Stock Price Range Forecast via a Recurrent Neural Network Based on the Zero-Crossing Rate Approach

Abstract

By knowing the future price range, which is the difference between the closing price and the opening price, we can calculate the long or short positions in advance. This paper presents a Recurrent Neural Network (RNN) based approach to forecast the price range. Compared to other methods based on machine learning, our method puts greater focus on the characteristics of the stock data, such as the zero-crossing rate (ZCR), which represents the ratio where the sign of the data changes within a time interval. We propose a decision-making method based on an estimate of the ZCR to enhance the ability to predict the stock price range, and apply our method to the Standard & Poors 500 (S&P500) stock index. The results indicate that our method can achieve better outcomes than other methods.