Analysis of Implicit Strategy Optimization and Economic Returns of Machine Learning Techniques in Financial Asset Management

Abstract

Abstract This paper formulates and adjusts investment portfolios based on machine learning techniques, adopts the B-L model after combining the GJR-GARCH-M model and the RBF model, combines the subjective view of the investor with the market equilibrium rate of return, and realizes the optimization of the implicit strategy in the management of the financial assets in the process, and finally analyzes the economic returns through the B-L model. The return of the asset portfolio under the improved B-L model is 0.37% higher than that of the market capitalization-weighted asset portfolio. The Improved B-L model’s asset allocation improves economic returns. In terms of cumulative return, the mean cumulative return of the improved B-L model is 50.7%, which is higher than the economic return of the regular B-L model and the equal-weight portfolio strategy.