A novel graph convolutional feature based convolutional neural network for stock trend prediction

Chen, Wei; Jiang, Manrui; Zhang, Weiguo; Chen, Zhen‐Song

doi:10.1016/j.ins.2020.12.068

Cited by 187 publications

(43 citation statements)

References 34 publications

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“…In order to verify the effectiveness of the proposed model, different models including CNN [10], LSTM [15], BiLSTM [25,26], CNN-LSTM [20], BiLSTM-ECA, CNN-LSTM-ECA, and CNN-BiLSTM [27] are compared on the three stock datasets collected from the Shanghai Composite Index, China Unicom, and CSI 300. e prediction results are shown in Figures 13-15. As shown in these figures, the blue curve is the predicted value of the closing price of the stock, and the red curve is the true value of the closing price of the stock.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…On one hand, since these methods are based on the assumption of linear relationship of model structure, they can hardly capture the nonlinear variation of the stock price [8,9]. On the other hand, 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].…”

Section: Introductionmentioning

confidence: 99%

“…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-BiLSTM-ECA, which integers Convolutional Neural Networks (CNN) [10,12] and BiLSTM to predict the closing price of stock data. First, our model adopts CNN to extract the deep features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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show abstract

“…In the financial sector, the IoT-based services [1] include banking, insurance, and investments. Forecasting or prediction [2] is the most interesting thing in the financial market, and many studies [3,4] deploy IoTbased services to achieve higher accuracy. Li et al [5] studied the development of IoTs and used deep learning to predict stock price trends.…”

Section: Introductionmentioning

confidence: 99%

A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

Sun

Srivastava

et al. 2021

Mobile Information Systems

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The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

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“…Long et al (2020) incorporated stock market information and public market information into a deep neural network to improve the prediction performance. Chen et al (2021) integrated graph convolutional features with convolutional neural networks to increase prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine for Stock Movement Direction Prediction with Sparsity for Feature Selection

Miao

Cai

et al. 2021

Preprint

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In the stock market, accurate prediction of stock price movement direction can effectively increase the profits for investors. However, the stock price is an extremely complex dynamic system with strong fluctuation, proper selection of technical indicators can potentially improve the accuracy of the direction prediction. We propose a novel sparse least squares support vector machine (LSSVM) by combining recursive feature elimination (RFE) and Relief via a weight parameter. Specially, the benefit if this hybrid is three fold: (1) accounting for any intrinsic correlations among the features, (2) more effective prediction due to the sparse framework capable of removing some “noise” features completely; and (3) simultaneously select technical indicators according to the feature ranking and accounts for possible interactions and possible non-linear effects among the features. Three stock datasets from the liquor and spirits concept are analyzed to demonstrate the superiority of our proposed new framework providing sparse solutions resulting in more accurate predictions and higher returns among all seven considered classifiers.

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A novel graph convolutional feature based convolutional neural network for stock trend prediction

Cited by 187 publications

References 34 publications

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

Support Vector Machine for Stock Movement Direction Prediction with Sparsity for Feature Selection

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