precision, Recall, and F1-score of the Decision tree model were 85.1, 84.1, and 84.6%, respectively. The Precision, Recall, and F1-score of the Random forest model were 88.0, 87.3, and 87.6%, respectively. The experimental results validated the hypothesis that there is indeed a correlation between the level of anxiety among investors when facing financial product defaults and their Socioeconomic attributes. The level of anxiety among investors when facing financial product defaults was classified and predicted based on their socioeconomic attributes. The classification and regression decision tree (CART) model output strong explicit rules, making it more suitable for clinical application.