Sector rotation multi-factor stock selection strategy based on six boards

Abstract

This paper proposes an investment strategy based on a regression linear model to predict sector rotation and a multi-factor scoring model to select stocks to achieve excess returns. The strategy uses January 2006 December 2020 as the model construction period and January 2021 June 2021 as the model validation and backtesting period. Using Shenyin & wanguo’s industry classification as a standard, the cyclicality of industries is classified by β value and the existence of sector rotation is verified using the difference in rate of return between cyclical and non-cyclical industry. The factors associated with the annualized rate of return are selected and the fitted formula for the annualized rate of return is obtained by regressing linear equation and neural network multidimensional correlation to select the dominant board. Valid factors for each board were selected for fitting, and the factors were assigned weights for scoring, resulting in 32 stocks. The backtest results significantly outperformed the CSI 300 Index, and the model performance is stable and can achieve better returns.