Abstract
Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.
Highlights
With the unprecedented development of the network, multimedia data such as text, image, video, financial data from mobile phones, social networking sites, news, and financial websites are growing at a rapid pace and affecting our real daily life
Inspired by the successful applications of deep learning and attention mechanism on stock data analysis [42,43,44], this paper proposes a time series prediction model named as CNN-Bidirectional Long Short-term Memory (BiLSTM)-Effective Channel Attention (ECA), which integers Convolutional Neural Networks (CNN) [10, 12] and BiLSTM to predict the closing price of stock data
Firstly, we can find that the prediction results of BiLSTM and Long Short-Term Memory (LSTM) methods are better than CNN model. It indicates that LSTM and BiLSTM take the trend of the stock price as time series in consideration, which is useful to improve the accuracy of the forecast
Summary
With the unprecedented development of the network, multimedia data such as text, image, video, financial data from mobile phones, social networking sites, news, and financial websites are growing at a rapid pace and affecting our real daily life. Investors can employ the financial data to predict the future price trend of financial assets to reduce the decision-making risk [3, 4]. Investors can hardly acquire the useful information of the budget allocation timely. In order to make right investment decisions for investors, some technical or quantitative methods are necessary and important to use to predict the fluctuation of asset prices [5]. Since these methods are based on the assumption of linear relationship of model structure, they can hardly capture the Scientific Programming nonlinear variation of the stock price [8, 9]. These approaches assume that the data have constant variance, while the financial time series have high-noisy, time-varying, dynamic properties, and so on [10]
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