Forecasting Fluctuations in the Financial Index Using a Recurrent Neural Network Based on Price Features

Abstract

Profits can be made from a trading strategy where long or short positions are placed in advance, based on the ability to forecast a future stock price or index, such as the closing or opening price. In addition to predicting stock index values, a prediction of the sign for the difference between closing and opening prices is important in order to earn a profit. This article presents an approach based on a Recurrent Neural Network (RNN) to forecast the opening price, the closing price, and the difference between them. Compared to previously reported approaches that were based on machine learning, the method proposed here emphasizes the pre-processing of the data, including the normalized first order difference method, as well as the focusing on the characteristics of the stock data, such as the zero-crossing rate (ZCR), which denotes the ratio of changes in the sign within a specific time interval. We propose a decision-making approach that is based on an estimate of both the ZCR and the cross-validation data so as to enhance the ability to forecast the difference between the opening and closing prices. We apply our technique to the S&P500 (Standard & Poor's 500) and the Dow Jones stock indices. As indicated by the results, our method can achieve a better performance compared to previous work.