Asset Allocation Strategy Based on Announcements and Machine Learning-- An approach in Chinese Market

Abstract

Asset allocation strategy is frequently discussed by investors, either based on fundamental or quantitative analysis. This article discusses Announcement based quantitative asset allocation strategy using machine learning models, based on 2018.Q1 to 2022.Q2 Chinese A share market. We initially select and adjust the pool of tickets through announcement signals, then use technical analysis methods with machine learning models to make return predictions. Finally, we construct portfolios using Mean-variance optimization on daily frequency. The result shows that the combination of fundamental analysis and machine learning models can generate satisfactory return. The best model can reach annualized return of 59.4% considering turnover fee, beating the market which has annualized return of 3%. The annual sharpe ratio with turnover fee of the best portfolio is 2.28, which is a satisfactory result for investors. Besides, through combining fundamental analysis with quantitative methods, the interpretability and stability of quantitative models are greatly enhanced, which provides a novel way in synthesizing two separate investment concepts. In sum, this paper can provide investors with a relatively novel investment strategy that based on the impact of announcement information on stock price and the combination of fundamental and technical analysis.