Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

Xiaodong Zhang, Suhui Liu and Xin Zheng
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Xiaodong Zhang: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
Suhui Liu: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
Xin Zheng: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Mathematics, 2021, vol. 9, issue 8, 1-21

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.

Keywords: stock price movement prediction; feature interaction; temporal feature; deep factorization machine; attention mechanism; stock price co-movement (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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