Abstract: As an important part of the quantitative investment program,the quantitative multi-factor stock selection model is used to predict stock returns by modeling historical financial data.This model has introduced many machine learning methods including deep learning.For the first time,the application of PCANet in quantitative stock selection has been explored.By transforming factors from financial time series data to two-dimensional image data,the financial time series prediction problem is transformed into an image classification problem,which provides a new and more open perspective.The research object is the Shanghai and Shen zhen 300 stocks from January 1,2009 to June 6,2017,which will be used for PCANet training and prediction.In the two-year backtest results,it obtains a Sharpe ratio of 57.17%,an excess return of 16.84%,and a maximum drawdown of －18.14%.Compared with the CNN model and the linear regression model,a higher Alpha return and Sharpe ratio are obtained,and the maximum retracement is smaller than that of the linear regression model.This shows that using PCANet for multi-factor stock selection is a feasible method.The application of PCANet in the multi-factor stock selection model can not only maintain the feature extraction capability of the deep learning structure,but also can effectively extract the features of the factor compared to linear regression.It will be a new direction worth trying.

Key words: Alpha return, Factor picture, Multi factor stock selection, Principal component analysis net, Sharpe ratio