Predicting Chinese stock market using XGBoost multi-objective optimization with optimal weighting.

ABSTRACT: The application of artificial intelligence (AI) technology in various fields has been a recent research hotspot. As a representative technology of AI, the specific application of machine learning models in the field of economics and finance undoubtedly holds significant research value. This article proposes Extreme Gradient Boosting Multi-Objective Optimization Model with Optimal Weights (OW-XGBoost) to comprehensively balance the returns and risks of investment portfolios. The model utilizes fusing label with optimal weights to achieve multi-objective tasks, effectively controlling the impact of various risk and return indicators on the model, thus improving the interpretability and generalization ability of the model. In the experiments, we tested the model using China A-share data from

SUBMITTER: Liu J 

PROVIDER: S-EPMC10936758 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature