Empirical Analysis of Multi-factor Stock Selection Model Based on Weight Assignment of Momentum and Discrete Degree

Abstract

Through the stock selection of a large number of literature, we found that factors contribute differently at different market styles or stages, and the factor weight is different. Therefore, in this paper, the factors were preliminarily screened by IC mean and IR equal weight scoring method and T test, and then the screened factors were tested by correlation test and sorting method to obtain the final factors. After five methods, such as factor equal weight, momentum and dispersion comprehensive average weight, were tested and compared, we found that the method of momentum and dispersion comprehensive average weight on the effect of the stock selection is more excellent than other methods, so we chose the momentum and discrete degree of comprehensive weighted average approach to dynamic weighting of each stock, the last stock through empowerment scoring method selection. In this way, the speed of market change and the impact of industry on factors are better considered, and the te-test effect is better.