Abstract
Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked. Artificial neural network is usually used to analyze and predict financial time series. Based on deep learning, six layer long short-term memory neural networks were constructed. Eight long short-term memory neural networks were combined with Bagging method in ensemble learning and predicting model of neural networks ensemble learning was used in Chinese Stock Market. The experiment tested Shanghai Composite Index, Shenzhen Composite Index, Shanghai Stock Exchange 50 Index, Shanghai-Shenzhen 300 Index, Medium and Small Plate Index and Gem Index during the period from January 4, 2012 to December 29, 2017. For long short-term memory neural network ensemble learning model, its accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which are better than those of multilayer long short-term memory neural network model and reflect a good prediction outcome.
Highlights
In recent years, the theory of stock market prediction has become more and more matured
From the random walk theory and the effective market hypothesis to the quantitative investment, the prediction of the time series and the financial trading are more closely linked with machine learning
This paper proposes a financial time series prediction model based on ensemble learning of long short-term memory neural network
Summary
The theory of stock market prediction has become more and more matured. From the random walk theory and the effective market hypothesis to the quantitative investment, the prediction of the time series and the financial trading are more closely linked with machine learning. This paper proposes a financial time series prediction model based on ensemble learning of long short-term memory neural network. Each index converts the data from two-dimensional data into threedimensional data and normalizes each of the threedimensional data. This neural network consists of 6 layers, including four LSTM layers, one connected layer and one activated layer. The multi-layer long short-term memory neural network is used as the base learner, and the data set is divided into several sub data sets to train those classifiers. The final classification result is formed by integrating the classification results of the base learners and it is the estimation of trading day
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