An SVM improvement prediction in multifactor model for stocks selection

Abstract

In this paper, an entire multifactor model has constructed, based on financial indicators. We improve the prediction of the SVM classification in the multifactor model. The ranking method is used for data preprocessing, then SVM predicts the stock return classification. Finally, the distance from data to the hyperplane is used to improve the classification predict. With this strategy, in constituent stocks of CSI500, the portfolio gains 88.96% accumulated return from 2016Q4 to 2018Q1. Technical analysis moving average(MA) and channel breakout(CB) as trading time strategies can decrease fluctuation and drawdown. High frequent data are used to re-construct the MA strategy and get lower fluctuation. This model provides a new research perspective: SVM character is used for prediction improvement, technical analysis for strategy return.

Keywords

• multifactor model
• stock selection
• technical analysis