Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network

Chen, Qian; Zhang, Wenyu; Lou, Yu

doi:10.1109/access.2020.3004284

Cited by 61 publications

(27 citation statements)

References 59 publications

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“…The convolutional layer is the core of CNN. The convolution operation is mainly used for feature extraction, and the convolutional layer is used for convolution operation [28]. The pooling layer is generally after the convolutional layer because after the convolution is done, the data dimension is still large when the convolution kernel is small.…”

Section: B Cnnmentioning

confidence: 99%

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Wang

et al. 2021

IEEE Access

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The Exchange rate affects the economic development of various countries. To grasp the information of the changeable exchange rate in time, it is necessary to predict the exchange rate price. This paper proposes the CNN-TLSTM model to predict the United States Dollar/Chinese yuan (USD/CNY) exchange rate closing price of the next trading day. The model consists of two parts, namely convolutional neural networks(CNN)and tanh long short-term memory (TLSTM). The function of CNN is to extract feature factors from the input data. TLSTM is used to receive the output data of CNN for prediction, and finally obtain the prediction result. TLSTM is a new model proposed in this paper to improve the internal structure of the long short-term memory (LSTM). Its advantage is to change the range of the output value of the input gate, retain more data features, and prevent the output value of the input gate in LSTM from being overfitting. This paper selects the exchange rate of USD/CNY data and some stock data for each trading day from January 2, 2006, to October 30, 2020, as the experimental data. To prove the effectiveness of the CNN-TLSTM prediction model, the model is compared with multilayer perceptron (MLP), CNN, recurrent neural network (RNN), LSTM, and CNN-LSTM models. mean absolute percentage error (MAPE), mean square error (MSE), and R-squared (R 2 ) are used for comparative analysis. The experimental results show that the CNN-TLSTM model has the best predictive effect on the USD/CNY exchange rate closing price of the next trading day.

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Section: B Cnnmentioning

confidence: 99%

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Wang

et al. 2021

IEEE Access

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“…BiLSTM contains a forward LSTM and a reverse LSTM, in which the forward LSTM takes advantage of past information and the reverse LSTM takes advantage of future information. en, many researchers applied BiLSTM to solve the problem of time series prediction [26][27][28]. Because BiLSTM network can use the information of both the past and the future, the final prediction is more accurate than unidirectional LSTM.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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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.

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“…Since the efficient market hypothesis is not proved, more elaborate techniques have been used trying to exploit the market inefficiencies. Among these techniques, in the literature can be found applications with linear models [16], support vector machines [17], genetic algorithms [18] or more frequently neural networks [19]- [21] and deep learning methods [22]- [24] (for a recent survey on this topic see [25] or [26] for a more general survey).…”

Section: Introductionmentioning

confidence: 99%