Abstract:In recent years, deep learning methods have been widely used in the financial field, especially promoting the development of stock price prediction. Aiming at the problem of poor accuracy and robustness of univariate Long Short Term Memory (LSTM) network in general stock price prediction, this paper introduces the idea of multiple-factor model in quantitative stock option strategy in the field of economics into stock price prediction, calculates the multiple-factor of stock and takes it as the input feature for the prediction model. At the same time, in order to make the model accept the multiple-factor input, a multi-variable LSTM model for stock price prediction is produced on the basis of the univariate LSTM model. The experiment results show that, with the introduction of multiple-factor model, it not only improves the accuracy of stock price prediction, but also brings better model robustness to some extent.