Predicting stock high price using forecast error with recurrent neural network

Abstract

Abstract Stock price forecasting is an eye-catching research topic. In previous works, many researchers used a single method or combination of methods to make predictions. However, accurately predicting stock prices is very difficult. To improve the predicting precision, in this study, an innovative prediction approach was proposed by recurrent substitution of forecast error into the historical neural network model through three steps. According to the historical data, the initial predicted value of the next day is obtained through the neural network. Then, the prediction error of the next day is obtained through the neural network according to the historical prediction error. Finally, the initial predicted value and the prediction error are added to obtain the final predicted value of the next day. We use recurrent neural network prediction methods, such as Long Short-Term Memory Network Model and Gated Recurrent Unit, which are popular in the recent neural network study. In the simulations, the past stock prices of China from June 2010 to August 2017 are used as training data, and those from September 2017 to April 2018 are used as test data. The experimental findings demonstrate that the proposed method with forecast error gives a more accurate prediction result for the stock’s high price on the next day, which indicates that the performance of the proposed one is superior to that of the traditional models without forecast error.