Research on investment strategy of E-fund Asian Selection QDII Fund

^* Corresponding author: qianli@sut.edu.cn

Abstract

The paper optimizes E-fund Asian Selection Hong Kong stock investment strategy using the XGBoost machine learning algorithm and multi-factor stock selection theory, determining stock holding based on Hong Kong stock correlation factors.First, the new investment strategy derived from the XGBoost algorithm has better performance in terms of profitability and risk control compared to the original investment strategy.Second, for Hong Kong stocks, technical and valuation factors are more important in multi-factor quantitative stock selection, which can be a reference for QDII fund managers as well as investors when selecting factors.Thirdly, the XGBoost algorithm classifier is also more capable of classifying Hong Kong stocks, which can improve the rationality and accuracy of investment decisions, thus helping investors to better allocate their investment in Hong Kong stocks and achieve excess returns. Studying strategies investment of the QDII funds can aid fund managers in adjusting their strategies, enhance fund performance.