The Application of Stock Index Price Prediction with Neural Network

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Abstract

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.

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