Abstract
In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.
Highlights
The trend of the stock market index refers to the upward or downward movements of price series in the future
We present a novel end-to-end hybrid neural network to learn multiple time scale features for predicting the trend of the stock market index
Differing from previous work, we propose a hybrid neural network that mainly focuses on multiple time scale features in financial time series for trend prediction
Summary
The trend of the stock market index refers to the upward or downward movements of price series in the future. Outputs of different layers of the CNN as features of varying time scales of the original price series. We present a novel end-to-end hybrid neural network to learn multiple time scale features for predicting the trend of the stock market index. These works prove that LSTMs can successfully extract time dependencies in the financial sequence These works do not consider the multiple time scale features in the price series. Differing from previous work, we propose a hybrid neural network that mainly focuses on multiple time scale features in financial time series for trend prediction. We innovatively use a CNN to extract features on multiple time scales, simplifying the model and facilitating better predictions. We use several LSTMs to learn time dependencies in feature sequences extracted by the CNN, and fully connected layers for higher-level feature abstraction
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