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.
Highlights
The stock market is an essential component of the nation’s economy, where most of the capital is exchanged around the world
The Uncertainty-aware Attention (UA) model outperforms the other three models in all metrics, which indicates that the attention-based model could achieve better performance in multivariate financial time series prediction
Several deep learning models are used including Multilayer Perceptron (MLP) model, Long Short Term Memory (LSTM) model, Convolutional Neural Network (CNN) model and UA model to predict the one-day-ahead closing price of three stock indices traded in different financial markets
Summary
The stock market is an essential component of the nation’s economy, where most of the capital is exchanged around the world. The stock market’s performance has a significant influence on the national economy. It plays a crucial role in attracting and directing the distributed liquidity and savings into optimal paths. In this way, the scarce financial resources could be adequately allocated to the most profitable activities and projects [1]. One could take advantage of the financial market if they have proper models to predict the stock price and volatility which are affected by macroeconomic factors, and by hundreds of other factors
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