Visualization of nonlinear relationship in capital flows of Japanese mutual funds

Abstract

The purpose of this study is to visualize nonlinear relationships which are quite ambiguous in the conventional correlation diagram. We firstly applied the LightGBM as a machine learning model to increase the modeling ability, and secondly applied the SHAP analysis to evaluate the contribution of explanatory variables to the objective variable. Finally, we visualize the contribution to identify nonlinear relationships, which can be used for practical marketing problems. As an example, we demonstrated that our visualization can work well to express the nonlinear relationships hidden in capital flows of Japanese mutual funds, and can see investors psychology based on the behavioral economics from the given results.