Abstract
Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.
Highlights
E traditional analysis method is based on economics and finance, which mainly uses the fundamental analysis method and the technical analysis method
Combining the advantages of convolutional neural networks (CNN) that can extract effective features from the data, and long shortterm memory (LSTM) which can find the interdependence of data in time series data, and automatically detect the best mode suitable for relevant data, this method can effectively improve the accuracy of stock price forecasting. e CNN-LSTM model uses CNN to extract the features of the input time data and uses LSTM to predict the stock closing price on the day
CNN-LSTM has the highest degree of broken line fitting which almost coincides with each other, and multilayer perceptron (MLP) has the lowest degree of broken line fitting
Summary
The financial market is a noisy, nonparametric dynamic system, and there are two main kinds of forecasting methods for stock price: traditional analysis method and machine learning method [13]. e traditional econometric methods or equations with parameters are not suitable for analyzing complex, high-dimensional, and noisy financial series data. In 2014, Adhikari et al proposed a method combining random walk (RW) and artificial neural network (ANN) to predict four financial time series data, and the results showed that the forecasting accuracy had a certain improvement [17]. E results showed that the LSTM deep neural network has high forecasting accuracy and can effectively predict the time series of the stock market [21]. (1) By analyzing the correlation and time series of stock price data, a new deep learning method (CNNLSTM) is proposed to predict the stock price In this method, CNN is used to extract the time feature of data, and LSTM is used for data forecasting. (2) By comparing the evaluation indexes of CNN-LSTM with multilayer perceptron (MLP), CNN, RNN, LSTM, and CNN-RNN, it is proved that CNN-LSTM has high forecasting accuracy and is more suitable for stock price forecasting
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