Abstract
The prediction of stock price movement is a popular area of research in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices. In this paper, we constructed a convolutional neural network model based on a deep factorization machine and attention mechanism (FA-CNN) to improve the prediction accuracy of stock price movement via enhanced feature learning. Unlike most previous studies, which focus only on the temporal features of financial time series data, our model also extracts intraday interactions among input features. Further, in data representation, we used the sub-industry index as supplementary information for the current state of the stock, since there exists stock price co-movement between individual stocks and their industry index. The experiments were carried on the individual stocks in three industries. The results showed that the additional inputs of (a) the intraday interactions among input features and (b) the sub-industry index information effectively improved the prediction accuracy. The highest prediction accuracy of the proposed FA-CNN model is 64.81%. It is 7.38% higher than that of traditional LSTM, and 3.71% higher than that of the model without sub-industry index as additional input features.
Highlights
The accurate prediction of stock price movement allows investors to make appropriate decisions and obtain excess returns
We propose a hybrid convolutional neural network based on a deep factorization machine and attention mechanism (FA-convolutional neural networks (CNNs)) and a new stock state representation method to improve the prediction accuracy of stock price movement
To verify the effectiveness of the DeepFM and ATTCNN modules we proposed in Section 3.2, we conducted experiments on the following models: (1) the long- and short-term memory (LSTM) + DeepFM control model, where we sought to verify whether adding the DeepFM module to extract intraday feature interactions can improve the prediction accuracy; (2) the attention-based convolutional neural network (ATT-CNN) control model, where we sought to verify whether a CNN
Summary
The accurate prediction of stock price movement allows investors to make appropriate decisions and obtain excess returns. This remains a popular issue in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices [1]. The inputs of a DL model include the opening price, closing price, highest price, lowest price, trading volume and technical indicators (e.g., MA, RSI, MACD etc.) of a stock during a trading day [3,4]. We refer to these inputs as the ‘stock state’ in this paper. The output of the model is the probability that the stock price will rise at the predicted time point
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
